dcgan.py 文件源码

def __call__(self):
        model = Sequential()
        model.add(Reshape((28, 28, 1), input_shape=(784,)))
        # Convolution Layer 1
        model.add(Conv2D(64, kernel_size=(4, 4), strides=(2, 2), \
            kernel_initializer=self.initializer))
        model.add(LeakyReLU())

        # Convolution Layer 2
        model.add(Conv2D(128, kernel_size=(4, 4), strides=(2, 2), \
            kernel_initializer=self.initializer))
        model.add(LeakyReLU())

        # Batch Normalization
        model.add(BatchNormalization())

        # Flatten the input
        model.add(Flatten())

        # Dense Layer
        model.add(Dense(1024, kernel_initializer=self.initializer))
        model.add(LeakyReLU())

        # Batch Normalization
        model.add(BatchNormalization())

        # To the output that has two classes
        model.add(Dense(2, activation='softmax'))

        return model