from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense, Conv2D, Flatten | |
from tensorflow.keras import optimizers | |
model = Sequential() | |
model.add(Conv2D(64, kernel_size=(3,3), activation='relu', input_shape=train_images[0].shape)) | |
model.add(Conv2D(32, kernel_size=(3,3), activation='relu')) | |
model.add(Conv2D(32, kernel_size=(3,3), activation='relu')) | |
model.add(Flatten()) | |
model.add(Dense(10, activation='softmax')) | |
adam = optimizers.Adam(lr=0.001) | |
model.compile( | |
optimizer=adam, | |
loss='categorical_crossentropy', | |
metrics=['accuracy'] | |
) | |
model.fit( | |
train_images, | |
train_labels, | |
validation_data=(test_images, test_labels), | |
epochs=5, | |
batch_size=256 | |
) |
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