text stringlengths 0 4.99k |
|---|
plt.xlabel(class_names[train_labels[i][0]]) |
plt.show() |
png |
Convolutional Neural Network |
# Build the conv model. |
print(\"building the convolution model...\") |
conv_model = keras.Sequential( |
[ |
keras.layers.Conv2D(32, (3, 3), input_shape=(32, 32, 3), padding=\"same\"), |
keras.layers.ReLU(name=\"relu1\"), |
keras.layers.MaxPooling2D((2, 2)), |
keras.layers.Conv2D(64, (3, 3), padding=\"same\"), |
keras.layers.ReLU(name=\"relu2\"), |
keras.layers.MaxPooling2D((2, 2)), |
keras.layers.Conv2D(64, (3, 3), padding=\"same\"), |
keras.layers.ReLU(name=\"relu3\"), |
keras.layers.Flatten(), |
keras.layers.Dense(64, activation=\"relu\"), |
keras.layers.Dense(10), |
] |
) |
# Compile the mode with the necessary loss function and optimizer. |
print(\"compiling the convolution model...\") |
conv_model.compile( |
optimizer=\"adam\", |
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), |
metrics=[\"accuracy\"], |
) |
# Train the model. |
print(\"conv model training...\") |
conv_hist = conv_model.fit(train_ds, epochs=20, validation_data=test_ds) |
building the convolution model... |
compiling the convolution model... |
conv model training... |
Epoch 1/20 |
196/196 [==============================] - 16s 16ms/step - loss: 1.6367 - accuracy: 0.4041 - val_loss: 1.3283 - val_accuracy: 0.5275 |
Epoch 2/20 |
196/196 [==============================] - 3s 16ms/step - loss: 1.2207 - accuracy: 0.5675 - val_loss: 1.1365 - val_accuracy: 0.5965 |
Epoch 3/20 |
196/196 [==============================] - 3s 16ms/step - loss: 1.0649 - accuracy: 0.6267 - val_loss: 1.0219 - val_accuracy: 0.6378 |
Epoch 4/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.9642 - accuracy: 0.6613 - val_loss: 0.9741 - val_accuracy: 0.6601 |
Epoch 5/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.8779 - accuracy: 0.6939 - val_loss: 0.9145 - val_accuracy: 0.6826 |
Epoch 6/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.8126 - accuracy: 0.7180 - val_loss: 0.8841 - val_accuracy: 0.6913 |
Epoch 7/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.7641 - accuracy: 0.7334 - val_loss: 0.8667 - val_accuracy: 0.7049 |
Epoch 8/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.7210 - accuracy: 0.7503 - val_loss: 0.8363 - val_accuracy: 0.7089 |
Epoch 9/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.6796 - accuracy: 0.7630 - val_loss: 0.8150 - val_accuracy: 0.7203 |
Epoch 10/20 |
196/196 [==============================] - 3s 15ms/step - loss: 0.6370 - accuracy: 0.7793 - val_loss: 0.9021 - val_accuracy: 0.6964 |
Epoch 11/20 |
196/196 [==============================] - 3s 15ms/step - loss: 0.6089 - accuracy: 0.7886 - val_loss: 0.8336 - val_accuracy: 0.7207 |
Epoch 12/20 |
196/196 [==============================] - 3s 15ms/step - loss: 0.5723 - accuracy: 0.8022 - val_loss: 0.8326 - val_accuracy: 0.7246 |
Epoch 13/20 |
196/196 [==============================] - 3s 15ms/step - loss: 0.5375 - accuracy: 0.8144 - val_loss: 0.8482 - val_accuracy: 0.7223 |
Epoch 14/20 |
196/196 [==============================] - 3s 15ms/step - loss: 0.5121 - accuracy: 0.8230 - val_loss: 0.8244 - val_accuracy: 0.7306 |
Epoch 15/20 |
196/196 [==============================] - 3s 15ms/step - loss: 0.4786 - accuracy: 0.8363 - val_loss: 0.8313 - val_accuracy: 0.7363 |
Epoch 16/20 |
196/196 [==============================] - 3s 15ms/step - loss: 0.4518 - accuracy: 0.8458 - val_loss: 0.8634 - val_accuracy: 0.7293 |
Epoch 17/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.4403 - accuracy: 0.8489 - val_loss: 0.8683 - val_accuracy: 0.7290 |
Epoch 18/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.4094 - accuracy: 0.8576 - val_loss: 0.8982 - val_accuracy: 0.7272 |
Epoch 19/20 |
196/196 [==============================] - 3s 16ms/step - loss: 0.3941 - accuracy: 0.8630 - val_loss: 0.9537 - val_accuracy: 0.7200 |
Epoch 20/20 |
196/196 [==============================] - 3s 15ms/step - loss: 0.3778 - accuracy: 0.8691 - val_loss: 0.9780 - val_accuracy: 0.7184 |
Involutional Neural Network |
# Build the involution model. |
print(\"building the involution model...\") |
inputs = keras.Input(shape=(32, 32, 3)) |
x, _ = Involution( |
channel=3, group_number=1, kernel_size=3, stride=1, reduction_ratio=2, name=\"inv_1\" |
)(inputs) |
x = keras.layers.ReLU()(x) |
x = keras.layers.MaxPooling2D((2, 2))(x) |
x, _ = Involution( |
channel=3, group_number=1, kernel_size=3, stride=1, reduction_ratio=2, name=\"inv_2\" |
)(x) |
x = keras.layers.ReLU()(x) |
x = keras.layers.MaxPooling2D((2, 2))(x) |
x, _ = Involution( |
channel=3, group_number=1, kernel_size=3, stride=1, reduction_ratio=2, name=\"inv_3\" |
)(x) |
x = keras.layers.ReLU()(x) |
x = keras.layers.Flatten()(x) |
x = keras.layers.Dense(64, activation=\"relu\")(x) |
outputs = keras.layers.Dense(10)(x) |
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