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Я пытаюсь запустить состязательный автоэнкодер, используя метод keras Fit в классе keras.model, но по какой-то причине он не работает.

Имейте в виду, что я пытался обновить кодер и декодер одновременно. Я попытался передать потерю диска кодировщику с потерей восстановления и без нее

Потери при восстановлении остались прежними, в то время как потери на диске кодера продолжали увеличиваться по мере того, как собственные потери дискриминатора продолжали снижаться.

 discriminator = keras.Sequential(  [  keras.Input(shape=(4, 4, 128)),  layers.Flatten(),  layers.Dense(128, activation="relu"),  layers.Dense(128, activation="relu"),  layers.Dense(128, activation="relu"),  layers.Dense(1, activation="sigmoid"),  ],  name="discriminator", ) discriminator.summary() encoder = keras.Sequential(  [  keras.Input(shape=(28, 28, 1)),  layers.Conv2D(24, 3, activation="relu", strides=2, padding="same"),  layers.Conv2D(48, 3, activation="relu", strides=2, padding="same"),  layers.Conv2D(96, 3, activation="relu", strides=2, padding="same"),  layers.Flatten(),  layers.Dense(4 * 4 * 128, activation="linear"),  layers.Reshape((4, 4, 128)),  ],  name="encoder", ) encoder.summary()  decoder = keras.Sequential(  [  keras.Input(shape=(4, 4, 128)),  layers.Flatten(),  layers.Dense(7 * 7 * 64, activation="relu"),  layers.Reshape((7, 7, 64)),  layers.Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same"),  layers.Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same"),  layers.Conv2DTranspose(1, 3, activation="sigmoid", strides=1, padding="same"),   ],  name="decoder", )  

Я не уверен, что в самой модели этого нет. Для этого я использую набор данных MNIST

 class AAE(keras.Model):  def __init__(self, encoder, decoder, discriminator):  super(AAE, self).__init__()  self.encoder = encoder  self.decoder = decoder  self.discriminator = discriminator   self.total_loss_tracker = keras.metrics.Mean(name="total_loss")  self.reconstruction_loss_tracker = keras.metrics.Mean(name="reconstruction_loss")  self.disc_tracker = keras.metrics.Mean(name="disc_loss")  self.discEnc_tracker = keras.metrics.Mean(name="discEnc_loss")    @property  def metrics(self):  return [  self.total_loss_tracker,  self.reconstruction_loss_tracker,  self.disc_tracker,  self.discEnc_tracker,  ]    def compile(self, di_optimizer, e_optimizer,de_optimizer, loss_fn):  super(AAE, self).compile()  self.dis_optimizer = di_optimizer  self.e_optimizer = e_optimizer  self.de_optimizer = de_optimizer    self.lossBCE = loss_fn[0]  self.lossMAE = loss_fn[1]     def train_step(self, data):  latent = self.encoder(data)  batch_size = 200  dists = tf.random.normal((batch_size,4,4,128))   y_real = tf.ones((batch_size, 1))  y_fake = tf.zeros((batch_size, 1))  real_dist_mix = tf.concat((dists, latent),axis=0)  y_real_fake_mix = tf.concat((y_real, y_fake),axis=0)   with tf.GradientTape() as tape:  predictions = self.discriminator(real_dist_mix)    d_loss = self.lossBCE(y_real_fake_mix, predictions)    grads = tape.gradient(d_loss, self.discriminator.trainable_weights)  self.dis_optimizer.apply_gradients(zip(grads, self.discriminator.trainable_weights))   with tf.GradientTape() as Etape, tf.GradientTape() as Dtape:  latent = self.encoder(data)  reconstruction = self.decoder(latent)  reconstruction_loss = self.lossMAE(data, reconstruction)      total_loss = reconstruction_loss  Egrads = Etape.gradient(total_loss, self.encoder.trainable_weights)  self.e_optimizer.apply_gradients(zip(Egrads, self.encoder.trainable_weights))   Dgrads = Dtape.gradient(total_loss, self.decoder.trainable_weights)  self.de_optimizer.apply_gradients(zip(Dgrads, self.decoder.trainable_weights))    with tf.GradientTape() as tape:  latent = self.encoder(data)  predictions = self.discriminator(latent)  e_loss = self.lossBCE(y_fake, predictions)   grads = tape.gradient(e_loss, self.encoder.trainable_weights)  self.e_optimizer.apply_gradients(zip(grads, self.encoder.trainable_weights))   self.total_loss_tracker.update_state(total_loss)  self.reconstruction_loss_tracker.update_state(reconstruction_loss)  self.disc_tracker.update_state(d_loss)  self.discEnc_tracker.update_state(e_loss)  return {  "loss": self.total_loss_tracker.result(),  "reconstruction_loss": self.reconstruction_loss_tracker.result(),  "disc_loss": self.disc_tracker.result(),  "discEnc_loss": self.discEnc_tracker.result(),  }  

 (x_train, _), (x_test, _) = keras.datasets.mnist.load_data() mnist_digits = np.concatenate([x_train, x_test], axis=0) mnist_digits = np.expand_dims(mnist_digits, -1).astype("float32") / 255  Aae = AAE(encoder, decoder, discriminator) #vae.compile(optimizer=keras.optimizers.Adam()) Aae.compile(  di_optimizer=keras.optimizers.Adam(learning_rate=0.00001),  e_optimizer=keras.optimizers.Adam(learning_rate=0.0001),  de_optimizer=keras.optimizers.Adam(learning_rate=0.0001),  loss_fn=[tf.keras.losses.BinaryCrossentropy(),tf.keras.losses.MeanAbsoluteError()] ) h=Aae.fit(mnist_digits, epochs=15, batch_size=200)  

Я думаю, что ошибка здесь:

 with tf.GradientTape() as tape:  latent = self.encoder(data)  predictions = self.discriminator(latent)  e_loss = self.lossBCE(y_fake, predictions)   grads = tape.gradient(e_loss, self.encoder.trainable_weights)  self.e_optimizer.apply_gradients(zip(grads, self.encoder.trainable_weights))  

Я бы поставил e_loss = self.lossBCE(y_real, predictions) , потому что кодировщик пытается обмануть дискриминатор.