Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
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Epoch 13/40
187/187 - 63s - loss: 3.5028 - accuracy: 0.1447 - val_loss: 3.9513 - val_accuracy: 0.0933
Epoch 14/40
187/187 - 63s - loss: 3.4295 - accuracy: 0.1604 - val_loss: 3.7738 - val_accuracy: 0.1220
Epoch 15/40
187/187 - 63s - loss: 3.3410 - accuracy: 0.1735 - val_loss: 3.9104 - val_accuracy: 0.1104
Epoch 16/40
187/187 - 63s - loss: 3.2511 - accuracy: 0.1890 - val_loss: 3.6904 - val_accuracy: 0.1264
Epoch 17/40
187/187 - 63s - loss: 3.1624 - accuracy: 0.2076 - val_loss: 3.4026 - val_accuracy: 0.1769
Epoch 18/40
187/187 - 63s - loss: 3.0825 - accuracy: 0.2229 - val_loss: 3.4627 - val_accuracy: 0.1744
Epoch 19/40
187/187 - 63s - loss: 3.0041 - accuracy: 0.2355 - val_loss: 3.6061 - val_accuracy: 0.1542
Epoch 20/40
187/187 - 64s - loss: 2.8945 - accuracy: 0.2552 - val_loss: 3.2769 - val_accuracy: 0.2036
Epoch 21/40
187/187 - 63s - loss: 2.8054 - accuracy: 0.2710 - val_loss: 3.5355 - val_accuracy: 0.1834
Epoch 22/40
187/187 - 63s - loss: 2.7342 - accuracy: 0.2904 - val_loss: 3.3540 - val_accuracy: 0.1973
Epoch 23/40
187/187 - 62s - loss: 2.6258 - accuracy: 0.3042 - val_loss: 3.2608 - val_accuracy: 0.2217
Epoch 24/40
187/187 - 62s - loss: 2.5453 - accuracy: 0.3218 - val_loss: 3.4611 - val_accuracy: 0.1941
Epoch 25/40
187/187 - 63s - loss: 2.4585 - accuracy: 0.3356 - val_loss: 3.4163 - val_accuracy: 0.2070
Epoch 26/40
187/187 - 62s - loss: 2.3606 - accuracy: 0.3647 - val_loss: 3.2558 - val_accuracy: 0.2392
Epoch 27/40
187/187 - 63s - loss: 2.2819 - accuracy: 0.3801 - val_loss: 3.3676 - val_accuracy: 0.2222
Epoch 28/40
187/187 - 62s - loss: 2.2114 - accuracy: 0.3933 - val_loss: 3.6578 - val_accuracy: 0.2022
Epoch 29/40
187/187 - 62s - loss: 2.0964 - accuracy: 0.4215 - val_loss: 3.5366 - val_accuracy: 0.2186
Epoch 30/40
187/187 - 63s - loss: 1.9931 - accuracy: 0.4459 - val_loss: 3.5612 - val_accuracy: 0.2310
Epoch 31/40
187/187 - 63s - loss: 1.8924 - accuracy: 0.4657 - val_loss: 3.4780 - val_accuracy: 0.2359
Epoch 32/40
187/187 - 63s - loss: 1.8095 - accuracy: 0.4874 - val_loss: 3.5776 - val_accuracy: 0.2403
Epoch 33/40
187/187 - 63s - loss: 1.7126 - accuracy: 0.5086 - val_loss: 3.6865 - val_accuracy: 0.2316
Epoch 34/40
187/187 - 63s - loss: 1.6117 - accuracy: 0.5373 - val_loss: 3.6419 - val_accuracy: 0.2513
Epoch 35/40
187/187 - 63s - loss: 1.5532 - accuracy: 0.5514 - val_loss: 3.8050 - val_accuracy: 0.2415
Epoch 36/40
187/187 - 63s - loss: 1.4479 - accuracy: 0.5809 - val_loss: 4.0113 - val_accuracy: 0.2299
Epoch 37/40
187/187 - 62s - loss: 1.3885 - accuracy: 0.5939 - val_loss: 4.1262 - val_accuracy: 0.2158
Epoch 38/40
187/187 - 63s - loss: 1.2979 - accuracy: 0.6217 - val_loss: 4.2519 - val_accuracy: 0.2344
Epoch 39/40
187/187 - 62s - loss: 1.2066 - accuracy: 0.6413 - val_loss: 4.3924 - val_accuracy: 0.2169
Epoch 40/40
187/187 - 62s - loss: 1.1348 - accuracy: 0.6618 - val_loss: 4.2216 - val_accuracy: 0.2374
Training the model is relatively fast (takes only 20 seconds per epoch on TPUv2 that is available on Colab). This might make it sounds easy to simply train EfficientNet on any dataset wanted from scratch. However, training EfficientNet on smaller datasets, especially those with lower resolution like CIFAR-100, faces th...
Hence training from scratch requires very careful choice of hyperparameters and is difficult to find suitable regularization. It would also be much more demanding in resources. Plotting the training and validation accuracy makes it clear that validation accuracy stagnates at a low value.
import matplotlib.pyplot as plt
def plot_hist(hist):
plt.plot(hist.history[\"accuracy\"])
plt.plot(hist.history[\"val_accuracy\"])
plt.title(\"model accuracy\")
plt.ylabel(\"accuracy\")
plt.xlabel(\"epoch\")
plt.legend([\"train\", \"validation\"], loc=\"upper left\")
plt.show()
plot_hist(hist)
png
Transfer learning from pre-trained weights
Here we initialize the model with pre-trained ImageNet weights, and we fine-tune it on our own dataset.
def build_model(num_classes):
inputs = layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
x = img_augmentation(inputs)
model = EfficientNetB0(include_top=False, input_tensor=x, weights=\"imagenet\")
# Freeze the pretrained weights
model.trainable = False
# Rebuild top
x = layers.GlobalAveragePooling2D(name=\"avg_pool\")(model.output)
x = layers.BatchNormalization()(x)
top_dropout_rate = 0.2
x = layers.Dropout(top_dropout_rate, name=\"top_dropout\")(x)
outputs = layers.Dense(NUM_CLASSES, activation=\"softmax\", name=\"pred\")(x)
# Compile
model = tf.keras.Model(inputs, outputs, name=\"EfficientNet\")
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)
model.compile(
optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]