models.py 文件源码

def append_gan_network(self, true_X_input):

        # Normalize the inputs via custom VGG Normalization layer
        x = Normalize(type="gan", value=127.5, name="gan_normalize")(true_X_input)

        x = Convolution2D(64, self.k, self.k, border_mode='same', name='gan_conv1_1')(x)
        x = LeakyReLU(0.3, name="gan_lrelu1_1")(x)

        x = Convolution2D(64, self.k, self.k, border_mode='same', name='gan_conv1_2', subsample=(2, 2))(x)
        x = LeakyReLU(0.3, name='gan_lrelu1_2')(x)
        x = BatchNormalization(mode=self.mode, axis=channel_axis, name='gan_batchnorm1_1')(x)

        filters = [128, 256] if self.small_model else [128, 256, 512]

        for i, nb_filters in enumerate(filters):
            for j in range(2):
                subsample = (2, 2) if j == 1 else (1, 1)

                x = Convolution2D(nb_filters, self.k, self.k, border_mode='same', subsample=subsample,
                                  name='gan_conv%d_%d' % (i + 2, j + 1))(x)
                x = LeakyReLU(0.3, name='gan_lrelu_%d_%d' % (i + 2, j + 1))(x)
                x = BatchNormalization(mode=self.mode, axis=channel_axis, name='gan_batchnorm%d_%d' % (i + 2, j + 1))(x)

        x = Flatten(name='gan_flatten')(x)

        output_dim = 128 if self.small_model else 1024

        x = Dense(output_dim, name='gan_dense1')(x)
        x = LeakyReLU(0.3, name='gan_lrelu5')(x)

        gan_regulrizer = AdversarialLossRegularizer(weight=self.adversarial_loss_weight)
        x = Dense(2, activation="softmax", activity_regularizer=gan_regulrizer, name='gan_output')(x)

        return x