必备！人工智能和数据科学的七大 Python 库(15)_图1

activation='relu',

input_shape=input_shape))

model.add(Conv2D(64, (3,3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,

optimizer=keras.optimizers.Adadelta(),

metrics=['accuracy'])

model.fit(x_train, y_train,

batch_size=batch_size,

epochs=epochs,

必备！人工智能和数据科学的七大 Python 库(15)