批量规范化在Tensorflow 2.0中没有渐变吗？

Question

我正在尝试制作一个简单的GAN，以从MNIST数据集生成数字。但是，当我开始接受培训时（这是自定义的），我会得到这个烦人的警告，我怀疑这是我不习惯的原因造成的。

请记住，使用默认的急切执行，这一切都在tensorflow 2.0中。

获取数据（不是那么重要）

(train_images,train_labels),(test_images,test_labels) = tf.keras.datasets.mnist.load_data()

train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]

BUFFER_SIZE = 60000
BATCH_SIZE = 256

train_dataset = tf.data.Dataset.from_tensor_slices((train_images,train_labels)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

生成器模型（这是批处理归一化的位置）

def make_generator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Reshape((7, 7, 256)))
    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size

    model.add(tf.keras.layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 7, 7, 128)  
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 14, 14, 64)    
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 28, 28, 1)

    return model

判别器模型（可能不那么重要）

def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Conv2D(64, (5, 5), strides=(2, 2),    padding='same'))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dropout(0.3))

    model.add(tf.keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dropout(0.3))

    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(1))

    return model

解释模型（可能不那么重要）

generator = make_generator_model()
discriminator = make_discriminator_model()

定义损失（也许发电机损耗很重要，因为这是梯度的来源）

def generator_loss(generated_output):
    return tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.ones_like(generated_output), logits = generated_output)


def discriminator_loss(real_output, generated_output):
    # [1,1,...,1] with real output since it is true and we want our generated examples to look like it
    real_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(real_output), logits=real_output)

    # [0,0,...,0] with generated images since they are fake
    generated_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(generated_output), logits=generated_output)

    total_loss = real_loss + generated_loss

    return total_loss

进行优化（可能不重要）

generator_optimizer = tf.optimizers.Adam(1e-4)
discriminator_optimizer = tf.optimizers.Adam(1e-4)

发电机的随机噪声（可能不重要）

EPOCHS = 50
noise_dim = 100
num_examples_to_generate = 16

# We'll re-use this random vector used to seed the generator so
# it will be easier to see the improvement over time.
random_vector_for_generation = tf.random.normal([num_examples_to_generate,
                                                 noise_dim])

单步训练（这是我得到错误的地方

def train_step(images):
   # generating noise from a normal distribution
    noise = tf.random.normal([BATCH_SIZE, noise_dim])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)
        real_output = discriminator(images[0], training=True)
        generated_output = discriminator(generated_images, training=True)

        gen_loss = generator_loss(generated_output)
        disc_loss = discriminator_loss(real_output, generated_output)

This line >>>>>

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.variables)

<<<<< This line 

    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.variables))

完整火车（不重要，只不过它叫train_step）

def train(dataset, epochs):  
    for epoch in range(epochs):
        start = time.time()

        for images in dataset:
            train_step(images)

        display.clear_output(wait=True)
        generate_and_save_images(generator,
                                   epoch + 1,
                                   random_vector_for_generation)

        # saving (checkpoint) the model every 15 epochs
        if (epoch + 1) % 15 == 0:
            checkpoint.save(file_prefix = checkpoint_prefix)

        print ('Time taken for epoch {} is {} sec'.format(epoch + 1,
                                                      time.time()-start))
    # generating after the final epoch
    display.clear_output(wait=True)
    generate_and_save_images(generator,
                           epochs,
                           random_vector_for_generation)

开始培训

train(train_dataset, EPOCHS)

我得到的错误如下，

W0330 19:42:57.366302 4738405824 optimizer_v2.py:928] Gradients does 
not exist for variables ['batch_normalization_v2_54/moving_mean:0', 
'batch_normalization_v2_54/moving_variance:0', 
'batch_normalization_v2_55/moving_mean:0', 
'batch_normalization_v2_55/moving_variance:0', 
'batch_normalization_v2_56/moving_mean:0', 
'batch_normalization_v2_56/moving_variance:0'] when minimizing the
 loss.

我从生成器中获得了一个图像，如下所示：

如果没有规范化，这是我所期望的。一切都会集结到一个角落，因为存在极端价值。

Answer 1

问题在这里：

gradients_of_generator = gen_tape.gradient(gen_loss, generator.variables)

您应该只获得可训练变量的梯度。所以你应该把它改成

gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)

接下来的三行也是如此。该variables字段包括诸如推论期间的移动平均值批处理规范之类的内容。由于在训练期间未使用它们，因此没有定义任何合理的梯度，尝试计算它们将导致崩溃。