我在尝试使用人脸图像训练VAE时，在调用model.fit()方法后遇到了一个错误。我找不到解决这个问题的方案。
我得到的错误是：
ValueError: Cannot create an execution function which is comprised of elements from multiple graphs.编码器：
def build_encoder(self): global K K.clear_session() conv_filters = [32, 64, 64, 64] conv_kernel_size = [3, 3, 3, 3] conv_strides = [2, 2, 2, 2] n_layers = len(conv_filters) x = self.encoder_input for i in range(n_layers): x = Conv2D(filters=conv_filters[i], kernel_size=conv_kernel_size[i], strides=conv_strides[i], padding='same', name='encoder_conv_' + str(i) )(x) if self.use_batch_norm: x = BatchNormalization()(x) x = LeakyReLU()(x) if self.use_dropout: x = Dropout(rate=0.25)(x) self.shape_before_flattening = K.int_shape(x)[1:] x = Flatten()(x) self.mean_layer = Dense(self.encoder_output_dim, name='mu')(x) self.sd_layer = Dense(self.encoder_output_dim, name='log_var')(x) def sampling(args): mean_mu, log_var = args epsilon = K.random_normal(shape=K.shape(mean_mu), mean=0., stddev=1.) return mean_mu + K.exp(log_var / 2) * epsilon encoder_output = Lambda(sampling, name='encoder_output')([self.mean_layer, self.sd_layer]) return Model(self.encoder_input, encoder_output, name="VAE_Encoder")解码器：
def build_decoder(self): conv_filters = [64, 64, 32, 3] conv_kernel_size = [3, 3, 3, 3] conv_strides = [2, 2, 2, 2] n_layers = len(conv_filters) decoder_input = self.decoder_input x = Dense(np.prod(self.shape_before_flattening))(decoder_input) x = Reshape(self.shape_before_flattening)(x) for i in range(n_layers): x = Conv2DTranspose(filters=conv_filters[i], kernel_size=conv_kernel_size[i], strides=conv_strides[i], padding='same', name='decoder_conv_' + str(i) )(x) if i < n_layers - 1: x = LeakyReLU()(x) else: x = Activation('sigmoid')(x) self.decoder_output = x return Model(decoder_input, self.decoder_output, name="VAE_Decoder")组合模型：
def build_autoencoder(self): self.encoder = self.build_encoder() self.decoder = self.build_decoder() self.autoencoder = Model(self.encoder_input, self.decoder(self.encoder(self.encoder_input)), name="Variational_Auto_Encoder") self.autoencoder.compile(optimizer=self.adam_optimizer, loss=self.total_loss, metrics=[self.r_loss, self.kl_loss], experimental_run_tf_function=False) self.autoencoder.summary() if os.path.exists(self.model_name + ".h5"): self.autoencoder.load_weights(self.model_name + ".h5") # 加载预训练权重 return self.autoencoder训练过程：
def train(self): filenames = np.array(glob.glob(os.path.join(self.data_dir, '*/*.jpg'))) NUM_IMAGES = len(filenames) print("图像总数 : " + str(NUM_IMAGES)) data_flow = ImageDataGenerator(rescale=1. / 255).flow_from_directory(self.data_dir, target_size=self.input_shape[:2], batch_size=self.batch_size, shuffle=True, class_mode='input', subset='training' ) self.autoencoder.fit_generator(data_flow, shuffle=True, epochs=self.epochs, initial_epoch=0, steps_per_epoch=NUM_IMAGES // self.batch_size # callbacks=[self.checkpoint_callback] ) self.autoencoder.save_weights(self.save_dir + self.model_name + ".h5")我知道在这里提问可能不是最好的方式，但我真的不知道该如何解决这个问题，希望你能告诉我我做错了什么。☺
回答：
导致问题的代码行是：
K.clear_session()移除它就解决了问题。
