polish tensorflow buggy and fixed versions

benchmark/tensorflow_10/tensorflow_10_fixed.ipynb

@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "_cell_guid": "db36d43e-fbdc-4fc3-8e8a-3f4c7a788b5d",
     "_uuid": "0d477273-98dd-4a45-97d9-3450116cd27e",
@@ -154,7 +154,7 @@
        "4  0       0.0       0  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "_cell_guid": "e4f0ee12-42dd-4437-9445-2b9de59506f0",
     "_uuid": "04c89e61-f09d-4eb5-9433-22e688897120",
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "f3113856-1ca3-49d1-80cf-07c60da185d3",
     "_uuid": "03dd987b-d612-4b93-a793-07c4b036914f",
@@ -253,125 +253,7 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sentences</th>\n",
-       "      <th>ADR</th>\n",
-       "      <th>WD</th>\n",
-       "      <th>EF</th>\n",
-       "      <th>INF</th>\n",
-       "      <th>SSI</th>\n",
-       "      <th>DI</th>\n",
-       "      <th>Findings</th>\n",
-       "      <th>Others</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>extreme weight gain, short-term memory loss, h...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>I am detoxing from Lexapro now.</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>I slowly cut my dosage over several months and...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>I am now 10 days completely off and OMG is it ...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>I have flu-like symptoms, dizziness, major moo...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           sentences  ADR   WD   EF  INF  SSI  \\\n",
-       "0  extreme weight gain, short-term memory loss, h...  1.0  0.0  0.0  0.0  0.0   \n",
-       "1                    I am detoxing from Lexapro now.  0.0  0.0  0.0  0.0  0.0   \n",
-       "2  I slowly cut my dosage over several months and...  0.0  0.0  0.0  0.0  0.0   \n",
-       "3  I am now 10 days completely off and OMG is it ...  0.0  0.0  0.0  0.0  0.0   \n",
-       "4  I have flu-like symptoms, dizziness, major moo...  0.0  1.0  0.0  0.0  0.0   \n",
-       "\n",
-       "  DI  Findings  Others  \n",
-       "0  0       0.0       0  \n",
-       "1  0       0.0       0  \n",
-       "2  0       0.0       1  \n",
-       "3  0       0.0       1  \n",
-       "4  0       0.0       0  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head(5)"
    ]
@@ -388,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "_cell_guid": "a1f8d583-8ec8-416d-b08c-c2b8c24376df",
     "_uuid": "4a8bb12b-86fc-49b0-8390-2e6b44236dad",
@@ -411,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "_cell_guid": "36e6dc50-12d9-4405-8213-a96065630497",
     "_uuid": "a657ba5a-d433-49a8-b783-35e8070dec7c",
@@ -433,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "_cell_guid": "5aff1087-a1aa-4771-9b55-625ea9972830",
     "_uuid": "abf84e40-be86-4593-928d-cca60640dce9",
@@ -455,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "_cell_guid": "4c72764b-ed0b-4cb8-80b9-a61ac336468a",
     "_uuid": "89540180-d48c-4424-8ca4-ff56b7e90846",
@@ -487,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "_cell_guid": "f98e293c-33ed-4f62-a5b5-ad94c3002f4d",
     "_uuid": "5561ce5d-d8df-4649-b27e-9f7936995c82",
@@ -524,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "f3d157fe-bf0f-4dc3-8bc3-1ba2eae83952",
     "_uuid": "ad56dd21-3dc8-4448-a283-e3fbf29c3bd8",
@@ -539,15 +421,7 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': [101, 2023, 2003, 1037, 7099, 3319, 1997, 1037, 3185, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'label': 1, 'text': 'this is a sample review of a movie.'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(process_data({\n",
     "    'sentences': 'this is a sample review of a movie.',\n",
@@ -557,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "_cell_guid": "a18da14f-f5f6-4834-ac49-3f9dbd2ba752",
     "_uuid": "8e5c355d-f6cf-4430-8dd4-682ac7a3a4f3",
@@ -582,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-04T03:32:35.667297Z",
@@ -610,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "_cell_guid": "a53aa2b1-d4ec-497a-ba1c-143059051090",
     "_uuid": "3858c27a-ce1d-4195-9b16-d7e459a4aaf0",
@@ -640,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {
     "_cell_guid": "6c4dcd43-c77c-481b-bb2b-222b62949079",
     "_uuid": "7060e586-ac88-4bc5-93b1-ff10eb1f470b",
@@ -676,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 13,
    "metadata": {
     "_cell_guid": "8ccfbddf-53c0-40ef-ab0f-a16907019abb",
     "_uuid": "41674a1c-1391-455a-aa3d-d83590aff0ab",
@@ -714,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "45938b6b-455a-4cf4-9ab0-b4f4b76dc344",
     "_uuid": "fddc048a-36d4-4e8f-a482-68424b79b993",
@@ -736,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 14,
    "metadata": {
     "_cell_guid": "101a58f9-017b-45ca-8f4e-1437a1147ad5",
     "_uuid": "2d1d59d1-b5c0-428a-8b53-6453f68765e7",
@@ -756,7 +630,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias']\n",
+      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
       "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
       "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
       "All the weights of TFBertModel were initialized from the PyTorch model.\n",
@@ -785,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 15,
    "metadata": {
     "_cell_guid": "37cc08e3-f74c-4eec-838a-e6426e9f8967",
     "_uuid": "e0933395-1e3b-4a10-b92e-25b3e2847931",
@@ -806,18 +680,18 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 3s/step - accuracy: 1.0000 - loss: 0.1241\n",
+      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 3s/step - accuracy: 0.9873 - loss: 0.1911\n",
       "Epoch 2/2\n",
-      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 3s/step - accuracy: 1.0000 - loss: 0.0028\n"
+      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 3s/step - accuracy: 1.0000 - loss: 0.0042\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x7ff36010fc40>"
+       "<keras.src.callbacks.history.History at 0x73c93c1135e0>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }

benchmark/tensorflow_10/tensorflow_10_reproduced.ipynb

@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "_cell_guid": "db36d43e-fbdc-4fc3-8e8a-3f4c7a788b5d",
     "_uuid": "0d477273-98dd-4a45-97d9-3450116cd27e",
@@ -154,7 +154,7 @@
        "4  0       0.0       0  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "_cell_guid": "e4f0ee12-42dd-4437-9445-2b9de59506f0",
     "_uuid": "04c89e61-f09d-4eb5-9433-22e688897120",
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "f3113856-1ca3-49d1-80cf-07c60da185d3",
     "_uuid": "03dd987b-d612-4b93-a793-07c4b036914f",
@@ -253,125 +253,7 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sentences</th>\n",
-       "      <th>ADR</th>\n",
-       "      <th>WD</th>\n",
-       "      <th>EF</th>\n",
-       "      <th>INF</th>\n",
-       "      <th>SSI</th>\n",
-       "      <th>DI</th>\n",
-       "      <th>Findings</th>\n",
-       "      <th>Others</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>extreme weight gain, short-term memory loss, h...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>I am detoxing from Lexapro now.</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>I slowly cut my dosage over several months and...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>I am now 10 days completely off and OMG is it ...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>I have flu-like symptoms, dizziness, major moo...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           sentences  ADR   WD   EF  INF  SSI  \\\n",
-       "0  extreme weight gain, short-term memory loss, h...  1.0  0.0  0.0  0.0  0.0   \n",
-       "1                    I am detoxing from Lexapro now.  0.0  0.0  0.0  0.0  0.0   \n",
-       "2  I slowly cut my dosage over several months and...  0.0  0.0  0.0  0.0  0.0   \n",
-       "3  I am now 10 days completely off and OMG is it ...  0.0  0.0  0.0  0.0  0.0   \n",
-       "4  I have flu-like symptoms, dizziness, major moo...  0.0  1.0  0.0  0.0  0.0   \n",
-       "\n",
-       "  DI  Findings  Others  \n",
-       "0  0       0.0       0  \n",
-       "1  0       0.0       0  \n",
-       "2  0       0.0       1  \n",
-       "3  0       0.0       1  \n",
-       "4  0       0.0       0  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head(5)"
    ]
@@ -388,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "_cell_guid": "a1f8d583-8ec8-416d-b08c-c2b8c24376df",
     "_uuid": "4a8bb12b-86fc-49b0-8390-2e6b44236dad",
@@ -411,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "_cell_guid": "36e6dc50-12d9-4405-8213-a96065630497",
     "_uuid": "a657ba5a-d433-49a8-b783-35e8070dec7c",
@@ -433,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "_cell_guid": "5aff1087-a1aa-4771-9b55-625ea9972830",
     "_uuid": "abf84e40-be86-4593-928d-cca60640dce9",
@@ -455,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "_cell_guid": "4c72764b-ed0b-4cb8-80b9-a61ac336468a",
     "_uuid": "89540180-d48c-4424-8ca4-ff56b7e90846",
@@ -487,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "_cell_guid": "f98e293c-33ed-4f62-a5b5-ad94c3002f4d",
     "_uuid": "5561ce5d-d8df-4649-b27e-9f7936995c82",
@@ -524,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "f3d157fe-bf0f-4dc3-8bc3-1ba2eae83952",
     "_uuid": "ad56dd21-3dc8-4448-a283-e3fbf29c3bd8",
@@ -539,15 +421,7 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': [101, 2023, 2003, 1037, 7099, 3319, 1997, 1037, 3185, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'label': 1, 'text': 'this is a sample review of a movie.'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(process_data({\n",
     "    'sentences': 'this is a sample review of a movie.',\n",
@@ -557,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "_cell_guid": "a18da14f-f5f6-4834-ac49-3f9dbd2ba752",
     "_uuid": "8e5c355d-f6cf-4430-8dd4-682ac7a3a4f3",
@@ -582,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-04T03:32:35.667297Z",
@@ -610,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "_cell_guid": "a53aa2b1-d4ec-497a-ba1c-143059051090",
     "_uuid": "3858c27a-ce1d-4195-9b16-d7e459a4aaf0",
@@ -640,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 13,
    "metadata": {
     "_cell_guid": "6c4dcd43-c77c-481b-bb2b-222b62949079",
     "_uuid": "7060e586-ac88-4bc5-93b1-ff10eb1f470b",
@@ -676,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 14,
    "metadata": {
     "_cell_guid": "8ccfbddf-53c0-40ef-ab0f-a16907019abb",
     "_uuid": "41674a1c-1391-455a-aa3d-d83590aff0ab",
@@ -712,7 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "45938b6b-455a-4cf4-9ab0-b4f4b76dc344",
     "_uuid": "fddc048a-36d4-4e8f-a482-68424b79b993",
@@ -734,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 15,
    "metadata": {
     "_cell_guid": "101a58f9-017b-45ca-8f4e-1437a1147ad5",
     "_uuid": "2d1d59d1-b5c0-428a-8b53-6453f68765e7",
@@ -754,7 +628,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
+      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
       "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
       "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
       "All the weights of TFBertModel were initialized from the PyTorch model.\n",
@@ -771,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 16,
    "metadata": {
     "_cell_guid": "37cc08e3-f74c-4eec-838a-e6426e9f8967",
     "_uuid": "e0933395-1e3b-4a10-b92e-25b3e2847931",
@@ -795,24 +669,16 @@
      ]
     },
     {
-     "ename": "TypeError",
-     "evalue": "in user code:\n\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1284, in train_function  *\n        return step_function(self, iterator)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1268, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in run_step  **\n        outputs = model.train_step(data)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1050, in train_step\n        y_pred = self(x, training=True)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/tmp/__autograph_generated_file_7qcj_31.py\", line 10, in tf__call\n        outputs = ag__.converted_call(ag__.ld(self).model, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope)\n    File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n        retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n    File \"/tmp/__autograph_generated_filei01ktml5.py\", line 31, in tf__call\n        outputs = ag__.converted_call(ag__.ld(self).bert, (), dict(input_ids=ag__.ld(input_ids), attention_mask=ag__.ld(attention_mask), token_type_ids=ag__.ld(token_type_ids), position_ids=ag__.ld(position_ids), head_mask=ag__.ld(head_mask), inputs_embeds=ag__.ld(inputs_embeds), encoder_hidden_states=ag__.ld(encoder_hidden_states), encoder_attention_mask=ag__.ld(encoder_attention_mask), past_key_values=ag__.ld(past_key_values), use_cache=ag__.ld(use_cache), output_attentions=ag__.ld(output_attentions), output_hidden_states=ag__.ld(output_hidden_states), return_dict=ag__.ld(return_dict), training=ag__.ld(training)), fscope)\n    File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n        retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n    File \"/tmp/__autograph_generated_file8w9ibu71.py\", line 127, in tf__call\n        embedding_output = ag__.converted_call(ag__.ld(self).embeddings, (), dict(input_ids=ag__.ld(input_ids), position_ids=ag__.ld(position_ids), token_type_ids=ag__.ld(token_type_ids), inputs_embeds=ag__.ld(inputs_embeds), past_key_values_length=ag__.ld(past_key_values_length), training=ag__.ld(training)), fscope)\n    File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 46, in tf__call\n        ag__.if_stmt(ag__.ld(input_ids) is not None, if_body_1, else_body_1, get_state_1, set_state_1, ('inputs_embeds',), 1)\n    File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 40, in if_body_1\n        ag__.converted_call(ag__.ld(check_embeddings_within_bounds), (ag__.ld(input_ids), ag__.ld(self).config.vocab_size), None, fscope)\n    File \"/tmp/__autograph_generated_file29zlsorl.py\", line 17, in tf__check_embeddings_within_bounds\n        ag__.converted_call(ag__.ld(tf).debugging.assert_less, (ag__.ld(tensor), ag__.converted_call(ag__.ld(tf).cast, (ag__.ld(embed_dim),), dict(dtype=ag__.ld(tensor).dtype), fscope)), dict(message=f\"The maximum value of {ag__.ld(tensor_name)} ({ag__.converted_call(ag__.ld(tf).math.reduce_max, (ag__.ld(tensor),), None, fscope)}) must be smaller than the embedding layer's input dimension ({ag__.ld(embed_dim)}). The likely cause is some problem at tokenization time.\"), fscope)\n\n    TypeError: Exception encountered when calling layer 'hugging_face_layer_3' (type HuggingFaceLayer).\n    \n    in user code:\n    \n        File \"/tmp/ipykernel_32/1009480242.py\", line 14, in call  *\n            outputs = self.model(inputs, **kwargs)\n        File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler  **\n            raise e.with_traceback(filtered_tb) from None\n        File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n            retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n        File \"/tmp/__autograph_generated_filei01ktml5.py\", line 31, in tf__call\n            outputs = ag__.converted_call(ag__.ld(self).bert, (), dict(input_ids=ag__.ld(input_ids), attention_mask=ag__.ld(attention_mask), token_type_ids=ag__.ld(token_type_ids), position_ids=ag__.ld(position_ids), head_mask=ag__.ld(head_mask), inputs_embeds=ag__.ld(inputs_embeds), encoder_hidden_states=ag__.ld(encoder_hidden_states), encoder_attention_mask=ag__.ld(encoder_attention_mask), past_key_values=ag__.ld(past_key_values), use_cache=ag__.ld(use_cache), output_attentions=ag__.ld(output_attentions), output_hidden_states=ag__.ld(output_hidden_states), return_dict=ag__.ld(return_dict), training=ag__.ld(training)), fscope)\n        File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n            retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n        File \"/tmp/__autograph_generated_file8w9ibu71.py\", line 127, in tf__call\n            embedding_output = ag__.converted_call(ag__.ld(self).embeddings, (), dict(input_ids=ag__.ld(input_ids), position_ids=ag__.ld(position_ids), token_type_ids=ag__.ld(token_type_ids), inputs_embeds=ag__.ld(inputs_embeds), past_key_values_length=ag__.ld(past_key_values_length), training=ag__.ld(training)), fscope)\n        File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 46, in tf__call\n            ag__.if_stmt(ag__.ld(input_ids) is not None, if_body_1, else_body_1, get_state_1, set_state_1, ('inputs_embeds',), 1)\n        File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 40, in if_body_1\n            ag__.converted_call(ag__.ld(check_embeddings_within_bounds), (ag__.ld(input_ids), ag__.ld(self).config.vocab_size), None, fscope)\n        File \"/tmp/__autograph_generated_file29zlsorl.py\", line 17, in tf__check_embeddings_within_bounds\n            ag__.converted_call(ag__.ld(tf).debugging.assert_less, (ag__.ld(tensor), ag__.converted_call(ag__.ld(tf).cast, (ag__.ld(embed_dim),), dict(dtype=ag__.ld(tensor).dtype), fscope)), dict(message=f\"The maximum value of {ag__.ld(tensor_name)} ({ag__.converted_call(ag__.ld(tf).math.reduce_max, (ag__.ld(tensor),), None, fscope)}) must be smaller than the embedding layer's input dimension ({ag__.ld(embed_dim)}). The likely cause is some problem at tokenization time.\"), fscope)\n    \n        TypeError: Exception encountered when calling layer 'tf_bert_model_3' (type TFBertModel).\n        \n        in user code:\n        \n            File \"/opt/conda/lib/python3.10/site-packages/transformers/modeling_tf_utils.py\", line 1061, in run_call_with_unpacked_inputs  *\n                return func(self, **unpacked_inputs)\n            File \"/opt/conda/lib/python3.10/site-packages/transformers/models/bert/modeling_tf_bert.py\", line 1088, in call  *\n                outputs = self.bert(\n            File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler  **\n                raise e.with_traceback(filtered_tb) from None\n            File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n                retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n            File \"/tmp/__autograph_generated_file8w9ibu71.py\", line 127, in tf__call\n                embedding_output = ag__.converted_call(ag__.ld(self).embeddings, (), dict(input_ids=ag__.ld(input_ids), position_ids=ag__.ld(position_ids), token_type_ids=ag__.ld(token_type_ids), inputs_embeds=ag__.ld(inputs_embeds), past_key_values_length=ag__.ld(past_key_values_length), training=ag__.ld(training)), fscope)\n            File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 46, in tf__call\n                ag__.if_stmt(ag__.ld(input_ids) is not None, if_body_1, else_body_1, get_state_1, set_state_1, ('inputs_embeds',), 1)\n            File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 40, in if_body_1\n                ag__.converted_call(ag__.ld(check_embeddings_within_bounds), (ag__.ld(input_ids), ag__.ld(self).config.vocab_size), None, fscope)\n            File \"/tmp/__autograph_generated_file29zlsorl.py\", line 17, in tf__check_embeddings_within_bounds\n                ag__.converted_call(ag__.ld(tf).debugging.assert_less, (ag__.ld(tensor), ag__.converted_call(ag__.ld(tf).cast, (ag__.ld(embed_dim),), dict(dtype=ag__.ld(tensor).dtype), fscope)), dict(message=f\"The maximum value of {ag__.ld(tensor_name)} ({ag__.converted_call(ag__.ld(tf).math.reduce_max, (ag__.ld(tensor),), None, fscope)}) must be smaller than the embedding layer's input dimension ({ag__.ld(embed_dim)}). The likely cause is some problem at tokenization time.\"), fscope)\n        \n            TypeError: Exception encountered when calling layer 'bert' (type TFBertMainLayer).\n            \n            in user code:\n            \n                File \"/opt/conda/lib/python3.10/site-packages/transformers/modeling_tf_utils.py\", line 1061, in run_call_with_unpacked_inputs  *\n                    return func(self, **unpacked_inputs)\n                File \"/opt/conda/lib/python3.10/site-packages/transformers/models/bert/modeling_tf_bert.py\", line 780, in call  *\n                    embedding_output = self.embeddings(\n                File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler  **\n                    raise e.with_traceback(filtered_tb) from None\n                File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 46, in tf__call\n                    ag__.if_stmt(ag__.ld(input_ids) is not None, if_body_1, else_body_1, get_state_1, set_state_1, ('inputs_embeds',), 1)\n                File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 40, in if_body_1\n                    ag__.converted_call(ag__.ld(check_embeddings_within_bounds), (ag__.ld(input_ids), ag__.ld(self).config.vocab_size), None, fscope)\n                File \"/tmp/__autograph_generated_file29zlsorl.py\", line 17, in tf__check_embeddings_within_bounds\n                    ag__.converted_call(ag__.ld(tf).debugging.assert_less, (ag__.ld(tensor), ag__.converted_call(ag__.ld(tf).cast, (ag__.ld(embed_dim),), dict(dtype=ag__.ld(tensor).dtype), fscope)), dict(message=f\"The maximum value of {ag__.ld(tensor_name)} ({ag__.converted_call(ag__.ld(tf).math.reduce_max, (ag__.ld(tensor),), None, fscope)}) must be smaller than the embedding layer's input dimension ({ag__.ld(embed_dim)}). The likely cause is some problem at tokenization time.\"), fscope)\n            \n                TypeError: Exception encountered when calling layer 'embeddings' (type TFBertEmbeddings).\n                \n                in user code:\n                \n                    File \"/opt/conda/lib/python3.10/site-packages/transformers/models/bert/modeling_tf_bert.py\", line 202, in call  *\n                        check_embeddings_within_bounds(input_ids, self.config.vocab_size)\n                    File \"/opt/conda/lib/python3.10/site-packages/transformers/tf_utils.py\", line 161, in check_embeddings_within_bounds  *\n                        tf.debugging.assert_less(\n                \n                    TypeError: Value passed to parameter 'input' has DataType string not in list of allowed values: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, float16, uint32, uint64, qint8, quint8, qint32, qint16, quint16\n                \n                \n                Call arguments received by layer 'embeddings' (type TFBertEmbeddings):\n                  • input_ids=tf.Tensor(shape=(None, 1), dtype=string)\n                  • position_ids=None\n                  • token_type_ids=tf.Tensor(shape=(None, 1), dtype=int32)\n                  • inputs_embeds=None\n                  • past_key_values_length=0\n                  • training=True\n            \n            \n            Call arguments received by layer 'bert' (type TFBertMainLayer):\n              • input_ids=tf.Tensor(shape=(None, 1), dtype=string)\n              • attention_mask=None\n              • token_type_ids=None\n              • position_ids=None\n              • head_mask=None\n              • inputs_embeds=None\n              • encoder_hidden_states=None\n              • encoder_attention_mask=None\n              • past_key_values=None\n              • use_cache=True\n              • output_attentions=False\n              • output_hidden_states=False\n              • return_dict=True\n              • training=True\n        \n        \n        Call arguments received by layer 'tf_bert_model_3' (type TFBertModel):\n          • input_ids=tf.Tensor(shape=(None, 1), dtype=string)\n          • attention_mask=None\n          • token_type_ids=None\n          • position_ids=None\n          • head_mask=None\n          • inputs_embeds=None\n          • encoder_hidden_states=None\n          • encoder_attention_mask=None\n          • past_key_values=None\n          • use_cache=None\n          • output_attentions=None\n          • output_hidden_states=None\n          • return_dict=None\n          • training=True\n    \n    \n    Call arguments received by layer 'hugging_face_layer_3' (type HuggingFaceLayer):\n      • inputs=tf.Tensor(shape=(None, 1), dtype=string)\n      • kwargs={'training': 'True'}\n",
+     "ename": "ValueError",
+     "evalue": "Layers added to a Sequential model can only have a single positional argument, the input tensor. Layer HuggingFaceLayer has multiple positional arguments: [<Parameter \"inputs\">, <Parameter \"**kwargs\">]",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[55], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Compile and train the model\u001b[39;00m\n\u001b[1;32m      2\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m, metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_file0uma5t6e.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_file_7qcj_31.py:10\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__call\u001b[0;34m(self, inputs, **kwargs)\u001b[0m\n\u001b[1;32m      8\u001b[0m do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m      9\u001b[0m retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mUndefinedReturnValue()\n\u001b[0;32m---> 10\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverted_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfscope\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_file4cxfdhhw.py:37\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__run_call_with_unpacked_inputs\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     36\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(func), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m),), \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mag__\u001b[38;5;241m.\u001b[39mld(unpacked_inputs)), fscope)\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m     39\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_filei01ktml5.py:31\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__call\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, training)\u001b[0m\n\u001b[1;32m     29\u001b[0m do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m     30\u001b[0m retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mUndefinedReturnValue()\n\u001b[0;32m---> 31\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverted_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbert\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43muse_cache\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfscope\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     33\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_file4cxfdhhw.py:37\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__run_call_with_unpacked_inputs\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     36\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(func), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m),), \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mag__\u001b[38;5;241m.\u001b[39mld(unpacked_inputs)), fscope)\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m     39\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_file8w9ibu71.py:127\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__call\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, training)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    126\u001b[0m ag__\u001b[38;5;241m.\u001b[39mif_stmt(ag__\u001b[38;5;241m.\u001b[39mld(token_type_ids) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, if_body_6, else_body_6, get_state_6, set_state_6, (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken_type_ids\u001b[39m\u001b[38;5;124m'\u001b[39m,), \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 127\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverted_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpast_key_values_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpast_key_values_length\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfscope\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    128\u001b[0m attention_mask_shape \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(shape_list), (ag__\u001b[38;5;241m.\u001b[39mld(attention_mask),), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[1;32m    129\u001b[0m mask_seq_length \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mld(seq_length) \u001b[38;5;241m+\u001b[39m ag__\u001b[38;5;241m.\u001b[39mld(past_key_values_length)\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_filev0vda2ze.py:46\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__call\u001b[0;34m(self, input_ids, position_ids, token_type_ids, inputs_embeds, past_key_values_length, training)\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[38;5;28;01mnonlocal\u001b[39;00m inputs_embeds\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m \u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mif_stmt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mif_body_1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43melse_body_1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mget_state_1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mset_state_1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minputs_embeds\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     47\u001b[0m input_shape \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(shape_list), (ag__\u001b[38;5;241m.\u001b[39mld(inputs_embeds),), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_state_2\u001b[39m():\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_filev0vda2ze.py:40\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__call.<locals>.if_body_1\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mif_body_1\u001b[39m():\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;28;01mnonlocal\u001b[39;00m inputs_embeds\n\u001b[0;32m---> 40\u001b[0m     \u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverted_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheck_embeddings_within_bounds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvocab_size\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfscope\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     41\u001b[0m     inputs_embeds \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(tf)\u001b[38;5;241m.\u001b[39mgather, (), \u001b[38;5;28mdict\u001b[39m(params\u001b[38;5;241m=\u001b[39mag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mweight, indices\u001b[38;5;241m=\u001b[39mag__\u001b[38;5;241m.\u001b[39mld(input_ids)), fscope)\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_file29zlsorl.py:17\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__check_embeddings_within_bounds\u001b[0;34m(tensor, embed_dim, tensor_name)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;124;03m`tf.gather`, on which TF embedding layers are based, won't check positive out of bound indices on GPU, returning\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;124;03mzeros instead. This function adds a check against that dangerous silent behavior.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;124;03m    tensor_name (`str`, *optional*): The name of the tensor to use in the error message.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     16\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m ag__\u001b[38;5;241m.\u001b[39mFunctionScope(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcheck_embeddings_within_bounds\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfscope\u001b[39m\u001b[38;5;124m'\u001b[39m, ag__\u001b[38;5;241m.\u001b[39mSTD) \u001b[38;5;28;01mas\u001b[39;00m fscope:\n\u001b[0;32m---> 17\u001b[0m             ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(tf)\u001b[38;5;241m.\u001b[39mdebugging\u001b[38;5;241m.\u001b[39massert_less, (ag__\u001b[38;5;241m.\u001b[39mld(tensor), ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(tf)\u001b[38;5;241m.\u001b[39mcast, (ag__\u001b[38;5;241m.\u001b[39mld(embed_dim),), \u001b[38;5;28mdict\u001b[39m(dtype\u001b[38;5;241m=\u001b[39mag__\u001b[38;5;241m.\u001b[39mld(tensor)\u001b[38;5;241m.\u001b[39mdtype), fscope)), \u001b[38;5;28mdict\u001b[39m(message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe maximum value of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mag__\u001b[38;5;241m.\u001b[39mld(tensor_name)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverted_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtf\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce_max\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;250;43m \u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mag__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mld\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;250;43m \u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[38;5;250;43m \u001b[39;49m\u001b[43mfscope\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) must be smaller than the embedding layer\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms input dimension (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mag__\u001b[38;5;241m.\u001b[39mld(embed_dim)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m). The likely cause is some problem at tokenization time.\u001b[39m\u001b[38;5;124m\"\u001b[39m), fscope)\n",
-      "\u001b[0;31mTypeError\u001b[0m: in user code:\n\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1284, in train_function  *\n        return step_function(self, iterator)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1268, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in run_step  **\n        outputs = model.train_step(data)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1050, in train_step\n        y_pred = self(x, training=True)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/tmp/__autograph_generated_file_7qcj_31.py\", line 10, in tf__call\n        outputs = ag__.converted_call(ag__.ld(self).model, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope)\n    File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n        retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n    File \"/tmp/__autograph_generated_filei01ktml5.py\", line 31, in tf__call\n        outputs = ag__.converted_call(ag__.ld(self).bert, (), dict(input_ids=ag__.ld(input_ids), attention_mask=ag__.ld(attention_mask), token_type_ids=ag__.ld(token_type_ids), position_ids=ag__.ld(position_ids), head_mask=ag__.ld(head_mask), inputs_embeds=ag__.ld(inputs_embeds), encoder_hidden_states=ag__.ld(encoder_hidden_states), encoder_attention_mask=ag__.ld(encoder_attention_mask), past_key_values=ag__.ld(past_key_values), use_cache=ag__.ld(use_cache), output_attentions=ag__.ld(output_attentions), output_hidden_states=ag__.ld(output_hidden_states), return_dict=ag__.ld(return_dict), training=ag__.ld(training)), fscope)\n    File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n        retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n    File \"/tmp/__autograph_generated_file8w9ibu71.py\", line 127, in tf__call\n        embedding_output = ag__.converted_call(ag__.ld(self).embeddings, (), dict(input_ids=ag__.ld(input_ids), position_ids=ag__.ld(position_ids), token_type_ids=ag__.ld(token_type_ids), inputs_embeds=ag__.ld(inputs_embeds), past_key_values_length=ag__.ld(past_key_values_length), training=ag__.ld(training)), fscope)\n    File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 46, in tf__call\n        ag__.if_stmt(ag__.ld(input_ids) is not None, if_body_1, else_body_1, get_state_1, set_state_1, ('inputs_embeds',), 1)\n    File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 40, in if_body_1\n        ag__.converted_call(ag__.ld(check_embeddings_within_bounds), (ag__.ld(input_ids), ag__.ld(self).config.vocab_size), None, fscope)\n    File \"/tmp/__autograph_generated_file29zlsorl.py\", line 17, in tf__check_embeddings_within_bounds\n        ag__.converted_call(ag__.ld(tf).debugging.assert_less, (ag__.ld(tensor), ag__.converted_call(ag__.ld(tf).cast, (ag__.ld(embed_dim),), dict(dtype=ag__.ld(tensor).dtype), fscope)), dict(message=f\"The maximum value of {ag__.ld(tensor_name)} ({ag__.converted_call(ag__.ld(tf).math.reduce_max, (ag__.ld(tensor),), None, fscope)}) must be smaller than the embedding layer's input dimension ({ag__.ld(embed_dim)}). The likely cause is some problem at tokenization time.\"), fscope)\n\n    TypeError: Exception encountered when calling layer 'hugging_face_layer_3' (type HuggingFaceLayer).\n    \n    in user code:\n    \n        File \"/tmp/ipykernel_32/1009480242.py\", line 14, in call  *\n            outputs = self.model(inputs, **kwargs)\n        File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler  **\n            raise e.with_traceback(filtered_tb) from None\n        File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n            retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n        File \"/tmp/__autograph_generated_filei01ktml5.py\", line 31, in tf__call\n            outputs = ag__.converted_call(ag__.ld(self).bert, (), dict(input_ids=ag__.ld(input_ids), attention_mask=ag__.ld(attention_mask), token_type_ids=ag__.ld(token_type_ids), position_ids=ag__.ld(position_ids), head_mask=ag__.ld(head_mask), inputs_embeds=ag__.ld(inputs_embeds), encoder_hidden_states=ag__.ld(encoder_hidden_states), encoder_attention_mask=ag__.ld(encoder_attention_mask), past_key_values=ag__.ld(past_key_values), use_cache=ag__.ld(use_cache), output_attentions=ag__.ld(output_attentions), output_hidden_states=ag__.ld(output_hidden_states), return_dict=ag__.ld(return_dict), training=ag__.ld(training)), fscope)\n        File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n            retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n        File \"/tmp/__autograph_generated_file8w9ibu71.py\", line 127, in tf__call\n            embedding_output = ag__.converted_call(ag__.ld(self).embeddings, (), dict(input_ids=ag__.ld(input_ids), position_ids=ag__.ld(position_ids), token_type_ids=ag__.ld(token_type_ids), inputs_embeds=ag__.ld(inputs_embeds), past_key_values_length=ag__.ld(past_key_values_length), training=ag__.ld(training)), fscope)\n        File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 46, in tf__call\n            ag__.if_stmt(ag__.ld(input_ids) is not None, if_body_1, else_body_1, get_state_1, set_state_1, ('inputs_embeds',), 1)\n        File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 40, in if_body_1\n            ag__.converted_call(ag__.ld(check_embeddings_within_bounds), (ag__.ld(input_ids), ag__.ld(self).config.vocab_size), None, fscope)\n        File \"/tmp/__autograph_generated_file29zlsorl.py\", line 17, in tf__check_embeddings_within_bounds\n            ag__.converted_call(ag__.ld(tf).debugging.assert_less, (ag__.ld(tensor), ag__.converted_call(ag__.ld(tf).cast, (ag__.ld(embed_dim),), dict(dtype=ag__.ld(tensor).dtype), fscope)), dict(message=f\"The maximum value of {ag__.ld(tensor_name)} ({ag__.converted_call(ag__.ld(tf).math.reduce_max, (ag__.ld(tensor),), None, fscope)}) must be smaller than the embedding layer's input dimension ({ag__.ld(embed_dim)}). The likely cause is some problem at tokenization time.\"), fscope)\n    \n        TypeError: Exception encountered when calling layer 'tf_bert_model_3' (type TFBertModel).\n        \n        in user code:\n        \n            File \"/opt/conda/lib/python3.10/site-packages/transformers/modeling_tf_utils.py\", line 1061, in run_call_with_unpacked_inputs  *\n                return func(self, **unpacked_inputs)\n            File \"/opt/conda/lib/python3.10/site-packages/transformers/models/bert/modeling_tf_bert.py\", line 1088, in call  *\n                outputs = self.bert(\n            File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler  **\n                raise e.with_traceback(filtered_tb) from None\n            File \"/tmp/__autograph_generated_file4cxfdhhw.py\", line 37, in tf__run_call_with_unpacked_inputs\n                retval_ = ag__.converted_call(ag__.ld(func), (ag__.ld(self),), dict(**ag__.ld(unpacked_inputs)), fscope)\n            File \"/tmp/__autograph_generated_file8w9ibu71.py\", line 127, in tf__call\n                embedding_output = ag__.converted_call(ag__.ld(self).embeddings, (), dict(input_ids=ag__.ld(input_ids), position_ids=ag__.ld(position_ids), token_type_ids=ag__.ld(token_type_ids), inputs_embeds=ag__.ld(inputs_embeds), past_key_values_length=ag__.ld(past_key_values_length), training=ag__.ld(training)), fscope)\n            File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 46, in tf__call\n                ag__.if_stmt(ag__.ld(input_ids) is not None, if_body_1, else_body_1, get_state_1, set_state_1, ('inputs_embeds',), 1)\n            File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 40, in if_body_1\n                ag__.converted_call(ag__.ld(check_embeddings_within_bounds), (ag__.ld(input_ids), ag__.ld(self).config.vocab_size), None, fscope)\n            File \"/tmp/__autograph_generated_file29zlsorl.py\", line 17, in tf__check_embeddings_within_bounds\n                ag__.converted_call(ag__.ld(tf).debugging.assert_less, (ag__.ld(tensor), ag__.converted_call(ag__.ld(tf).cast, (ag__.ld(embed_dim),), dict(dtype=ag__.ld(tensor).dtype), fscope)), dict(message=f\"The maximum value of {ag__.ld(tensor_name)} ({ag__.converted_call(ag__.ld(tf).math.reduce_max, (ag__.ld(tensor),), None, fscope)}) must be smaller than the embedding layer's input dimension ({ag__.ld(embed_dim)}). The likely cause is some problem at tokenization time.\"), fscope)\n        \n            TypeError: Exception encountered when calling layer 'bert' (type TFBertMainLayer).\n            \n            in user code:\n            \n                File \"/opt/conda/lib/python3.10/site-packages/transformers/modeling_tf_utils.py\", line 1061, in run_call_with_unpacked_inputs  *\n                    return func(self, **unpacked_inputs)\n                File \"/opt/conda/lib/python3.10/site-packages/transformers/models/bert/modeling_tf_bert.py\", line 780, in call  *\n                    embedding_output = self.embeddings(\n                File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler  **\n                    raise e.with_traceback(filtered_tb) from None\n                File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 46, in tf__call\n                    ag__.if_stmt(ag__.ld(input_ids) is not None, if_body_1, else_body_1, get_state_1, set_state_1, ('inputs_embeds',), 1)\n                File \"/tmp/__autograph_generated_filev0vda2ze.py\", line 40, in if_body_1\n                    ag__.converted_call(ag__.ld(check_embeddings_within_bounds), (ag__.ld(input_ids), ag__.ld(self).config.vocab_size), None, fscope)\n                File \"/tmp/__autograph_generated_file29zlsorl.py\", line 17, in tf__check_embeddings_within_bounds\n                    ag__.converted_call(ag__.ld(tf).debugging.assert_less, (ag__.ld(tensor), ag__.converted_call(ag__.ld(tf).cast, (ag__.ld(embed_dim),), dict(dtype=ag__.ld(tensor).dtype), fscope)), dict(message=f\"The maximum value of {ag__.ld(tensor_name)} ({ag__.converted_call(ag__.ld(tf).math.reduce_max, (ag__.ld(tensor),), None, fscope)}) must be smaller than the embedding layer's input dimension ({ag__.ld(embed_dim)}). The likely cause is some problem at tokenization time.\"), fscope)\n            \n                TypeError: Exception encountered when calling layer 'embeddings' (type TFBertEmbeddings).\n                \n                in user code:\n                \n                    File \"/opt/conda/lib/python3.10/site-packages/transformers/models/bert/modeling_tf_bert.py\", line 202, in call  *\n                        check_embeddings_within_bounds(input_ids, self.config.vocab_size)\n                    File \"/opt/conda/lib/python3.10/site-packages/transformers/tf_utils.py\", line 161, in check_embeddings_within_bounds  *\n                        tf.debugging.assert_less(\n                \n                    TypeError: Value passed to parameter 'input' has DataType string not in list of allowed values: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, float16, uint32, uint64, qint8, quint8, qint32, qint16, quint16\n                \n                \n                Call arguments received by layer 'embeddings' (type TFBertEmbeddings):\n                  • input_ids=tf.Tensor(shape=(None, 1), dtype=string)\n                  • position_ids=None\n                  • token_type_ids=tf.Tensor(shape=(None, 1), dtype=int32)\n                  • inputs_embeds=None\n                  • past_key_values_length=0\n                  • training=True\n            \n            \n            Call arguments received by layer 'bert' (type TFBertMainLayer):\n              • input_ids=tf.Tensor(shape=(None, 1), dtype=string)\n              • attention_mask=None\n              • token_type_ids=None\n              • position_ids=None\n              • head_mask=None\n              • inputs_embeds=None\n              • encoder_hidden_states=None\n              • encoder_attention_mask=None\n              • past_key_values=None\n              • use_cache=True\n              • output_attentions=False\n              • output_hidden_states=False\n              • return_dict=True\n              • training=True\n        \n        \n        Call arguments received by layer 'tf_bert_model_3' (type TFBertModel):\n          • input_ids=tf.Tensor(shape=(None, 1), dtype=string)\n          • attention_mask=None\n          • token_type_ids=None\n          • position_ids=None\n          • head_mask=None\n          • inputs_embeds=None\n          • encoder_hidden_states=None\n          • encoder_attention_mask=None\n          • past_key_values=None\n          • use_cache=None\n          • output_attentions=None\n          • output_hidden_states=None\n          • return_dict=None\n          • training=True\n    \n    \n    Call arguments received by layer 'hugging_face_layer_3' (type HuggingFaceLayer):\n      • inputs=tf.Tensor(shape=(None, 1), dtype=string)\n      • kwargs={'training': 'True'}\n"
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-fe4ef6d65433>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Compile and train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'adam'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'binary_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m    194\u001b[0m                 ]\n\u001b[1;32m    195\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositional_args\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                     raise ValueError(\n\u001b[0m\u001b[1;32m    197\u001b[0m                         \u001b[0;34m\"Layers added to a Sequential model \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                         \u001b[0;34m\"can only have a single positional argument, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Layers added to a Sequential model can only have a single positional argument, the input tensor. Layer HuggingFaceLayer has multiple positional arguments: [<Parameter \"inputs\">, <Parameter \"**kwargs\">]"
      ]
     }
    ],
@@ -822,13 +688,6 @@
     "model.fit(train_data, train_labels, epochs=10)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,

benchmark/tensorflow_12/tensorflow_12_fixed.ipynb

@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T15:58:13.581455Z",
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T15:58:13.886817Z",
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T15:58:16.537257Z",
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T15:58:19.936834Z",
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T16:01:25.682273Z",
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T16:01:29.535618Z",
@@ -345,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T16:01:31.246819Z",
@@ -356,6 +356,14 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -375,89 +383,89 @@
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
+       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
+       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_24               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
+       "│ batch_normalization                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_25               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+       "│ batch_normalization_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
+       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_26               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+       "│ batch_normalization_2                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
+       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_27               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ batch_normalization_3                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
+       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_28               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ batch_normalization_4                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼──────────────────────────���──┼─────────────────┤\n",
-       "│ conv2d_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
+       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_29               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
+       "│ batch_normalization_5                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
+       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_30               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
+       "│ batch_normalization_6                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
+       "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_31               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
+       "│ batch_normalization_7                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
+       "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_32               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
+       "│ batch_normalization_8                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
+       "│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_33               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
+       "│ batch_normalization_9                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_37 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
+       "│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_34               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
+       "│ batch_normalization_10               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_38 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
+       "│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_35               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
+       "│ batch_normalization_11               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4608</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4608</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">18,878,464</span> │\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">18,878,464</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">16,781,312</span> │\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">16,781,312</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">81,940</span> │\n",
+       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">81,940</span> │\n",
        "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
        "</pre>\n"
       ],
@@ -465,89 +473,89 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m1,792\u001b[0m │\n",
+       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m1,792\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m36,928\u001b[0m │\n",
+       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m36,928\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_24               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │             \u001b[38;5;34m256\u001b[0m │\n",
+       "│ batch_normalization                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │             \u001b[38;5;34m256\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
+       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
+       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_25               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
+       "│ batch_normalization_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
+       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
        "├────────────────────────────────────���─┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_26               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
+       "│ batch_normalization_2                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
+       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
+       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_27               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ batch_normalization_3                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
+       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_28               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ batch_normalization_4                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_32 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
+       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_29               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
+       "│ batch_normalization_5                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_12 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
+       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_33 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m1,180,160\u001b[0m │\n",
+       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m1,180,160\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_30               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
+       "│ batch_normalization_6                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_34 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
+       "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_31               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
+       "│ batch_normalization_7                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_35 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
+       "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_32               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
+       "│ batch_normalization_8                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
+       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
+       "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_33               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n",
+       "│ batch_normalization_9                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_37 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
+       "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_34               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n",
+       "│ batch_normalization_10               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_38 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
+       "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ batch_normalization_35               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n",
+       "│ batch_normalization_11               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n",
        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_14 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
+       "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
+       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m18,878,464\u001b[0m │\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m18,878,464\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
+       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m16,781,312\u001b[0m │\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m16,781,312\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
+       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
        "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                  │          \u001b[38;5;34m81,940\u001b[0m │\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                  │          \u001b[38;5;34m81,940\u001b[0m │\n",
        "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
       ]
      },
@@ -648,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T16:01:31.528964Z",
@@ -678,9 +686,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2s/step - accuracy: 0.2000 - loss: 3.2265 - val_accuracy: 0.6875 - val_loss: 2.8994\n",
+      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2s/step - accuracy: 0.0327 - loss: 4.6730 - val_accuracy: 0.6875 - val_loss: 2.9083\n",
       "Epoch 2/2\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2s/step - accuracy: 0.5792 - loss: 1.7924 - val_accuracy: 0.5000 - val_loss: 2.9482\n"
+      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2s/step - accuracy: 0.6119 - loss: 1.7714 - val_accuracy: 0.6875 - val_loss: 2.9367\n"
      ]
     }
    ],

benchmark/tensorflow_12/tensorflow_12_reproduced.ipynb

@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T15:58:13.581455Z",
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T15:58:13.886817Z",
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T15:58:16.537257Z",
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T15:58:19.936834Z",
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T16:01:25.682273Z",
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T16:01:29.535618Z",
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T16:01:31.246819Z",
@@ -652,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-15T16:01:31.528964Z",
@@ -677,7 +677,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-bb53de7133d1>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-11-bb53de7133d1>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\u001b[0m in \u001b[0;36m_adjust_input_rank\u001b[0;34m(self, flat_inputs)\u001b[0m\n\u001b[1;32m    242\u001b[0m                     \u001b[0madjusted\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m                     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    245\u001b[0m                 \u001b[0;34mf\"Invalid input shape for input {x}. Expected shape \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m                 \u001b[0;34mf\"{ref_shape}, but input has incompatible shape {x.shape}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: Exception encountered when calling Sequential.call().\n\n\u001b[1mInvalid input shape for input Tensor(\"VGG19_1/Cast:0\", shape=(None, 100, 100), dtype=float32). Expected shape (None, 100, 100, 3), but input has incompatible shape (None, 100, 100)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 100, 100), dtype=uint8)\n  • training=True\n  • mask=None"

benchmark/tensorflow_13/tensorflow_13_fixed.ipynb

@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-04T16:21:43.780900Z",
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-04T16:22:12.413004Z",
@@ -305,7 +305,7 @@
        "(10, 768)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }

benchmark/tensorflow_13/tensorflow_13_reproduced.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-04T15:57:43.670730Z",
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-04T16:21:43.780900Z",
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-04T16:22:12.413004Z",
@@ -310,8 +310,8 @@
       "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()",
       "\nThe above exception was the direct cause of the following exception:\n",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-d10e2288d2b9>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmax_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m768\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxlen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_length\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-6-d10e2288d2b9>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmax_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m768\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxlen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_length\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-4-d10e2288d2b9>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmax_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m768\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxlen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_length\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-4-d10e2288d2b9>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmax_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m768\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxlen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_length\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/sequence_utils.py\u001b[0m in \u001b[0;36mpad_sequences\u001b[0;34m(sequences, maxlen, dtype, padding, truncating, value)\u001b[0m\n\u001b[1;32m     93\u001b[0m                 \u001b[0mflag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m                 \u001b[0;34m\"`sequences` must be a list of iterables. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0;34mf\"Found non-iterable: {str(x)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: `sequences` must be a list of iterables. Found non-iterable: 101"
      ]

benchmark/tensorflow_14/data/updated_vgg_face_weights.h5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:21c41bde5acc166c1aa37dec6e982a894f514e1ea5090c4bb4e419a3d5fd7e1d
+oid sha256:dbf0b8b0ca043f1ac26251ae64221be5e3824640c6ef9784521e4ed360efdc48
 size 60551544

benchmark/tensorflow_14/tensorflow_14_fixed.ipynb

@@ -180,7 +180,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 3s/step - accuracy: 0.1736 - loss: 1.9429\n"
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m162s\u001b[0m 4s/step - accuracy: 0.1995 - loss: 1.9225\n"
      ]
     }
    ],

benchmark/tensorflow_14/tensorflow_14_reproduced.ipynb

@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-16T18:38:15.987969Z",
@@ -72,7 +72,16 @@
      "shell.execute_reply.started": "2023-10-16T18:38:15.987942Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/zero_padding2d.py:72: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    }
+   ],
    "source": [
     "# Define VGG_FACE_MODEL architecture\n",
     "model = Sequential()\n",
@@ -121,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-16T18:53:21.121348Z",
@@ -141,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-16T18:53:24.463478Z",
@@ -166,7 +175,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-ec4455d9d5bd>\u001b[0m in \u001b[0;36m<cell line: 36>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;31m# Load the previously saved model weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/vgg_face_weights.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;31m# Continue training the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-4-ec4455d9d5bd>\u001b[0m in \u001b[0;36m<cell line: 36>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;31m# Load the previously saved model weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/vgg_face_weights.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;31m# Continue training the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/legacy/saving/legacy_h5_format.py\u001b[0m in \u001b[0;36mload_weights_from_hdf5_group\u001b[0;34m(f, model)\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[0mlayer_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_layer_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_names\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_layers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    358\u001b[0m             \u001b[0;34m\"Layer count mismatch when loading weights from file. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0;34mf\"Model expected {len(filtered_layers)} layers, found \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: Layer count mismatch when loading weights from file. Model expected 13 layers, found 16 saved layers."

benchmark/tensorflow_15/tensorflow_15_fixed.ipynb

@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:45.406959Z",
@@ -181,15 +181,7 @@
      "shell.execute_reply.started": "2023-08-13T14:02:45.406935Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Chic', 'Duck']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "class_names = train_ds.class_names\n",
     "print(class_names)"
@@ -204,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:45.418637Z",
@@ -214,23 +206,14 @@
      "shell.execute_reply.started": "2023-08-13T14:02:45.418605Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
-      "\u001b[1m94765736/94765736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "base_model = tf.keras.applications.ResNet50(weights = 'imagenet', include_top = False, input_shape = (224,224,3))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:51.116122Z",
@@ -259,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:51.199550Z",
@@ -277,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:51.215197Z",
@@ -298,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:51.238258Z",
@@ -314,9 +297,9 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 221ms/step - accuracy: 0.7926 - loss: 0.4553 - val_accuracy: 1.0000 - val_loss: 2.2706e-05 - learning_rate: 0.0010\n",
+      "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 453ms/step - accuracy: 0.7965 - loss: 0.4471 - val_accuracy: 0.9932 - val_loss: 0.0220 - learning_rate: 0.0010\n",
       "Epoch 2/2\n",
-      "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 193ms/step - accuracy: 1.0000 - loss: 7.1555e-06 - val_accuracy: 1.0000 - val_loss: 4.1497e-06 - learning_rate: 0.0010\n"
+      "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 318ms/step - accuracy: 1.0000 - loss: 2.8730e-06 - val_accuracy: 1.0000 - val_loss: 0.0018 - learning_rate: 0.0010\n"
      ]
     }
    ],
@@ -348,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:27:29.800492Z",
@@ -363,7 +346,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Test Loss: 0.00000\n",
+      "    Test Loss: 0.00153\n",
       "Test Accuracy: 100.00%\n"
      ]
     }
@@ -376,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:52:01.475250Z",
@@ -391,7 +374,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 164ms/step\n"
+      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 186ms/step\n"
      ]
     }
    ],
@@ -418,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:51:40.649738Z",
@@ -434,53 +417,53 @@
      "output_type": "stream",
      "text": [
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━��━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 214ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 215ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 219ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 212ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 201ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 246ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━���\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n"
      ]
     },
@@ -490,7 +473,7 @@
        "array([[146]], dtype=int32)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }

benchmark/tensorflow_15/tensorflow_15_reproduced.ipynb

@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:45.406959Z",
@@ -181,15 +181,7 @@
      "shell.execute_reply.started": "2023-08-13T14:02:45.406935Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Chic', 'Duck']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "class_names = train_ds.class_names\n",
     "print(class_names)"
@@ -204,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:45.418637Z",
@@ -214,23 +206,14 @@
      "shell.execute_reply.started": "2023-08-13T14:02:45.418605Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
-      "\u001b[1m94765736/94765736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "base_model = tf.keras.applications.ResNet50(weights = 'imagenet', include_top = False, input_shape = (224,224,3))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:51.116122Z",
@@ -259,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:51.199550Z",
@@ -277,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:51.215197Z",
@@ -298,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:02:51.238258Z",
@@ -314,9 +297,9 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 221ms/step - accuracy: 0.7926 - loss: 0.4553 - val_accuracy: 1.0000 - val_loss: 2.2706e-05 - learning_rate: 0.0010\n",
+      "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 578ms/step - accuracy: 0.8190 - loss: 0.4595 - val_accuracy: 0.9932 - val_loss: 0.0371 - learning_rate: 0.0010\n",
       "Epoch 2/2\n",
-      "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 193ms/step - accuracy: 1.0000 - loss: 7.1555e-06 - val_accuracy: 1.0000 - val_loss: 4.1497e-06 - learning_rate: 0.0010\n"
+      "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 321ms/step - accuracy: 0.9957 - loss: 0.0163 - val_accuracy: 1.0000 - val_loss: 5.0102e-05 - learning_rate: 0.0010\n"
      ]
     }
    ],
@@ -348,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:27:29.800492Z",
@@ -363,7 +346,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Test Loss: 0.00000\n",
+      "    Test Loss: 0.00002\n",
       "Test Accuracy: 100.00%\n"
      ]
     }
@@ -376,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:52:01.475250Z",
@@ -391,7 +374,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 164ms/step\n"
+      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 311ms/step\n"
      ]
     }
    ],
@@ -418,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-13T14:51:40.649738Z",
@@ -436,7 +419,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-eb8427388746>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtest_ds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m   \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m   \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-13-eb8427388746>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtest_ds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m   \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m   \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'predict_classes'"
      ]
     }

benchmark/tensorflow_2/tensorflow_2_fixed.ipynb

@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:50:38.166455Z",
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:50:45.790896Z",
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:50:56.614037Z",
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:51:29.354256Z",
@@ -151,6 +151,13 @@
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -163,7 +170,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/5\n"
+      "\r",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 910ms/step - accuracy: 0.3750 - loss: 0.7235"
      ]
     },
     {
@@ -178,25 +186,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5000 - loss: 0.6882 - val_accuracy: 0.5000 - val_loss: 3.6359\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.3750 - loss: 0.7235 - val_accuracy: 0.5000 - val_loss: 13.4394\n",
       "Epoch 2/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - accuracy: 0.6250 - loss: 0.7036 - val_accuracy: 0.5000 - val_loss: 4.5634\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 220ms/step - accuracy: 0.6250 - loss: 4.9176 - val_accuracy: 0.5000 - val_loss: 13.2867\n",
       "Epoch 3/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - accuracy: 0.7500 - loss: 1.6109 - val_accuracy: 0.5000 - val_loss: 5.2952\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 215ms/step - accuracy: 0.5000 - loss: 3.4270 - val_accuracy: 0.5000 - val_loss: 10.7268\n",
       "Epoch 4/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 247ms/step - accuracy: 0.7500 - loss: 1.8012 - val_accuracy: 0.5000 - val_loss: 2.9796\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 217ms/step - accuracy: 0.6250 - loss: 1.1602 - val_accuracy: 0.0000e+00 - val_loss: 5.7769\n",
       "Epoch 5/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - accuracy: 0.7500 - loss: 1.0304 - val_accuracy: 0.5000 - val_loss: 1.9335\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.2180 - loss: 2.2783   \n"
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 215ms/step - accuracy: 1.0000 - loss: 0.0021 - val_accuracy: 0.5000 - val_loss: 10.3910\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.2180 - loss: 12.0928\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[1.4066473245620728, 0.5]"
+       "[7.4195027351379395, 0.5]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:51:58.182938Z",
@@ -233,32 +241,14 @@
      "shell.execute_reply.started": "2023-12-20T19:51:58.182905Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.2180 - loss: 2.2783     \n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[1.4066473245620728, 0.5]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.evaluate(test_generator)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:53:59.121550Z",
@@ -268,35 +258,7 @@
      "shell.execute_reply.started": "2023-12-20T19:53:59.121510Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6962 - val_accuracy: 0.0000e+00 - val_loss: 5.6572\n",
-      "Epoch 2/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 969ms/step - accuracy: 1.0000 - loss: 0.0075 - val_accuracy: 0.5000 - val_loss: 35.4526\n",
-      "Epoch 3/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5000 - loss: 11.1134 - val_accuracy: 0.5000 - val_loss: 66.2266\n",
-      "Epoch 4/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5000 - loss: 31.7608 - val_accuracy: 0.5000 - val_loss: 48.0883\n",
-      "Epoch 5/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 986ms/step - accuracy: 0.7500 - loss: 11.7477 - val_accuracy: 0.0000e+00 - val_loss: 18.7021\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.2336 - loss: 6.5994 \n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[8.03508472442627, 0.25]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def prepare_model():\n",
     "    model = Sequential()\n",
@@ -322,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:56:11.309742Z",
@@ -332,35 +294,7 @@
      "shell.execute_reply.started": "2023-12-20T19:56:11.309713Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.7500 - loss: 0.6904 - val_accuracy: 0.5000 - val_loss: 1.0907\n",
-      "Epoch 2/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 264ms/step - accuracy: 0.5000 - loss: 0.6330 - val_accuracy: 0.5000 - val_loss: 1.3007\n",
-      "Epoch 3/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 249ms/step - accuracy: 0.5000 - loss: 0.7753 - val_accuracy: 0.5000 - val_loss: 0.7774\n",
-      "Epoch 4/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - accuracy: 0.7500 - loss: 0.4554 - val_accuracy: 0.5000 - val_loss: 0.9031\n",
-      "Epoch 5/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 271ms/step - accuracy: 1.0000 - loss: 0.3848 - val_accuracy: 0.5000 - val_loss: 0.8823\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.3458 - loss: 0.7522   \n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[0.7701693177223206, 0.375]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def prepare_model():\n",
     "    model = Sequential()\n",
@@ -393,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:57:28.208777Z",
@@ -403,35 +337,7 @@
      "shell.execute_reply.started": "2023-12-20T19:57:28.208744Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.3750 - loss: 0.7022 - val_accuracy: 0.5000 - val_loss: 0.9562\n",
-      "Epoch 2/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 256ms/step - accuracy: 0.6250 - loss: 0.6158 - val_accuracy: 0.5000 - val_loss: 2.0259\n",
-      "Epoch 3/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step - accuracy: 0.5000 - loss: 0.8807 - val_accuracy: 0.0000e+00 - val_loss: 1.0135\n",
-      "Epoch 4/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.8750 - loss: 0.3902 - val_accuracy: 0.5000 - val_loss: 1.2975\n",
-      "Epoch 5/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 269ms/step - accuracy: 0.7500 - loss: 0.4320 - val_accuracy: 0.5000 - val_loss: 1.1843\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.2180 - loss: 1.2437   \n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[0.8738863468170166, 0.5]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def prepare_model():\n",
     "    model = Sequential()\n",
@@ -464,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T20:04:08.984286Z",
@@ -474,200 +380,7 @@
      "shell.execute_reply.started": "2023-12-20T20:04:08.984240Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"sequential_10\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_43 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">223</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">223</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_44 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">223</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">223</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_45 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_46 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_47 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_48 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_49 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_50 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_51 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_52 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_53 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_54 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_55 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ flatten_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18432</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">75,501,568</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │      <span style=\"color: #00af00; text-decoration-color: #00af00\">16,781,312</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,194</span> │\n",
-       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_43 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m223\u001b[0m, \u001b[38;5;34m223\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m1,792\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_44 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m223\u001b[0m, \u001b[38;5;34m223\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m36,928\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_26 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_45 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m73,856\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_46 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │         \u001b[38;5;34m147,584\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_27 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_47 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_48 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_49 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_28 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_50 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m1,180,160\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_51 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_52 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_29 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼──────────────���──────────────┼─────────────────┤\n",
-       "│ conv2d_53 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_54 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_55 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_30 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ flatten_10 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18432\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_29 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m75,501,568\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_30 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │      \u001b[38;5;34m16,781,312\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_31 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)                   │           \u001b[38;5;34m8,194\u001b[0m │\n",
-       "└──────────────────────────────────────┴─────────────────────────────┴─────────────��───┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">107,005,762</span> (408.19 MB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m107,005,762\u001b[0m (408.19 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">107,005,762</span> (408.19 MB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m107,005,762\u001b[0m (408.19 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6933 - val_accuracy: 0.5000 - val_loss: 2.4683\n",
-      "Epoch 2/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4s/step - accuracy: 0.5000 - loss: 1.1338 - val_accuracy: 0.5000 - val_loss: 0.7069\n",
-      "Epoch 3/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4s/step - accuracy: 0.5000 - loss: 0.7134 - val_accuracy: 0.5000 - val_loss: 0.7027\n",
-      "Epoch 4/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4s/step - accuracy: 0.5000 - loss: 0.7037 - val_accuracy: 0.5000 - val_loss: 0.6958\n",
-      "Epoch 5/5\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4s/step - accuracy: 0.5000 - loss: 0.6952 - val_accuracy: 0.5000 - val_loss: 0.6949\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 110ms/step - accuracy: 0.2180 - loss: 0.7284\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[0.6948874592781067, 0.5]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from keras.models import Sequential\n",
     "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",

benchmark/tensorflow_2/tensorflow_2_reproduced.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:50:38.166455Z",
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:50:45.790896Z",
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:50:56.614037Z",
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-20T19:51:29.354256Z",
@@ -151,6 +151,14 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -160,15 +168,15 @@
     },
     {
      "ename": "ValueError",
-     "evalue": "Exception encountered when calling Sequential.call().\n\n\u001b[1mInput 0 of layer \"dense_2\" is incompatible with the layer: expected axis -1 of input shape to have value 76832, but received input with shape (None, 387200)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 223, 223, 3), dtype=float32)\n  • training=True\n  • mask=None",
+     "evalue": "Exception encountered when calling Sequential.call().\n\n\u001b[1mInput 0 of layer \"dense\" is incompatible with the layer: expected axis -1 of input shape to have value 76832, but received input with shape (None, 387200)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 223, 223, 3), dtype=float32)\n  • training=True\n  • mask=None",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-88477c8a9c0c>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepare_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m model.fit(train_generator,\n\u001b[0m\u001b[1;32m     12\u001b[0m                     \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalid_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m                     epochs=5)\n",
+      "\u001b[0;32m<ipython-input-5-88477c8a9c0c>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepare_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m model.fit(train_generator,\n\u001b[0m\u001b[1;32m     12\u001b[0m                     \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalid_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m                     epochs=5)\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/layers/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m    225\u001b[0m                     \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 }:\n\u001b[0;32m--> 227\u001b[0;31m                     raise ValueError(\n\u001b[0m\u001b[1;32m    228\u001b[0m                         \u001b[0;34mf'Input {input_index} of layer \"{layer_name}\" is '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m                         \u001b[0;34mf\"incompatible with the layer: expected axis {axis} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Exception encountered when calling Sequential.call().\n\n\u001b[1mInput 0 of layer \"dense_2\" is incompatible with the layer: expected axis -1 of input shape to have value 76832, but received input with shape (None, 387200)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 223, 223, 3), dtype=float32)\n  • training=True\n  • mask=None"
+      "\u001b[0;31mValueError\u001b[0m: Exception encountered when calling Sequential.call().\n\n\u001b[1mInput 0 of layer \"dense\" is incompatible with the layer: expected axis -1 of input shape to have value 76832, but received input with shape (None, 387200)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 223, 223, 3), dtype=float32)\n  • training=True\n  • mask=None"
      ]
     }
    ],

benchmark/tensorflow_3/train.h5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:37fd312a2494e4760afd9135e06140482c2cf451b7360272df2f6c80c7c09db7
-size 28529736
+oid sha256:991c9642eb194621a1db5271828bdb8191d354f7b14cd59242b0294de9c4a45e
+size 28529464

benchmark/tensorflow_4/tensorflow_4_reproduced.ipynb

@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:53:47.494705Z",
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:53:47.510005Z",
@@ -129,18 +129,7 @@
      "shell.execute_reply.started": "2023-10-29T14:53:47.509969Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Class_names1_train1: 64\n",
-      "Class_names2_train 64\n",
-      "Class_names1_test 21\n",
-      "Class_names2_test 21\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# train\n",
     "Class_names1_train = set()\n",
@@ -174,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:53:47.586296Z",
@@ -182,55 +171,10 @@
      "iopub.status.idle": "2023-10-29T14:53:47.998495Z",
      "shell.execute_reply": "2023-10-29T14:53:47.997197Z",
      "shell.execute_reply.started": "2023-10-29T14:53:47.586236Z"
-    }
+    },
+    "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'04': 18,\n",
-       " '07': 18,\n",
-       " '08': 18,\n",
-       " '09': 18,\n",
-       " '10': 18,\n",
-       " '11': 18,\n",
-       " '12': 18,\n",
-       " '13': 18,\n",
-       " '14': 18,\n",
-       " '15': 18,\n",
-       " '16': 18,\n",
-       " '17': 18,\n",
-       " '18': 18,\n",
-       " '19': 18,\n",
-       " '22': 18,\n",
-       " '25': 18,\n",
-       " '28': 18,\n",
-       " '29': 18,\n",
-       " '30': 18,\n",
-       " '31': 18,\n",
-       " '32': 18,\n",
-       " '33': 18,\n",
-       " '34': 18,\n",
-       " '35': 18,\n",
-       " '36': 18,\n",
-       " '37': 18,\n",
-       " '38': 18,\n",
-       " '39': 18,\n",
-       " '40': 18,\n",
-       " '41': 18,\n",
-       " '42': 19,\n",
-       " '43': 18,\n",
-       " '44': 24,\n",
-       " '45': 18,\n",
-       " '46': 18,\n",
-       " '47': 18}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "freq= {}\n",
     "directory='data/'\n",
@@ -249,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:53:48.001500Z",
@@ -259,27 +203,7 @@
      "shell.execute_reply.started": "2023-10-29T14:53:48.001445Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'02': 18,\n",
-       " '03': 18,\n",
-       " '05': 18,\n",
-       " '06': 18,\n",
-       " '20': 18,\n",
-       " '21': 18,\n",
-       " '23': 18,\n",
-       " '24': 18,\n",
-       " '26': 18,\n",
-       " '27': 18}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "freq= {}\n",
     "directory='data'\n",
@@ -319,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:53:48.147497Z",
@@ -336,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:53:48.161246Z",
@@ -346,7 +270,17 @@
      "shell.execute_reply.started": "2023-10-29T14:53:48.161197Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of real_images 887\n",
+      "number of real_images 1139\n",
+      "number of forged_images 1010\n"
+     ]
+    }
+   ],
    "source": [
     "def find_files_in_folder(path, folder_name):\n",
     "    folder_path = None\n",
@@ -502,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:54:18.031748Z",
@@ -549,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:54:18.228892Z",
@@ -574,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:54:18.239789Z",
@@ -585,37 +519,109 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">492,544</span> │\n",
+       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">514</span> │\n",
+       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │         \u001b[38;5;34m492,544\u001b[0m │\n",
+       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)                   │             \u001b[38;5;34m514\u001b[0m │\n",
+       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">493,058</span> (1.88 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m493,058\u001b[0m (1.88 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">493,058</span> (1.88 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m493,058\u001b[0m (1.88 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " lstm (LSTM)                 (None, 256)               492544    \n",
-      "                                                                 \n",
-      " dense (Dense)               (None, 2)                 514       \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 493,058\n",
-      "Trainable params: 493,058\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
       "Epoch 1/10\n"
      ]
     },
     {
      "ename": "ValueError",
-     "evalue": "in user code:\n\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1284, in train_function  *\n        return step_function(self, iterator)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1268, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in run_step  **\n        outputs = model.train_step(data)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1050, in train_step\n        y_pred = self(x, training=True)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/input_spec.py\", line 235, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer 'sequential' (type Sequential).\n    \n    Input 0 of layer \"lstm\" is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: (None, 1, 224, 224)\n    \n    Call arguments received by layer 'sequential' (type Sequential):\n      • inputs=tf.Tensor(shape=(None, 1, 224, 224), dtype=float32)\n      • training=True\n      • mask=None\n",
+     "evalue": "Exception encountered when calling Sequential.call().\n\n\u001b[1mInvalid input shape for input Tensor(\"data:0\", shape=(None, 1, 224, 224), dtype=float32). Expected shape (None, 1, 224), but input has incompatible shape (None, 1, 224, 224)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 1, 224, 224), dtype=float32)\n  • training=True\n  • mask=None",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[17], line 29\u001b[0m\n\u001b[1;32m     26\u001b[0m early_stop \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# train the model\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_split\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m.2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mearly_stop\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;66;03m# plot accuracy and loss\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-      "File \u001b[0;32m/tmp/__autograph_generated_filep2cz96wq.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1284, in train_function  *\n        return step_function(self, iterator)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1268, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in run_step  **\n        outputs = model.train_step(data)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1050, in train_step\n        y_pred = self(x, training=True)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/input_spec.py\", line 235, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer 'sequential' (type Sequential).\n    \n    Input 0 of layer \"lstm\" is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: (None, 1, 224, 224)\n    \n    Call arguments received by layer 'sequential' (type Sequential):\n      • inputs=tf.Tensor(shape=(None, 1, 224, 224), dtype=float32)\n      • training=True\n      • mask=None\n"
+      "\u001b[0;32m<ipython-input-7-bb833a2141e2>\u001b[0m in \u001b[0;36m<cell line: 29>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;31m# train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mearly_stop\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;31m# plot accuracy and loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\u001b[0m in \u001b[0;36m_adjust_input_rank\u001b[0;34m(self, flat_inputs)\u001b[0m\n\u001b[1;32m    242\u001b[0m                     \u001b[0madjusted\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m                     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    245\u001b[0m                 \u001b[0;34mf\"Invalid input shape for input {x}. Expected shape \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m                 \u001b[0;34mf\"{ref_shape}, but input has incompatible shape {x.shape}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Exception encountered when calling Sequential.call().\n\n\u001b[1mInvalid input shape for input Tensor(\"data:0\", shape=(None, 1, 224, 224), dtype=float32). Expected shape (None, 1, 224), but input has incompatible shape (None, 1, 224, 224)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 1, 224, 224), dtype=float32)\n  • training=True\n  • mask=None"
      ]
     }
    ],
@@ -704,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T14:54:40.076229Z",
@@ -714,86 +720,7 @@
      "shell.execute_reply.started": "2023-10-29T14:54:40.076187Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_1\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " conv2d (Conv2D)             (None, 216, 216, 16)      1312      \n",
-      "                                                                 \n",
-      " max_pooling2d (MaxPooling2D  (None, 108, 108, 16)     0         \n",
-      " )                                                               \n",
-      "                                                                 \n",
-      " conv2d_1 (Conv2D)           (None, 104, 104, 32)      12832     \n",
-      "                                                                 \n",
-      " max_pooling2d_1 (MaxPooling  (None, 52, 52, 32)       0         \n",
-      " 2D)                                                             \n",
-      "                                                                 \n",
-      " conv2d_2 (Conv2D)           (None, 50, 50, 32)        9248      \n",
-      "                                                                 \n",
-      " max_pooling2d_2 (MaxPooling  (None, 25, 25, 32)       0         \n",
-      " 2D)                                                             \n",
-      "                                                                 \n",
-      " conv2d_3 (Conv2D)           (None, 23, 23, 16)        4624      \n",
-      "                                                                 \n",
-      " max_pooling2d_3 (MaxPooling  (None, 11, 11, 16)       0         \n",
-      " 2D)                                                             \n",
-      "                                                                 \n",
-      " conv2d_4 (Conv2D)           (None, 9, 9, 8)           1160      \n",
-      "                                                                 \n",
-      " max_pooling2d_4 (MaxPooling  (None, 4, 4, 8)          0         \n",
-      " 2D)                                                             \n",
-      "                                                                 \n",
-      " flatten (Flatten)           (None, 128)               0         \n",
-      "                                                                 \n",
-      " dense_1 (Dense)             (None, 256)               33024     \n",
-      "                                                                 \n",
-      " dropout (Dropout)           (None, 256)               0         \n",
-      "                                                                 \n",
-      " dense_2 (Dense)             (None, 128)               32896     \n",
-      "                                                                 \n",
-      " dropout_1 (Dropout)         (None, 128)               0         \n",
-      "                                                                 \n",
-      " dense_3 (Dense)             (None, 1)                 129       \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 95,225\n",
-      "Trainable params: 95,225\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/3\n",
-      "22/22 [==============================] - 85s 4s/step - loss: 0.6931 - accuracy: 0.5578 - val_loss: 0.6932 - val_accuracy: 0.4622\n",
-      "Epoch 2/3\n",
-      "22/22 [==============================] - 81s 4s/step - loss: 0.6931 - accuracy: 0.5469 - val_loss: 0.6932 - val_accuracy: 0.4622\n",
-      "Epoch 3/3\n",
-      "22/22 [==============================] - 83s 4s/step - loss: 0.6931 - accuracy: 0.5469 - val_loss: 0.6932 - val_accuracy: 0.4622\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# import tensorflow as tf\n",
     "from tensorflow import keras\n",
@@ -868,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T15:00:24.673773Z",
@@ -878,17 +805,7 @@
      "shell.execute_reply.started": "2023-10-29T15:00:24.673739Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14/14 [==============================] - 5s 353ms/step - loss: 0.6931 - accuracy: 0.5326\n",
-      "Final Loss: 69.31%\n",
-      "Final Accuracy: 53.26%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# get the final loss and accuracy\n",
     "final_loss, final_accuracy = model.evaluate(test_data, test_labels)\n",
@@ -900,7 +817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-29T15:01:49.457438Z",

benchmark/tensorflow_5/tensorflow_5_fixed.ipynb

@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:26:39.148833Z",
@@ -161,25 +161,14 @@
      "shell.execute_reply.started": "2023-12-07T13:26:39.148792Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rating_df['rating'].isna().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T11:29:10.997404Z",
@@ -314,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -339,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T12:01:00.036863Z",
@@ -362,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T12:01:11.473101Z",
@@ -379,7 +368,7 @@
        "1000"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -390,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:52:39.565889Z",
@@ -445,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T12:39:18.292278Z",
@@ -493,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T12:39:33.627085Z",
@@ -513,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:06:05.665173Z",
@@ -675,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:06:21.674206Z",
@@ -734,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:06:27.387608Z",
@@ -744,198 +733,14 @@
      "shell.execute_reply.started": "2023-12-07T13:06:27.387574Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[array([17630, 27556, 13937,  6410, 33577,  7397, 14950,  4690,  8329,\n",
-      "       29061,  7162,  3565, 28951, 25967, 14472, 24584, 12455, 22938,\n",
-      "       17548, 31256, 15968, 26333, 21170,  5644,  8341,  8973, 28151,\n",
-      "       22054,  6231, 25095, 18001, 34150, 31555,  7449, 21938, 17771,\n",
-      "       18300, 11871,  4932, 26034, 21251, 20301, 25261,  5125, 16680,\n",
-      "         959, 30762, 10490,  5226,  7564, 28018,  1597, 30405, 10550,\n",
-      "       29160, 21862,  3093,  6123, 32987, 29897, 16679,  3647, 18791,\n",
-      "       32300,  7848, 22732,  2268, 28430,  1076,  2799,  8601, 12904,\n",
-      "       27698, 11231, 28343, 12641, 14211, 13741,  2279, 22745, 17313,\n",
-      "       30535,  2739, 13346, 27555, 23985, 18256, 14804, 14350, 21553,\n",
-      "       12733, 12718, 29325, 34554, 23170, 26063, 23198, 13095, 32036,\n",
-      "         122, 21349,  3502,  4486, 32575, 16810, 32495,  4401, 29738,\n",
-      "       13539, 23277, 18833, 29129,  6204,  5266,  2645, 13997, 34273,\n",
-      "       19991, 17705, 10982,  1163,  7557, 16877,  9780, 30575, 20533,\n",
-      "        9514,  2508,  5533,  5678, 27996,  5977, 10558, 24740, 25719,\n",
-      "       30181, 25461,  6821, 22322,  3447, 22399,  2421,   809, 22121,\n",
-      "       12873, 18028,  3399, 18688, 22264,  7352,  6118, 10299, 17261,\n",
-      "       28591, 22939, 13935, 24908,  6160, 18178, 33600,  7448, 22740,\n",
-      "       23335, 24857, 15309, 26354,    21,   153,  3487, 11600, 16402,\n",
-      "       24167,  6032, 25878, 32739, 32814,  4642, 19704, 32375, 28072,\n",
-      "       15439, 13472, 19371, 32269,  7999,  4626, 30984, 16619,  6780,\n",
-      "       24680,  2580, 19666, 16492,  7361, 14336, 13449, 26324,  5009,\n",
-      "        6886,  2596,  2420,  5262,  8772, 31115, 28278, 18753, 28002,\n",
-      "       24693, 11623, 21253,   583, 21768, 24725, 15650, 28871, 29975,\n",
-      "        6846, 15390, 29610, 25475, 32834, 26205, 19086, 13656, 19964,\n",
-      "        2196, 22724,  3971, 17867,  4549, 21626,  7774, 18868,  8838,\n",
-      "       27074,  7938, 11370, 29672, 17699, 33478, 35010, 33272,  2375,\n",
-      "       16637,   249, 32277,  6027, 31677,  7319, 10839, 12402, 21187,\n",
-      "       34357, 30612,  7563, 25202, 11490, 29636, 21568, 18139, 28804,\n",
-      "       21723, 32930,  8239, 25650, 18068, 28889, 11290,  7831, 31058,\n",
-      "       20878, 33222, 23984, 27336, 22338, 16455,  4945, 13109,   936,\n",
-      "       19330, 25844,  6134, 28040, 12289,  3579, 12373, 24728, 33038,\n",
-      "       19712, 18867, 22911,  2356,  2374,  6673,  4075, 18883, 23604,\n",
-      "        4091, 15689, 34327, 17773,  9799,  4599, 16508, 33739,  3603,\n",
-      "       13707, 11520,  7256, 28988, 31751, 33476, 23564, 18026, 21009,\n",
-      "       25130, 33308, 14131, 16911, 27344, 13842, 17409, 27568, 26266,\n",
-      "       23370, 11402, 12198, 21192, 20649,  3252,  7748, 27757,  1311,\n",
-      "       21700,  5059,  8424,  1678, 12286, 27390,  9873, 32625, 22740,\n",
-      "         459, 21740,  1260, 14810,  3504, 28309, 11140, 27923, 28160,\n",
-      "       30247, 33977, 15226,  5845,  6526,  9778, 22587, 28247, 23472,\n",
-      "        8361, 26176, 20264, 15597,   310, 12539,  3133,  3266,  3573,\n",
-      "        2400, 33918, 20250, 21083, 34932,  2676, 23694,  7144,  1543,\n",
-      "       21502,  2748, 27322, 11214, 30610, 20062, 30822, 16234, 20458,\n",
-      "       33609, 17874, 21878, 30033,  3764, 24982,  9061, 34878, 23011,\n",
-      "       29819, 30958, 34996,  9816, 31303, 32824,  6666, 19493,  4498,\n",
-      "        2973, 18283, 21483, 17284, 18478, 26370,  1999, 34846, 19173,\n",
-      "        5186, 34683, 21214,   824, 17986,  5772, 11766,  5400,  7748,\n",
-      "       32078, 21550, 28336, 24754, 21376,   944, 19918, 29731,  4242,\n",
-      "         278, 34075,  5048, 12332, 34213, 10768,  6995, 14927, 14640,\n",
-      "       24547, 20990, 31891, 10912, 11758,  2238,  4955,  7024,  9619,\n",
-      "       21261, 18472, 12777, 34941, 21419,  6712, 10823,  4472,  8104,\n",
-      "       20174, 34200, 26900, 22180, 26033, 16957, 12583,  3143, 19361,\n",
-      "       18719, 34750, 34833, 32384,  8595, 32595, 29452, 32556, 20299,\n",
-      "       23598, 15338, 14341, 26747, 16193,   615,  5024, 29974, 20638,\n",
-      "       17545, 17970, 15409, 22578, 23224, 12107, 30788,  4185, 34737,\n",
-      "       26471, 18767,  3615,  6585, 15820, 19008, 26366, 15312,   772,\n",
-      "       32935, 15746, 32073, 24812,  1008, 13476, 28316,  1282, 14786,\n",
-      "        2780, 31565, 31798, 29247, 21990, 35135, 33211, 19527, 14751,\n",
-      "       13763, 17519,  1421,  5820, 33762, 20645,  9872, 19254, 33879,\n",
-      "        5513,  1472,  9925,  8131, 18388, 22695, 20848, 25002, 23920,\n",
-      "       28597, 33794, 33694, 23721,   472, 33339, 33661, 31408, 10110,\n",
-      "       20205,  6321, 32672,  3491, 19041,  8449, 34096, 13354, 15173,\n",
-      "       27413,  2308, 25513, 15043,  6349, 22146, 30401, 28383, 16941,\n",
-      "       23153, 21250, 11326,   891,   210,  4653,   356, 25113, 15926,\n",
-      "       28732, 17940, 32930,  2368, 21821, 13653, 13414,  4270, 32733,\n",
-      "       25328, 34488, 33901, 15091, 29997, 27300, 26279, 15404,  2390,\n",
-      "       33826, 16589, 22358,  4873,  3877, 18209, 12889,  3408, 30505,\n",
-      "       15329,  8950,  9737, 12358,  5671, 20472,  4090, 31772, 12943,\n",
-      "        4435, 14907, 29127, 14288, 16278,  2697, 25042, 21239, 33703,\n",
-      "       10334, 19925,  8715, 16876,  4625,  5655, 14892, 29660, 20537,\n",
-      "       15841,  5381, 32041, 15230, 28034, 12449, 30398, 21595, 25228,\n",
-      "         857, 14858,  8707, 14140, 26814,  9726, 23086, 26395, 21845,\n",
-      "        4108, 15942, 18478, 14820, 23897, 15998,   782,  6727, 31703,\n",
-      "       23781, 23670,  5993,   744,  9945,  7761, 30040,  8247, 16086,\n",
-      "       17018, 33425, 32353, 24268, 25085,  3749, 25961,   592,  9991,\n",
-      "        7560,  3949, 11680,  7125, 19358, 17785,  9502, 14193, 13259,\n",
-      "        5633, 32796, 15609, 22852, 15521,  6812,  6641,  5189, 13897,\n",
-      "       17779, 17962, 26620,   809, 26203, 24145,   733, 13526, 14565,\n",
-      "        4034, 29322,  9222,  3284, 25320,  4685, 32480,  8810, 22567,\n",
-      "        4111,  4342, 31467, 29518,  8817, 24310, 31838, 10533,  8568,\n",
-      "       20363, 15037, 24793,  6384, 34252, 25320, 27556, 13298,  6543,\n",
-      "       28210,  7691,  1306, 24542,  4535, 11938, 31032, 17701, 33193,\n",
-      "       21211, 32827, 25177,  6555, 12448, 12864, 21835, 14681,  9801,\n",
-      "       16416, 25821, 32923, 14718,  1029,  4278,  5750, 31825, 22312,\n",
-      "       31069, 32796,  5884,  8131, 18888,  9968, 19699, 21704, 28080,\n",
-      "        4442, 14213,  9401,   287, 13188, 24082, 13652, 33361, 20994,\n",
-      "       24635, 28069, 27122,  6778, 23789, 14952, 11317,  1539, 24729,\n",
-      "       22993, 13483,  5740, 18419, 12740, 13732, 14637, 29962, 13212,\n",
-      "       29317, 29973,  6835,  1023,  8062, 16111, 11874, 28108]), array([  783,  3996,   981,   856,  8239,  1910,    22,   860,  2189,\n",
-      "        4230,   313,    53,   141,  3803,  6515,   656,  1786,  1982,\n",
-      "        1341,   505,   189,  2108,  3701,   585,   253,   764,  3358,\n",
-      "        1962,  5778,  1258,  3586,  4015,  1922,   215,  1057,  3646,\n",
-      "        3085,  9897,  2969,   396,  4326,  2227,   272,  2667,  1939,\n",
-      "        1817,  3507,   638,  3364,   369,   769,  2358,  1079,   141,\n",
-      "         272,   881,  1050,  4364,  3391,  5015,   263,  3056,  2652,\n",
-      "        6939,  2040,   233,  9233,  3519,  3399,  1803,  1355,   714,\n",
-      "        4150,   654,  3578,  2029,  3351,  1120,  2544,   317,   524,\n",
-      "        1126,   682,   764,  1474,  3124,  5622,  2628,  1707,  2767,\n",
-      "        2163,  4290,  3507,   346,   495,   916,     9,  1148,   224,\n",
-      "        9830,   860,   599,   966,   216,  2488,  1031,   474,  8057,\n",
-      "         840,   474,  2287, 10719,   867,  1799,    13,   302,  1340,\n",
-      "        2062,   532,  1945,   488,  2623,   398,  2758,   172,  1060,\n",
-      "         415,   911,   662,   812,  2252,   832,  3909,  7875,  3063,\n",
-      "        1938,  4133,  2977,  4439,  6398,   697,  2319,  3744,   395,\n",
-      "        1794, 10117,  2639,  2325,  3860,  1947,   806,  2623,   584,\n",
-      "        4727,  1087,   105,  1743,  4693,   244,  1072,   908,  1995,\n",
-      "         584,    49,  5167,   620,  3705, 10375, 14587,  4604,    56,\n",
-      "        4447,  3615,  2310,   669,  1803,  1863,  1106,   802,  1870,\n",
-      "        1307,  1966,  6357,  1039,  3732,  7854,  2958,  1876,  3407,\n",
-      "        5349, 10132,   637,  9147,  1605,  5363,   631,  3619,  2253,\n",
-      "        4971,  1342,   433,  8058,  3193,   649,   954,   393,  3255,\n",
-      "         858,   442,  1039,   597,   411,   556,   317,  3421,  5795,\n",
-      "        4772,  2300,   152,  1834,   898,  1702,  1096,  8244,   585,\n",
-      "         843,  1989,  3808,  1268,  8600,  4278,  3825,  8001,  1721,\n",
-      "        2998,   568,  3857,  1619,  2847,  1775,  2171,  2041,   176,\n",
-      "        4169,   763,   402,   146,  2077,  2795,   968,  4856,    61,\n",
-      "         840,  4481,  2265,  1480,  2262,   171,  1246,   177,   263,\n",
-      "         356,  2309,  2402,  2310,   393,  1879,   162,  3614,  1937,\n",
-      "        2996,   324,  1091,  1207,  5442,  1136,  1008,  1958,  4017,\n",
-      "        3322,  1929,  9494,   346,  2890,   222,   858,  1552,   303,\n",
-      "        2901,  2864,   803,  5328,   143,  2728,   878,  2969,   982,\n",
-      "         287,    15,   315,  1282,   610,  2066,   637,    87, 10603,\n",
-      "         139,   597,   124,  2715,   289,    53,  3403,  4172,   708,\n",
-      "         512,  3618,   202,  3766,  1862,   333,    24,    83,  8090,\n",
-      "        4773,  1049,  2307,   102,  3492,  2172,  7953,  1134,   189,\n",
-      "         529,   929,   338,  2975,  2871,   334,  3741,  2442,  2860,\n",
-      "        5294,  4880,  4128,   383,   361,  1607,  2154,   678,     2,\n",
-      "         454,  2144,   932,   629,  1913,  2604,   557,  1530,  2030,\n",
-      "         124,   647,  1042,    55,   981,    56,   133,  8540, 13884,\n",
-      "         669,  1985,   253, 10533,  9310,  2082,  3728,   322,   831,\n",
-      "        3619,   126,  3664,   807,  5042,  2081,   719,  1325,   274,\n",
-      "        1187,    18,   753,  7921,  1003, 10406,  1026,   166,   847,\n",
-      "        4390,   416,  2801,   315,   844,   675,  1790,  1140,  4460,\n",
-      "         263,   409,   310, 15209,   941,  3450,   564,  1086,   223,\n",
-      "         978,  6194,   758,  1008,   676,  3438,   882,  1143,  4711,\n",
-      "          99,  7330,  2338,  2783,  4471,  5889,  5501,   618,  3246,\n",
-      "        2260,  3881,   604,  1151,   919,  2022,  2080,  4104,   982,\n",
-      "         304,     6,   346,  5244,  2100,  4519,   183,  1712,  1901,\n",
-      "         808,   249,  2116,  3530, 10153,   115, 10351,  1101,  1345,\n",
-      "        2355,  5307,   605,  2873,   581,   317,  2026,  1258,  2067,\n",
-      "        6357,  3061,  3853,  4475,  2213,   930,    72,  1969,   852,\n",
-      "         607,  2217,  1162,  3610,   387,   725,  7535,   731,  4606,\n",
-      "         605,   375,  1989,   711,  3151,   352,  2357,   604,  2018,\n",
-      "         269,   112,   860,   397,   447,  1969,  1073,   267,  9774,\n",
-      "         445,  2740,  4987,   728,  2588,  4958,  5782,  2919,   351,\n",
-      "         118,   489,   229,   952,   202,  2154,  1821,    34,   632,\n",
-      "         963,   436,    96,   506,  5860,  8614,  6122,  3720,  2085,\n",
-      "        2062,    53,  3243,  1347,   220,  3405,   861,  1552,   109,\n",
-      "         398,   356,  2613,   209,   462,   709,    99,  2632,   220,\n",
-      "        2045,   900,   396,   535,   196,  2135,  3590,  4258,   552,\n",
-      "        1726,  4662,  5371,  2980,  3397,   615,  2202,   594,   857,\n",
-      "          18,   989,  5471,   875,  4028,  2065,   179,   310,  3893,\n",
-      "          85,  2478,  4082,   961,  2189,    55,  9315,  5251,   266,\n",
-      "         566,  2267,  1901,  4259,   648,  1004,   182,  1128,  9666,\n",
-      "         302,  4453,  3833,   611,  5054,  2766, 10541,   314,   635,\n",
-      "          80,  3970,   319,   126,    99, 10465,   622,  1008,   641,\n",
-      "         312,  3239,  2014,   742,   171,  7962,  5153,  1378,   742,\n",
-      "        3447,  2176,   579,  1150,  1922,  5403,  1792,   207,   802,\n",
-      "         342,  2774,  5876,   895,  4276,  3357, 11850,    19,  2540,\n",
-      "        3503,   644,   557,    84,  2075,  1039,  1953,  2337,   234,\n",
-      "        1292,  2747,   790,  3767,   561,   472,   219,  6050,   464,\n",
-      "         898,  2182,  1881,   553,   910,  3681,   952,  2649,   118,\n",
-      "        2062,   178,   736,  2152,  1863,  7966,  3917,   413,  6456,\n",
-      "        2470,  2482,  6476,   377,  2321,  1265,  2142,   864,  2436,\n",
-      "         724,  8372,    18,  4350,  4700,  2153,  3950,    66,  1689,\n",
-      "         198,  3926,   476,   498,   346,  1816,   146,  1760,    80,\n",
-      "        3291,   424,  1057,  1915,  4203,   592,  1956,   162,  2039,\n",
-      "        3526,   555,   189,  4492,  3522,  6172,  1127,  2610,  7985,\n",
-      "        3066,   218,  1203,   716,   752,  2895,  7574,   166,  1087,\n",
-      "        2073,   352,  5428,   968,  4508,   886,  1036,  1576,   257,\n",
-      "         641,   996,    18, 10569,   938,  1011,  3632,   379,   291,\n",
-      "        1691, 11686,  2410,  2287,  7940,  5412,  3712,   982,  1906,\n",
-      "          56,   765,   304,  1058,   389,  2289,   505,  1134,   361,\n",
-      "         499,  6461,   302,   509,  2559,  9886,  2569,  2103,   904,\n",
-      "        5841,  1085,  1224,  6440,   599,  2052,  1726,  1028,  9859,\n",
-      "         587,  1085,  3847,  2242,   564,  1091,  3406,  1884,  2091,\n",
-      "         902,   604,   371,  2631,  1025,  7781,  3646,    16])]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(X_train_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:06:40.924775Z",
@@ -951,13 +756,13 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/5\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - loss: 0.8059 - mae: 0.2812 - mse: 0.1209 - val_loss: 0.6954 - val_mae: 0.2461 - val_mse: 0.0808 - learning_rate: 1.0000e-05\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 141ms/step - loss: 0.7945 - mae: 0.2766 - mse: 0.1126 - val_loss: 0.6952 - val_mae: 0.2494 - val_mse: 0.0808 - learning_rate: 1.0000e-05\n",
       "Epoch 2/5\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.8064 - mae: 0.2828 - mse: 0.1215 - val_loss: 0.6956 - val_mae: 0.2460 - val_mse: 0.0809 - learning_rate: 1.8000e-05\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.7902 - mae: 0.2791 - mse: 0.1153 - val_loss: 0.6954 - val_mae: 0.2494 - val_mse: 0.0809 - learning_rate: 1.8000e-05\n",
       "Epoch 3/5\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.7952 - mae: 0.2716 - mse: 0.1135 - val_loss: 0.6958 - val_mae: 0.2458 - val_mse: 0.0810 - learning_rate: 2.6000e-05\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.7946 - mae: 0.2775 - mse: 0.1151 - val_loss: 0.6955 - val_mae: 0.2494 - val_mse: 0.0809 - learning_rate: 2.6000e-05\n",
       "Epoch 4/5\n",
-      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.7915 - mae: 0.2755 - mse: 0.1127 - val_loss: 0.6960 - val_mae: 0.2456 - val_mse: 0.0811 - learning_rate: 3.4000e-05\n"
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.7835 - mae: 0.2703 - mse: 0.1090 - val_loss: 0.6956 - val_mae: 0.2494 - val_mse: 0.0810 - learning_rate: 3.4000e-05\n"
      ]
     }
    ],

benchmark/tensorflow_5/tensorflow_5_reproduced.ipynb

@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:26:39.148833Z",
@@ -161,25 +161,14 @@
      "shell.execute_reply.started": "2023-12-07T13:26:39.148792Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rating_df['rating'].isna().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T11:29:10.997404Z",
@@ -282,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T11:40:24.564887Z",
@@ -322,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T12:01:00.036863Z",
@@ -345,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T12:01:11.473101Z",
@@ -362,7 +351,7 @@
        "1000"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -373,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:52:39.565889Z",
@@ -428,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T12:39:18.292278Z",
@@ -476,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T12:39:33.627085Z",
@@ -496,7 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:06:05.665173Z",
@@ -658,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:06:21.674206Z",
@@ -717,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:06:27.387608Z",
@@ -727,166 +716,14 @@
      "shell.execute_reply.started": "2023-12-07T13:06:27.387574Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "       319., 126.,  99.,  nan, 622.,  nan, 641., 312.,  nan,  nan, 742.,\n",
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-      "        nan, 207., 802., 342.,  nan,  nan, 895.,  nan,  nan,  nan,  19.,\n",
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-      "        nan,  nan, 413.,  nan,  nan,  nan,  nan, 377.,  nan,  nan,  nan,\n",
-      "       864.,  nan, 724.,  nan,  18.,  nan,  nan,  nan,  nan,  66.,  nan,\n",
-      "       198.,  nan, 476., 498., 346.,  nan, 146.,  nan,  80.,  nan, 424.,\n",
-      "        nan,  nan,  nan, 592.,  nan, 162.,  nan,  nan, 555., 189.,  nan,\n",
-      "        nan,  nan,  nan,  nan,  nan,  nan, 218.,  nan, 716., 752.,  nan,\n",
-      "        nan, 166.,  nan,  nan, 352.,  nan, 968.,  nan, 886.,  nan,  nan,\n",
-      "       257., 641., 996.,  18.,  nan, 938.,  nan,  nan, 379., 291.,  nan,\n",
-      "        nan,  nan,  nan,  nan,  nan,  nan, 982.,  nan,  56., 765., 304.,\n",
-      "        nan, 389.,  nan, 505.,  nan, 361., 499.,  nan, 302., 509.,  nan,\n",
-      "        nan,  nan,  nan, 904.,  nan,  nan,  nan,  nan, 599.,  nan,  nan,\n",
-      "        nan,  nan, 587.,  nan,  nan,  nan, 564.,  nan,  nan,  nan,  nan,\n",
-      "       902., 604., 371.,  nan,  nan,  nan,  nan,  16.])]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(X_train_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-07T13:06:40.924775Z",
@@ -906,15 +743,15 @@
     },
     {
      "ename": "InvalidArgumentError",
-     "evalue": "Graph execution error:\n\nDetected at node functional_1/user_embedding_1/GatherV2 defined at (most recent call last):\n  File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n\n  File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n\n  File \"/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py\", line 37, in <module>\n\n  File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n\n  File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 377, in dispatch_queue\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 250, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 748, in __init__\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n\n  File \"<ipython-input-15-05d1ae813ed0>\", line 2, in <cell line: 2>\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 318, in fit\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 121, in one_step_on_iterator\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 108, in one_step_on_data\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 51, in train_step\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 882, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\", line 175, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/function.py\", line 171, in _run_through_graph\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\", line 556, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 882, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py\", line 140, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/numpy.py\", line 4875, in take\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/numpy.py\", line 1951, in take\n\nindices[0,0] = -2147482648 is not in [0, 1000)\n\t [[{{node functional_1/user_embedding_1/GatherV2}}]] [Op:__inference_one_step_on_iterator_1786]",
+     "evalue": "Graph execution error:\n\nDetected at node functional_1/user_embedding_1/GatherV2 defined at (most recent call last):\n  File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n\n  File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n\n  File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n\n  File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n\n  File \"<ipython-input-13-05d1ae813ed0>\", line 2, in <cell line: 2>\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 318, in fit\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 121, in one_step_on_iterator\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 108, in one_step_on_data\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 51, in train_step\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 882, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\", line 175, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/function.py\", line 171, in _run_through_graph\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\", line 556, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 882, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py\", line 140, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/numpy.py\", line 4875, in take\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/numpy.py\", line 1951, in take\n\nindices[0,0] = -2147482648 is not in [0, 1000)\n\t [[{{node functional_1/user_embedding_1/GatherV2}}]] [Op:__inference_one_step_on_iterator_1786]",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-05d1ae813ed0>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Model training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train_array\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-13-05d1ae813ed0>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Model training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train_array\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     54\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     55\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node functional_1/user_embedding_1/GatherV2 defined at (most recent call last):\n  File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n\n  File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n\n  File \"/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py\", line 37, in <module>\n\n  File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n\n  File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 377, in dispatch_queue\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 250, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 748, in __init__\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n\n  File \"<ipython-input-15-05d1ae813ed0>\", line 2, in <cell line: 2>\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 318, in fit\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 121, in one_step_on_iterator\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 108, in one_step_on_data\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 51, in train_step\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 882, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\", line 175, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/function.py\", line 171, in _run_through_graph\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\", line 556, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 882, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py\", line 140, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/numpy.py\", line 4875, in take\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/numpy.py\", line 1951, in take\n\nindices[0,0] = -2147482648 is not in [0, 1000)\n\t [[{{node functional_1/user_embedding_1/GatherV2}}]] [Op:__inference_one_step_on_iterator_1786]"
+      "\u001b[0;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node functional_1/user_embedding_1/GatherV2 defined at (most recent call last):\n  File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n\n  File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n\n  File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n\n  File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n\n  File \"<ipython-input-13-05d1ae813ed0>\", line 2, in <cell line: 2>\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 318, in fit\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 121, in one_step_on_iterator\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 108, in one_step_on_data\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 51, in train_step\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 882, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\", line 175, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/function.py\", line 171, in _run_through_graph\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py\", line 556, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 882, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py\", line 140, in call\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/ops/numpy.py\", line 4875, in take\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/numpy.py\", line 1951, in take\n\nindices[0,0] = -2147482648 is not in [0, 1000)\n\t [[{{node functional_1/user_embedding_1/GatherV2}}]] [Op:__inference_one_step_on_iterator_1786]"
      ]
     }
    ],

benchmark/tensorflow_5/weights.weights.h5

@@ -1,3 +1,3 @@
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-oid sha256:e6592b5eadc14d9d13150936fbc961aac3d6470ee7a59d7887964c329d761426
+oid sha256:621d9734c16ca5364ea45a0b328157411731a8f49ce1a6e56d90a5297f775b95
 size 80111960

benchmark/tensorflow_6/tensorflow_6_fixed.ipynb

@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:02.453784Z",
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:02.639276Z",
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.360141Z",
@@ -142,7 +142,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.685510Z",
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.868530Z",
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.875409Z",
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.894885Z",
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:19:29.397074Z",
@@ -340,12 +340,20 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "(28, 28, 1)\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n"
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step\n"
      ]
     },
     {
@@ -354,7 +362,7 @@
        "(3, 28, 28, 32)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -455,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:37:06.070632Z",
@@ -526,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:10.434652Z",
@@ -537,13 +545,21 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/base_optimizer.py:33: UserWarning: Argument `decay` is no longer supported and will be ignored.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'\\n“decay=0”是RMSprop优化算法的一个参数，它控制了学习率的衰减。\\n具体来说，在RMSprop算法中，每个权重参数都有自己的学习率\\n，而decay参数会使这个学习率随着时间的推移而逐渐变小。\\n但是，由于decay=0，因此该算法不会在训练过程中降低学习率。\\n\\n“epsilon=1e-08”是RMSprop优化算法的一个参数，它是用来防止除零错误的一个小量，通常取极小的值（例如1e-8）。\\n在RMSprop算法中，计算梯度平方平均值时需要对平方梯度加上一个极小的值，以避免出现除以零的错误。\\n\\n“rho=0.9”是一种优化算法的参数，用于控制梯度的平滑度。具体来说，该参数决定了在计算平方梯度的指数移动平均值时，\\n历史数据的重要性。较高的rho值可以使平均值对历史数据的依赖性更强，从而使整个优化过程更加稳定。\\n'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -567,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:37:31.969911Z",
@@ -585,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:36:01.984363Z",
@@ -602,7 +618,7 @@
        "\"\\n\\n在您提供的代码中，learningratereduction是ReduceLROnPlateau回调函数的一个实例，用于在模型训练过程中动态地减小学习率。\\n具体来说，它监视了验证集的准确性（即'monitor'='valacc'），并且如果在3个时期内没有改进，\\n则减小学习率（即'patience'=3）。该调用还指定了减小因子（即'factor'=0.5）和最小学习率（即'minlr'=0.00001），\\n以便在执行减少操作时进行限制，从而保持学习率的稳定性和有效性。如果您想要更好地了解ReduceLROnPlateau的工作原理和参数设置，\\n\\nverbose是ReduceLROnPlateau回调函数的一个可选参数，用于控制输出详细程度的标志。\\n如果verbose=1，则在执行时期减少操作时将输出一条消息，以指示学习率的更新和当前的状态。\\n如果verbose=0，则不会输出任何消息。通常情况下，verbose的默认值为0，因为它可以大大减少输出的噪声和干扰。\\n如果您需要更详细的输出和信息，可以将verbose的值设置为1或更高\\n可以查看Keras文档：https://keras.io/callbacks/#reducelronplateau。\\n\""
       ]
      },
-     "execution_count": 21,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -633,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:53:09.648082Z",
@@ -656,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:36:05.950296Z",
@@ -723,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T04:01:52.740017Z",
@@ -762,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T04:01:55.664789Z",
@@ -785,7 +801,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "439/439 - 60s - 137ms/step - accuracy: 0.8606 - loss: 0.4346 - val_accuracy: 0.9783 - val_loss: 0.0711 - learning_rate: 0.0010\n"
+      "439/439 - 63s - 144ms/step - accuracy: 0.8645 - loss: 0.4261 - val_accuracy: 0.9736 - val_loss: 0.0943 - learning_rate: 0.0010\n"
      ]
     },
     {
@@ -836,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T04:10:02.887763Z",
@@ -849,7 +865,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]

benchmark/tensorflow_6/tensorflow_6_reproduced.ipynb

@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:02.453784Z",
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:02.639276Z",
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.360141Z",
@@ -142,7 +142,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.685510Z",
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.868530Z",
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.875409Z",
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:07.894885Z",
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:19:29.397074Z",
@@ -345,7 +345,15 @@
      "output_type": "stream",
      "text": [
       "(28, 28, 1)\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n"
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
      ]
     },
     {
@@ -354,7 +362,7 @@
        "(3, 28, 28, 32)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -455,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:37:06.070632Z",
@@ -526,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:43:10.434652Z",
@@ -537,13 +545,21 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/base_optimizer.py:33: UserWarning: Argument `decay` is no longer supported and will be ignored.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'\\n“decay=0”是RMSprop优化算法的一个参数，它控制了学习率的衰减。\\n具体来说，在RMSprop算法中，每个权重参数都有自己的学习率\\n，而decay参数会使这个学习率随着时间的推移而逐渐变小。\\n但是，由于decay=0，因此该算法不会在训练过程中降低学习率。\\n\\n“epsilon=1e-08”是RMSprop优化算法的一个参数，它是用来防止除零错误的一个小量，通常取极小的值（例如1e-8）。\\n在RMSprop算法中，计算梯度平方平均值时需要对平方梯度加上一个极小的值，以避免出现除以零的错误。\\n\\n“rho=0.9”是一种优化算法的参数，用于控制梯度的平滑度。具体来说，该参数决定了在计算平方梯度的指数移动平均值时，\\n历史数据的重要性。较高的rho值可以使平均值对历史数据的依赖性更强，从而使整个优化过程更加稳定。\\n'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -567,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:37:31.969911Z",
@@ -585,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:36:01.984363Z",
@@ -602,7 +618,7 @@
        "\"\\n\\n在您提供的代码中，learningratereduction是ReduceLROnPlateau回调函数的一个实例，用于在模型训练过程中动态地减小学习率。\\n具体来说，它监视了验证集的准确性（即'monitor'='valacc'），并且如果在3个时期内没有改进，\\n则减小学习率（即'patience'=3）。该调用还指定了减小因子（即'factor'=0.5）和最小学习率（即'minlr'=0.00001），\\n以便在执行减少操作时进行限制，从而保持学习率的稳定性和有效性。如果您想要更好地了解ReduceLROnPlateau的工作原理和参数设置，\\n\\nverbose是ReduceLROnPlateau回调函数的一个可选参数，用于控制输出详细程度的标志。\\n如果verbose=1，则在执行时期减少操作时将输出一条消息，以指示学习率的更新和当前的状态。\\n如果verbose=0，则不会输出任何消息。通常情况下，verbose的默认值为0，因为它可以大大减少输出的噪声和干扰。\\n如果您需要更详细的输出和信息，可以将verbose的值设置为1或更高\\n可以查看Keras文档：https://keras.io/callbacks/#reducelronplateau。\\n\""
       ]
      },
-     "execution_count": 21,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -633,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:53:09.648082Z",
@@ -656,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T03:36:05.950296Z",
@@ -723,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T04:01:52.740017Z",
@@ -762,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T04:01:55.664789Z",
@@ -785,7 +801,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "439/439 - 60s - 137ms/step - accuracy: 0.8606 - loss: 0.4346 - val_accuracy: 0.9783 - val_loss: 0.0711 - learning_rate: 0.0010\n"
+      "439/439 - 61s - 138ms/step - accuracy: 0.8586 - loss: 0.4352 - val_accuracy: 0.9767 - val_loss: 0.0695 - learning_rate: 0.0010\n"
      ]
     },
     {
@@ -836,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T04:10:02.887763Z",
@@ -854,7 +870,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-28-352f161fa038>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlegend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'best'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshadow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'acc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Training accuracy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_acc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Validation accuracy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mlegend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'best'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshadow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-18-352f161fa038>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlegend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'best'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshadow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'acc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Training accuracy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_acc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Validation accuracy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mlegend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'best'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshadow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mKeyError\u001b[0m: 'acc'"
      ]
     },

benchmark/tensorflow_7/tensorflow_7_fixed.ipynb

@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:49.850081Z",
@@ -520,107 +520,7 @@
      "shell.execute_reply.started": "2023-01-16T08:00:49.850036Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>id</th>\n",
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-       "      <td>th</td>\n",
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-       "\n",
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-       "0  The rules developed in the interim were put to...       en  English      0  \n",
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-       "2              J'essayais d'accomplir quelque chose.       fr   French      0  \n",
-       "3  They can't defend themselves because of their ...       en  English      0  \n",
-       "4    เด็กสามารถเห็นได้ว่าชาติพันธุ์แตกต่างกันอย่างไร       th     Thai      1  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train.head()"
    ]
@@ -1367,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:50.898473Z",
@@ -1402,7 +1302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:53.424542Z",
@@ -1452,7 +1352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:53.449365Z",
@@ -1537,7 +1437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:53.463417Z",
@@ -1587,7 +1487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:01:06.505373Z",
@@ -1639,7 +1539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:01:06.518092Z",
@@ -1654,7 +1554,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
+      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias']\n",
       "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
       "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
       "All the weights of TFBertModel were initialized from the PyTorch model.\n",
@@ -1714,7 +1614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:01:42.707229Z",
@@ -1731,18 +1631,18 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "1/1 [==============================] - 30s 30s/step - loss: 1.0250 - accuracy: 0.6250 - val_loss: 3.2026 - val_accuracy: 0.0000e+00\n",
+      "1/1 [==============================] - 25s 25s/step - loss: 1.2518 - accuracy: 0.1250 - val_loss: 8.3898 - val_accuracy: 0.5000\n",
       "Epoch 2/2\n",
-      "1/1 [==============================] - 5s 5s/step - loss: 1.3400 - accuracy: 0.2500 - val_loss: 1.5850 - val_accuracy: 0.5000\n"
+      "1/1 [==============================] - 6s 6s/step - loss: 5.4328 - accuracy: 0.6250 - val_loss: 3.6609 - val_accuracy: 0.5000\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tf_keras.src.callbacks.History at 0x7f1ded330460>"
+       "<tf_keras.src.callbacks.History at 0x7632aec30850>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }

benchmark/tensorflow_7/tensorflow_7_reproduced.ipynb

@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:49.850081Z",
@@ -520,107 +520,7 @@
      "shell.execute_reply.started": "2023-01-16T08:00:49.850036Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "      <td>ในการเล่นบทบาทสมมุติก็เช่นกัน โอกาสที่จะได้แสด...</td>\n",
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-       "           id                                            premise  \\\n",
-       "0  5130fd2cb5  and these comments were considered in formulat...   \n",
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-       "3  5622f0c60b  you know they can't really defend themselves l...   \n",
-       "4  86aaa48b45  ในการเล่นบทบาทสมมุติก็เช่นกัน โอกาสที่จะได้แสด...   \n",
-       "\n",
-       "                                          hypothesis lang_abv language  label  \n",
-       "0  The rules developed in the interim were put to...       en  English      0  \n",
-       "1  Practice groups are not permitted to work on t...       en  English      2  \n",
-       "2              J'essayais d'accomplir quelque chose.       fr   French      0  \n",
-       "3  They can't defend themselves because of their ...       en  English      0  \n",
-       "4    เด็กสามารถเห็นได้ว่าชาติพันธุ์แตกต่างกันอย่างไร       th     Thai      1  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train.head()"
    ]
@@ -1367,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:50.898473Z",
@@ -1402,7 +1302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:53.424542Z",
@@ -1452,7 +1352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:53.449365Z",
@@ -1537,7 +1437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:00:53.463417Z",
@@ -1587,7 +1487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:01:06.505373Z",
@@ -1636,7 +1536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:01:06.518092Z",
@@ -1651,7 +1551,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
+      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
       "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
       "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
       "All the weights of TFBertModel were initialized from the PyTorch model.\n",
@@ -1711,7 +1611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-16T08:01:42.707229Z",
@@ -1737,7 +1637,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-c8bc5cc527d1>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-10-c8bc5cc527d1>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tf_keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tf_keras/src/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/tf_keras/src/engine/training.py\", line 1398, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/tf_keras/src/engine/training.py\", line 1381, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/tf_keras/src/engine/training.py\", line 1370, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/tf_keras/src/engine/training.py\", line 1147, in train_step\n        y_pred = self(x, training=True)\n    File \"/usr/local/lib/python3.10/dist-packages/tf_keras/src/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/usr/local/lib/python3.10/dist-packages/tf_keras/src/engine/input_spec.py\", line 298, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"model\" is incompatible with the layer: expected shape=(None, 50), found shape=(None, 172)\n"

benchmark/tensorflow_8/tensorflow_8_fixed.ipynb

@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:38:18.291526Z",
@@ -263,18 +263,7 @@
      "shell.execute_reply.started": "2023-05-07T17:38:18.291485Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "total_images = len(benign_images) + len(malignent_images)\n",
     "total_images"
@@ -282,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:38:20.340832Z",
@@ -312,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T02:23:19.263565Z",
@@ -322,131 +311,14 @@
      "shell.execute_reply.started": "2023-05-07T02:23:19.263525Z"
     }
    },
-   "outputs": [
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-       "      <td>1</td>\n",
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-       "      <th>6</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-001...</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-002...</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-003...</td>\n",
-       "      <td>0</td>\n",
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-       "      <th>9</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-004...</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-014...</td>\n",
-       "      <td>0</td>\n",
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@@ -456,18 +328,7 @@
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-     "metadata": {},
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     "data.shape"
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@@ -481,7 +342,7 @@
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     "count_data"
@@ -513,7 +360,7 @@
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-     "execution_count": 11,
-     "metadata": {},
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    ]
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@@ -683,7 +444,7 @@
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-      "100%|██████████| 12/12 [00:00<00:00, 88.72it/s]"
+      "100%|██████████| 12/12 [00:00<00:00, 71.22it/s]"
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@@ -853,7 +614,7 @@
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@@ -865,7 +626,7 @@
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    "source": [
-    "inputs = Input(shape=(size, size, 1)) # fix the 2nd crash\n",
+    "inputs = Input(shape=(size, size, 1)) # fix --- the crash on \"model.fit\"\n",
     "\n",
     "# initial layers\n",
     "x = Conv2D(filters=64, kernel_size=(3, 3), strides=(2, 2), padding='same', use_bias=False)(inputs)\n",
@@ -925,7 +686,7 @@
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@@ -967,7 +728,7 @@
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@@ -994,9 +755,9 @@
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-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 43s/step - accuracy: 0.4444 - loss: 1.0574 - val_accuracy: 0.5000 - val_loss: 0.6982\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 40s/step - accuracy: 0.4444 - loss: 1.0785 - val_accuracy: 0.5000 - val_loss: 0.7002\n",
       "Epoch 2/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5s/step - accuracy: 0.5556 - loss: 24.1951 - val_accuracy: 0.5000 - val_loss: 0.7029\n"
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6s/step - accuracy: 0.5556 - loss: 35.0594 - val_accuracy: 0.5000 - val_loss: 0.6976\n"
      ]
     }
    ],
@@ -1194,7 +955,7 @@
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    "metadata": {
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@@ -1215,7 +976,8 @@
     }
    ],
    "source": [
-    "y_pred = np.argmax(model.predict(X_val),axis=1) # fix the first/original crash\n",
+    "# y_pred = model.predict_classes(X_val)\n",
+    "y_pred = np.argmax(model.predict(X_val),axis=1) # fix --- the crash (original) on \"model.predict_classes\"\n",
     "acc_test = 0\n",
     "\n",
     "for i in range(X_val.shape[0]):\n",

benchmark/tensorflow_8/tensorflow_8_reproduced.ipynb

@@ -253,7 +253,7 @@
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@@ -263,18 +263,7 @@
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-       "13"
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-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
+   "outputs": [],
    "source": [
     "total_images = len(benign_images) + len(malignent_images)\n",
     "total_images"
@@ -282,7 +271,7 @@
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   {
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@@ -312,7 +301,7 @@
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@@ -481,7 +342,7 @@
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    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "target\n",
-       "0    7\n",
-       "1    6\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "count_data = data[\"target\"].value_counts()\n",
     "count_data"
@@ -513,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:38:33.907990Z",
@@ -533,7 +380,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -548,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:38:37.239633Z",
@@ -558,89 +405,14 @@
      "shell.execute_reply.started": "2023-05-07T17:38:37.239596Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>image</th>\n",
-       "      <th>target</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-016...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-004...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-014...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-016...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-003...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>data/augmented/benign/SOB_B_TA-14-3411F-40-014...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                image target\n",
-       "12  data/augmented/benign/SOB_B_TA-14-3411F-40-016...      0\n",
-       "9   data/augmented/benign/SOB_B_TA-14-3411F-40-004...      0\n",
-       "10  data/augmented/benign/SOB_B_TA-14-3411F-40-014...      0\n",
-       "12  data/augmented/benign/SOB_B_TA-14-3411F-40-016...      0\n",
-       "8   data/augmented/benign/SOB_B_TA-14-3411F-40-003...      0\n",
-       "10  data/augmented/benign/SOB_B_TA-14-3411F-40-014...      0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ben_upsampled.head(10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:38:40.114716Z",
@@ -650,25 +422,14 @@
      "shell.execute_reply.started": "2023-05-07T17:38:40.114679Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(12, 2)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "up_sampled.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:38:42.467607Z",
@@ -683,7 +444,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 12/12 [00:00<00:00, 85.25it/s]"
+      "100%|██████████| 12/12 [00:00<00:00, 38.96it/s]"
      ]
     },
     {
@@ -737,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:42:50.854746Z",
@@ -773,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:42:53.875510Z",
@@ -823,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:42:56.988986Z",
@@ -853,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:43:00.697909Z",
@@ -925,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:43:12.316279Z",
@@ -942,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:43:13.967137Z",
@@ -967,7 +728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T17:43:16.639909Z",
@@ -981,6 +742,19 @@
      "text": [
       "Epoch 1/2\n"
      ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Exception encountered when calling Functional.call().\n\n\u001b[1mInput 0 of layer \"conv2d\" is incompatible with the layer: expected axis -1 of input shape to have value 3, but received input with shape (None, 75, 75, 1)\u001b[0m\n\nArguments received by Functional.call():\n  • inputs=tf.Tensor(shape=(None, 75, 75, 1), dtype=float32)\n  • training=True\n  • mask=None",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-dcd0a9b85680>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m history = model.fit(data_gen.flow(X_train,Y_train,\n\u001b[0m\u001b[1;32m      2\u001b[0m                                        \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                                        \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                                        shuffle=True),\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/layers/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m    225\u001b[0m                     \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 }:\n\u001b[0;32m--> 227\u001b[0;31m                     raise ValueError(\n\u001b[0m\u001b[1;32m    228\u001b[0m                         \u001b[0;34mf'Input {input_index} of layer \"{layer_name}\" is '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m                         \u001b[0;34mf\"incompatible with the layer: expected axis {axis} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Exception encountered when calling Functional.call().\n\n\u001b[1mInput 0 of layer \"conv2d\" is incompatible with the layer: expected axis -1 of input shape to have value 3, but received input with shape (None, 75, 75, 1)\u001b[0m\n\nArguments received by Functional.call():\n  • inputs=tf.Tensor(shape=(None, 75, 75, 1), dtype=float32)\n  • training=True\n  • mask=None"
+     ]
     }
    ],
    "source": [
@@ -1177,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-07T07:56:43.638084Z",
@@ -1195,7 +969,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-19-0c87e05d7114>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0macc_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-16-0c87e05d7114>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0macc_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'predict_classes'"
      ]
     }

benchmark/tensorflow_9/tensorflow_9_fixed.ipynb

@@ -85,43 +85,43 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4107</th>\n",
-       "      <td>5836</td>\n",
-       "      <td>hailstorm</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>@Haley_Whaley Hailstorm Hey There is a Secret ...</td>\n",
+       "      <th>2027</th>\n",
+       "      <td>2910</td>\n",
+       "      <td>danger</td>\n",
+       "      <td>Worldwide</td>\n",
+       "      <td>@DyannBridges @yeshayad Check out this #rockin...</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6771</th>\n",
-       "      <td>9703</td>\n",
-       "      <td>tornado</td>\n",
-       "      <td></td>\n",
-       "      <td>Maybe that's what happens when a tornado meets...</td>\n",
-       "      <td>1</td>\n",
+       "      <th>2784</th>\n",
+       "      <td>4005</td>\n",
+       "      <td>disaster</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>@LovelyLikeLaura I can see why one of your fav...</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6792</th>\n",
-       "      <td>9731</td>\n",
-       "      <td>tragedy</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Rly tragedy in MP: Some live to recount horror...</td>\n",
+       "      <th>4264</th>\n",
+       "      <td>6058</td>\n",
+       "      <td>heat%20wave</td>\n",
+       "      <td>State College, PA</td>\n",
+       "      <td>Must Read Forecast! Longest Streak of Triple-D...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3311</th>\n",
-       "      <td>4744</td>\n",
-       "      <td>evacuate</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>The summer program I worked for went the city ...</td>\n",
+       "      <th>1676</th>\n",
+       "      <td>2419</td>\n",
+       "      <td>collide</td>\n",
+       "      <td>Fort Smith, AR</td>\n",
+       "      <td>Students COLLIDE this Fri/Sat - register http:...</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7536</th>\n",
-       "      <td>10775</td>\n",
-       "      <td>wreckage</td>\n",
-       "      <td>Mumbai</td>\n",
-       "      <td>Wreckage 'Conclusively Confirmed' as From MH37...</td>\n",
+       "      <th>6598</th>\n",
+       "      <td>9450</td>\n",
+       "      <td>terrorism</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>Preacher faces UK terrorism charges http://t.c...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -129,19 +129,19 @@
        "</div>"
       ],
       "text/plain": [
-       "         id    keyword location  \\\n",
-       "4107   5836  hailstorm      USA   \n",
-       "6771   9703    tornado            \n",
-       "6792   9731    tragedy      NaN   \n",
-       "3311   4744   evacuate      NaN   \n",
-       "7536  10775   wreckage   Mumbai   \n",
+       "        id      keyword           location  \\\n",
+       "2027  2910       danger          Worldwide   \n",
+       "2784  4005     disaster                NaN   \n",
+       "4264  6058  heat%20wave  State College, PA   \n",
+       "1676  2419      collide     Fort Smith, AR   \n",
+       "6598  9450    terrorism                USA   \n",
        "\n",
        "                                                   text  target  \n",
-       "4107  @Haley_Whaley Hailstorm Hey There is a Secret ...       0  \n",
-       "6771  Maybe that's what happens when a tornado meets...       1  \n",
-       "6792  Rly tragedy in MP: Some live to recount horror...       1  \n",
-       "3311  The summer program I worked for went the city ...       0  \n",
-       "7536  Wreckage 'Conclusively Confirmed' as From MH37...       1  "
+       "2027  @DyannBridges @yeshayad Check out this #rockin...       0  \n",
+       "2784  @LovelyLikeLaura I can see why one of your fav...       0  \n",
+       "4264  Must Read Forecast! Longest Streak of Triple-D...       1  \n",
+       "1676  Students COLLIDE this Fri/Sat - register http:...       0  \n",
+       "6598  Preacher faces UK terrorism charges http://t.c...       1  "
       ]
      },
      "execution_count": 2,
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-10T04:36:05.785406Z",
@@ -624,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-10T04:36:05.890142Z",
@@ -639,9 +639,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-13-243bff371cf0>:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "<ipython-input-12-243bff371cf0>:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  input_ids = torch.tensor(bert_input.input_ids)\n",
-      "<ipython-input-13-243bff371cf0>:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "<ipython-input-12-243bff371cf0>:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  attention_mask = torch.tensor(bert_input.attention_mask)\n"
      ]
     },
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-10T04:36:54.738639Z",
@@ -714,7 +714,7 @@
        "================================================================================"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }

benchmark/tensorflow_9/tensorflow_9_reproduced.ipynb

@@ -85,63 +85,63 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4107</th>\n",
-       "      <td>5836</td>\n",
-       "      <td>hailstorm</td>\n",
+       "      <th>556</th>\n",
+       "      <td>805</td>\n",
+       "      <td>battle</td>\n",
        "      <td>USA</td>\n",
-       "      <td>@Haley_Whaley Hailstorm Hey There is a Secret ...</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6771</th>\n",
-       "      <td>9703</td>\n",
-       "      <td>tornado</td>\n",
-       "      <td></td>\n",
-       "      <td>Maybe that's what happens when a tornado meets...</td>\n",
+       "      <td>Black Eye 9: A space battle occurred at Star M...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6792</th>\n",
-       "      <td>9731</td>\n",
-       "      <td>tragedy</td>\n",
+       "      <th>244</th>\n",
+       "      <td>347</td>\n",
+       "      <td>annihilation</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Rly tragedy in MP: Some live to recount horror...</td>\n",
-       "      <td>1</td>\n",
+       "      <td>@POTUS Maybe we should call Israel and tell th...</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3311</th>\n",
-       "      <td>4744</td>\n",
-       "      <td>evacuate</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>The summer program I worked for went the city ...</td>\n",
+       "      <th>4655</th>\n",
+       "      <td>6619</td>\n",
+       "      <td>inundated</td>\n",
+       "      <td>Toronto, ON, Canada</td>\n",
+       "      <td>inundated with Westeros Between Storm of Sword...</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7536</th>\n",
-       "      <td>10775</td>\n",
-       "      <td>wreckage</td>\n",
-       "      <td>Mumbai</td>\n",
-       "      <td>Wreckage 'Conclusively Confirmed' as From MH37...</td>\n",
+       "      <th>2453</th>\n",
+       "      <td>3521</td>\n",
+       "      <td>derailment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>@greateranglia I know the cow incident not yr ...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6603</th>\n",
+       "      <td>9455</td>\n",
+       "      <td>terrorism</td>\n",
+       "      <td>Earth 0</td>\n",
+       "      <td>OMEGA MEN Writer Explores Terrorism Religion I...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         id    keyword location  \\\n",
-       "4107   5836  hailstorm      USA   \n",
-       "6771   9703    tornado            \n",
-       "6792   9731    tragedy      NaN   \n",
-       "3311   4744   evacuate      NaN   \n",
-       "7536  10775   wreckage   Mumbai   \n",
+       "        id       keyword             location  \\\n",
+       "556    805        battle                  USA   \n",
+       "244    347  annihilation                  NaN   \n",
+       "4655  6619     inundated  Toronto, ON, Canada   \n",
+       "2453  3521    derailment                  NaN   \n",
+       "6603  9455     terrorism              Earth 0   \n",
        "\n",
        "                                                   text  target  \n",
-       "4107  @Haley_Whaley Hailstorm Hey There is a Secret ...       0  \n",
-       "6771  Maybe that's what happens when a tornado meets...       1  \n",
-       "6792  Rly tragedy in MP: Some live to recount horror...       1  \n",
-       "3311  The summer program I worked for went the city ...       0  \n",
-       "7536  Wreckage 'Conclusively Confirmed' as From MH37...       1  "
+       "556   Black Eye 9: A space battle occurred at Star M...       1  \n",
+       "244   @POTUS Maybe we should call Israel and tell th...       0  \n",
+       "4655  inundated with Westeros Between Storm of Sword...       0  \n",
+       "2453  @greateranglia I know the cow incident not yr ...       1  \n",
+       "6603  OMEGA MEN Writer Explores Terrorism Religion I...       0  "
       ]
      },
      "execution_count": 2,
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-10T04:36:05.785406Z",
@@ -624,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-10T04:36:05.890142Z",
@@ -639,9 +639,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-13-243bff371cf0>:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "<ipython-input-12-243bff371cf0>:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  input_ids = torch.tensor(bert_input.input_ids)\n",
-      "<ipython-input-13-243bff371cf0>:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "<ipython-input-12-243bff371cf0>:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  attention_mask = torch.tensor(bert_input.attention_mask)\n"
      ]
     },
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-10T04:36:54.738639Z",
@@ -684,7 +684,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-a99874e77d9c>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbert_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-13-a99874e77d9c>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbert_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/model_visualization.py\u001b[0m in \u001b[0;36mplot_model\u001b[0;34m(model, to_file, show_shapes, show_dtype, show_layer_names, rankdir, expand_nested, dpi, show_layer_activations, show_trainable, **kwargs)\u001b[0m\n\u001b[1;32m    410\u001b[0m     \"\"\"\n\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m         raise ValueError(\n\u001b[1;32m    414\u001b[0m             \u001b[0;34m\"This model has not yet been built. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1727\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1728\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"'{type(self).__name__}' object has no attribute '{name}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1730\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Module'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mAttributeError\u001b[0m: 'BertModel' object has no attribute 'built'"