在GAN的实现中，我对.trainable的{{1}}语句感到困惑。
给出以下代码片段（摘自this repo）：
tf.keras.model在模型class GAN():
def __init__(self):
...
# Build and compile the discriminator
self.discriminator = self.build_discriminator()
self.discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# Build the generator
self.generator = self.build_generator()
# The generator takes noise as input and generates imgs
z = Input(shape=(self.latent_dim,))
img = self.generator(z)
# For the combined model we will only train the generator
self.discriminator.trainable = False
# The discriminator takes generated images as input and determines validity
validity = self.discriminator(img)
# The combined model (stacked generator and discriminator)
# Trains the generator to fool the discriminator
self.combined = Model(z, validity)
self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
def build_generator(self):
...
return Model(noise, img)
def build_discriminator(self):
...
return Model(img, validity)
def train(self, epochs, batch_size=128, sample_interval=50):
# Load the dataset
(X_train, _), (_, _) = mnist.load_data()
# Adversarial ground truths
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(epochs):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
# Generate a batch of new images
gen_imgs = self.generator.predict(noise)
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs, valid)
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
# Train the generator (to have the discriminator label samples as valid)
g_loss = self.combined.train_on_batch(noise, valid)
的定义期间，鉴别器的权重设置为self.combined，但从不打开。
仍然，在训练循环中，鉴别器的权重将随行而变化：
self.discriminator.trainable = False，并在以下期间保持不变：
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs, valid)
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
我没想到。
当然，这是训练GAN的正确（迭代）方式，但是我不明白为什么我们不必先通过# Train the generator (to have the discriminator label samples as valid)
g_loss = self.combined.train_on_batch(noise, valid)
就能对鉴别器进行一些训练。
如果有人对此做出解释，我想这是理解的关键。
答案 0 :(得分：1)
当您对github存储库中的代码有疑问时，通常最好检查一下问题（打开和关闭）。 This issue解释了为什么将标志设置为False。它说，
由于
self.discriminator.trainable = False是在鉴别符编译后设置的，因此不会影响鉴别符的训练。但是，由于它是在组合模型编译之前设置的，因此在训练组合模型时将冻结区分层。