ValueError：没有为任何变量提供梯度 - GAN 生成器模型中没有梯度

Question

我目前正在编写 GAN 来生成序列。当独立训练时，生成器和鉴别器都在工作。一旦我将两者结合到完整的 GAN 模型（用冻结的鉴别器权重来训练生成器），就会出现以下错误，并且生成器和鉴别器之间的图似乎没有连接。

ValueError：没有为任何变量提供梯度：['generator_lstm/kernel:0', 'generator_lstm/recurrent_kernel:0', 'generator_lstm/bias:0', 'generator_softmax/kernel:0', 'generator_softmax/bias:0' ].

起初我以为是我的自定义激活函数导致了问题。但由于它独立工作，我认为这两个“子模型”没有正确连接。我不确定它是否重要，但在张量板图中，两个模型之间没有联系。

错误正好发生在train()函数的最后一行。 我已经尝试过 TF 2.1 和 2.4.1 版本，没有什么区别。

# softargmax and build[...]() functions are located in my "gan" python module
# custom softargmax implementation 
@tf.function
def softargmax(values, beta = 1000000.0):

    # tf.range over all possible indices
    range_tensor = tf.range(54, dtype=tf.float32)
    range_tensor = tf.reshape(range_tensor, [-1, 1])

    # softmax activation of (input*beta)
    values = tf.cast(values, dtype=tf.float32)
    beta = tf.cast(beta, dtype=tf.float32)
    softmax = tf.nn.softmax(((values*beta) - tf.reduce_max(values*beta)))
    return softmax @ range_tensor

callable_softargmax = tf.function(softargmax)
get_custom_objects().update({'custom_activation': Activation(callable_softargmax)})

def build_generator(z_dim, seq_len, num_of_words):

    gen_input = Input(shape=(z_dim,), name="generator_input")
    gen_repeat = RepeatVector(seq_len, name="generator_repeat")(gen_input)
    gen_lstm = LSTM(128, activation="relu", return_sequences=True, name="generator_lstm")(gen_repeat)
    gen_softmax = Dense(num_of_words, name="generator_softmax")(gen_lstm)
    #gen_activation = tf.keras.layers.Activation(callable_softargmax)(gen_softmax)
    gen_soft_argmax = Lambda(callable_softargmax, name="generator_soft_argmax")(gen_softmax)
    generator = Model(gen_input, gen_soft_argmax, name="generator_model")
    generator.summary()
    return generator


def build_discriminator(seq_len, num_of_words, embedding_len):

    embedding = np.load(PATH + MODELS + "embedding_ae.npy")
    discriminator = Sequential(name="gan_discriminator")
    discriminator.add(tf.keras.layers.InputLayer(input_shape=(seq_len,1), name="discriminator_input"))
    discriminator.add(Reshape(target_shape=[18,], dtype=tf.float32, name="discriminator_reshape"))
    discriminator.add(Embedding(input_dim=num_of_words, output_dim=embedding_len, input_length=seq_len, mask_zero=False,
                         embeddings_initializer=tf.keras.initializers.Constant(embedding), trainable=False, name="discriminator_emb"))
    discriminator.add(Bidirectional(LSTM(128, activation="tanh", recurrent_activation="sigmoid", recurrent_dropout=0, unroll=False, use_bias=True,
                                         return_sequences=True), name="discriminator_lstm"))
    discriminator.add(Dropout(0.2, name="discriminator_dropout"))
    discriminator.add(LSTM(128, activation="tanh", recurrent_activation="sigmoid", recurrent_dropout=0, unroll=False, use_bias=True,
                           name="discriminator_lstm2"))
    discriminator.add(Dropout(0.2, name="discriminator_dropout2"))
    discriminator.add(Dense(1, activation="sigmoid", name="discriminator_output"))
    discriminator.summary()

    return discriminator


def build_gan(generator, discriminator):
    gan = Sequential(name="gan")
    gan.add(generator)
    gan.add(discriminator)

    return gan

def train(train_data, generator, discriminator, gan, iterations, batch_size, z_dim):
   
    real = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for iteration in range(iterations):
        idx = np.random.randint(0, train_data.shape[0], batch_size)
        train_samples = train_data[idx]
        train_samples = np.reshape(train_samples, [batch_size, 18, 1])

        # train discriminator
        z = np.random.normal(0, 1, (batch_size, z_dim))
        z = np.reshape(z, [batch_size, z_dim])
        gen_samples = generator.predict(z)

        d_loss_real = discriminator.train_on_batch(train_samples, real)
        d_loss_fake = discriminator.train_on_batch(gen_samples, fake)
        d_loss, accuracy, = 0.5 * np.add(d_loss_real, d_loss_fake)

        # train generator
        z = np.random.normal(0, 1, (batch_size, z_dim))
        gen_samples = generator.predict(z)
        g_loss = gan.train_on_batch(z, real)

# compiling and running models in main.py
discriminator = gan.build_discriminator(seq_len=18, num_of_words=54, embedding_len=200)
discriminator.compile(loss="binary_crossentropy", optimizer=tf.keras.optimizers.Adam(lr=0.001), metrics=["accuracy"])
discriminator.trainable = False
    
generator = gan.build_generator(z_dim, seq_len=18, num_of_words=54)


gan_model = gan.build_gan(generator, discriminator)
gan_model.compile(loss="binary_crossentropy", optimizer=tf.keras.optimizers.Adam(lr=0.001))

gan.train(train_data=train, generator=generator, discriminator=discriminator,
          gan=gan_model, iterations=iterations, batch_size=batch_size, z_dim=z_dim)


Model: "gan_discriminator"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
discriminator_reshape (Resha (None, 18)                0         
_________________________________________________________________
discriminator_emb (Embedding (None, 18, 200)           10800     
_________________________________________________________________
discriminator_lstm (Bidirect (None, 18, 256)           336896    
_________________________________________________________________
discriminator_dropout (Dropo (None, 18, 256)           0         
_________________________________________________________________
discriminator_lstm2 (LSTM)   (None, 128)               197120    
_________________________________________________________________
discriminator_dropout2 (Drop (None, 128)               0         
_________________________________________________________________
discriminator_output (Dense) (None, 1)                 129       
=================================================================
Total params: 544,945
Trainable params: 534,145
Non-trainable params: 10,800
_________________________________________________________________
Model: "generator_model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
generator_input (InputLayer) [(None, 128)]             0         
_________________________________________________________________
generator_repeat (RepeatVect (None, 18, 128)           0         
_________________________________________________________________
generator_lstm (LSTM)        (None, 18, 128)           131584    
_________________________________________________________________
generator_softmax (Dense)    (None, 18, 54)            6966      
_________________________________________________________________
generator_soft_argmax (Lambd (None, 18, 1)             0         
=================================================================
Total params: 138,550
Trainable params: 138,550
Non-trainable params: 0

_________________________________________________________________
Model: "gan"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
generator_model (Model)      (None, 18, 1)             138550    
_________________________________________________________________
gan_discriminator (Sequentia (None, 1)                 544945    
=================================================================
Total params: 683,495
Trainable params: 138,550
Non-trainable params: 544,945
_________________________________________________________________

您对模型有什么建议以及可能存在的问题吗？

Answer 1

你找到解决办法了吗？我也在努力使用 GAN 模型生成序列并面临这个错误