Datasets:
HenryAI
/
KerasCodeExamples.txt

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4.99k
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 3136) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 3136) 0
_________________________________________________________________
dense (Dense) (None, 512) 1606144
_________________________________________________________________
dense_1 (Dense) (None, 1) 513
=================================================================
Total params: 1,704,097
Trainable params: 1,704,097
Non-trainable params: 0
_________________________________________________________________
We also save the history since we later want to compare our model trained with and not trained with Gradient Centralization
history_no_gc = model.fit(
train_ds, epochs=10, verbose=1, callbacks=[time_callback_no_gc]
)
Epoch 1/10
9/9 [==============================] - 5s 571ms/step - loss: 0.7427 - accuracy: 0.5073
Epoch 2/10
9/9 [==============================] - 6s 667ms/step - loss: 0.6757 - accuracy: 0.5433
Epoch 3/10
9/9 [==============================] - 6s 660ms/step - loss: 0.6616 - accuracy: 0.6144
Epoch 4/10
9/9 [==============================] - 6s 642ms/step - loss: 0.6598 - accuracy: 0.6203
Epoch 5/10
9/9 [==============================] - 6s 666ms/step - loss: 0.6782 - accuracy: 0.6329
Epoch 6/10
9/9 [==============================] - 6s 655ms/step - loss: 0.6550 - accuracy: 0.6524
Epoch 7/10
9/9 [==============================] - 6s 645ms/step - loss: 0.6157 - accuracy: 0.7186
Epoch 8/10
9/9 [==============================] - 6s 654ms/step - loss: 0.6095 - accuracy: 0.6913
Epoch 9/10
9/9 [==============================] - 6s 677ms/step - loss: 0.5880 - accuracy: 0.7147
Epoch 10/10
9/9 [==============================] - 6s 663ms/step - loss: 0.5814 - accuracy: 0.6933
Train the model with GC
We will now train the same model, this time using Gradient Centralization, notice our optimizer is the one using Gradient Centralization this time.
time_callback_gc = TimeHistory()
model.compile(loss=\"binary_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])
model.summary()
history_gc = model.fit(train_ds, epochs=10, verbose=1, callbacks=[time_callback_gc])
Model: \"sequential_1\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 298, 298, 16) 448
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 149, 149, 16) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 147, 147, 32) 4640
_________________________________________________________________
dropout (Dropout) (None, 147, 147, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 73, 73, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 71, 71, 64) 18496
_________________________________________________________________
dropout_1 (Dropout) (None, 71, 71, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 33, 33, 64) 36928
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 14, 14, 64) 36928
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 3136) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 3136) 0
_________________________________________________________________
dense (Dense) (None, 512) 1606144
_________________________________________________________________
dense_1 (Dense) (None, 1) 513
=================================================================
Total params: 1,704,097
Trainable params: 1,704,097
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
9/9 [==============================] - 6s 673ms/step - loss: 0.6022 - accuracy: 0.7147
Epoch 2/10
9/9 [==============================] - 6s 662ms/step - loss: 0.5385 - accuracy: 0.7371
Epoch 3/10
9/9 [==============================] - 6s 673ms/step - loss: 0.4832 - accuracy: 0.7945
Epoch 4/10
9/9 [==============================] - 6s 645ms/step - loss: 0.4692 - accuracy: 0.7799
Epoch 5/10
9/9 [==============================] - 6s 720ms/step - loss: 0.4792 - accuracy: 0.7799
Epoch 6/10
9/9 [==============================] - 6s 658ms/step - loss: 0.4623 - accuracy: 0.7838