Question

我正在使用此代码来训练DCGAN模型以生成图像。 DCGAN

我希望能够以比训练模型更高的分辨率输出（预测）图像，并且我被告知如果使用卷积，这是可能的。在以下描述生成器模型的代码中，如果我更改Conv2DTranspose图层的跨度，则可以增加输出形状（因此图像分辨率）。在训练过程中如何将步幅设置为与预测不同的值？还是有其他方法可以做我想做的事？

# Creates the generator model. This model has an input of random noise and
# generates an image that will try mislead the discriminator.

def construct_generator():
generator = Sequential()

generator.add(Dense(units=4 * 4 * 512,
                    kernel_initializer='glorot_uniform',
                    input_shape=(1, 1, 100)))
generator.add(Reshape(target_shape=(4, 4, 512)))
generator.add(BatchNormalization(momentum=0.5))
generator.add(Activation('relu'))

generator.add(Conv2DTranspose(filters=256, kernel_size=(5, 5),
                              strides=(2, 2), padding='same',
                              data_format='channels_last',
                              kernel_initializer='glorot_uniform'))
generator.add(BatchNormalization(momentum=0.5))
generator.add(Activation('relu'))

generator.add(Conv2DTranspose(filters=128, kernel_size=(5, 5),
                              strides=(2, 2), padding='same',
                              data_format='channels_last',
                              kernel_initializer='glorot_uniform'))
generator.add(BatchNormalization(momentum=0.5))
generator.add(Activation('relu'))

generator.add(Conv2DTranspose(filters=64, kernel_size=(5, 5),
                              strides=(2, 2), padding='same',
                              data_format='channels_last',
                              kernel_initializer='glorot_uniform'))
generator.add(BatchNormalization(momentum=0.5))
generator.add(Activation('relu'))

generator.add(Conv2DTranspose(filters=3, kernel_size=(5, 5),
                              strides=(2, 2), padding='same',
                              data_format='channels_last',
                              kernel_initializer='glorot_uniform'))
generator.add(Activation('tanh'))

optimizer = Adam(lr=0.00015, beta_1=0.5)
generator.compile(loss='binary_crossentropy',
                  optimizer=optimizer,
                  metrics=None)

return generator

Keras和Tensorflow-图层参数作为变量在预测期间要更改

0 个答案: