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inv_model = keras.Model(inputs=[inputs], outputs=[outputs], name=\"inv_model\") |
# Compile the mode with the necessary loss function and optimizer. |
print(\"compiling the involution model...\") |
inv_model.compile( |
optimizer=\"adam\", |
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), |
metrics=[\"accuracy\"], |
) |
# train the model |
print(\"inv model training...\") |
inv_hist = inv_model.fit(train_ds, epochs=20, validation_data=test_ds) |
building the involution model... |
compiling the involution model... |
inv model training... |
Epoch 1/20 |
196/196 [==============================] - 5s 21ms/step - loss: 2.1570 - accuracy: 0.2266 - val_loss: 2.2712 - val_accuracy: 0.1557 |
Epoch 2/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.9445 - accuracy: 0.3054 - val_loss: 1.9762 - val_accuracy: 0.2963 |
Epoch 3/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.8469 - accuracy: 0.3433 - val_loss: 1.8044 - val_accuracy: 0.3669 |
Epoch 4/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.7837 - accuracy: 0.3646 - val_loss: 1.7640 - val_accuracy: 0.3761 |
Epoch 5/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.7369 - accuracy: 0.3784 - val_loss: 1.7180 - val_accuracy: 0.3907 |
Epoch 6/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.7031 - accuracy: 0.3917 - val_loss: 1.6839 - val_accuracy: 0.4004 |
Epoch 7/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.6748 - accuracy: 0.3988 - val_loss: 1.6786 - val_accuracy: 0.4037 |
Epoch 8/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.6592 - accuracy: 0.4052 - val_loss: 1.6550 - val_accuracy: 0.4103 |
Epoch 9/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.6412 - accuracy: 0.4106 - val_loss: 1.6346 - val_accuracy: 0.4158 |
Epoch 10/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.6251 - accuracy: 0.4178 - val_loss: 1.6330 - val_accuracy: 0.4145 |
Epoch 11/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.6124 - accuracy: 0.4206 - val_loss: 1.6214 - val_accuracy: 0.4218 |
Epoch 12/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.5978 - accuracy: 0.4252 - val_loss: 1.6121 - val_accuracy: 0.4239 |
Epoch 13/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.5868 - accuracy: 0.4301 - val_loss: 1.5974 - val_accuracy: 0.4284 |
Epoch 14/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.5759 - accuracy: 0.4353 - val_loss: 1.5939 - val_accuracy: 0.4325 |
Epoch 15/20 |
196/196 [==============================] - 4s 19ms/step - loss: 1.5677 - accuracy: 0.4369 - val_loss: 1.5889 - val_accuracy: 0.4372 |
Epoch 16/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.5586 - accuracy: 0.4413 - val_loss: 1.5817 - val_accuracy: 0.4376 |
Epoch 17/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.5507 - accuracy: 0.4447 - val_loss: 1.5776 - val_accuracy: 0.4381 |
Epoch 18/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.5420 - accuracy: 0.4477 - val_loss: 1.5785 - val_accuracy: 0.4378 |
Epoch 19/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.5357 - accuracy: 0.4484 - val_loss: 1.5639 - val_accuracy: 0.4431 |
Epoch 20/20 |
196/196 [==============================] - 4s 20ms/step - loss: 1.5305 - accuracy: 0.4530 - val_loss: 1.5661 - val_accuracy: 0.4418 |
Comparisons |
In this section, we will be looking at both the models and compare a few pointers. |
Parameters |
One can see that with a similar architecture the parameters in a CNN is much larger than that of an INN (Involutional Neural Network). |
conv_model.summary() |
inv_model.summary() |
Model: \"sequential_3\" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
conv2d_6 (Conv2D) (None, 32, 32, 32) 896 |
_________________________________________________________________ |
relu1 (ReLU) (None, 32, 32, 32) 0 |
_________________________________________________________________ |
max_pooling2d (MaxPooling2D) (None, 16, 16, 32) 0 |
_________________________________________________________________ |
conv2d_7 (Conv2D) (None, 16, 16, 64) 18496 |
_________________________________________________________________ |
relu2 (ReLU) (None, 16, 16, 64) 0 |
_________________________________________________________________ |
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 64) 0 |
_________________________________________________________________ |
conv2d_8 (Conv2D) (None, 8, 8, 64) 36928 |
_________________________________________________________________ |
relu3 (ReLU) (None, 8, 8, 64) 0 |
_________________________________________________________________ |
flatten (Flatten) (None, 4096) 0 |
_________________________________________________________________ |
dense (Dense) (None, 64) 262208 |
_________________________________________________________________ |
dense_1 (Dense) (None, 10) 650 |
================================================================= |
Total params: 319,178 |
Trainable params: 319,178 |
Non-trainable params: 0 |
_________________________________________________________________ |
Model: \"inv_model\" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
input_1 (InputLayer) [(None, 32, 32, 3)] 0 |
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