条件VAE -无法建立模型

Question

我目前有一个VAE与data_x dim =[300000,60,5]工作。我想加上data_y dim =[300000,1]，这是一个二进制条件。然而，我一直得到以下错误:

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_2:0", shape=(None, 1), dtype=float32) at layer "concatenate_1". The following previous layers were accessed without issue: []

我很确定将潜在输入与input_y连接在一起会出问题，但我找不到实际的解决方案。我将在下面发布我的模型代码(我使用自定义train_step)。也许我的整个方法都错了?

import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard
"""
## Create a sampling layer
"""
class Sampling(tf.keras.layers.Layer):
"""Uses (z_mean, z_log_var) to sample z."""
def call(self, inputs):
z_mean, z_log_var = inputs
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
"""
## Build the encoder
"""
latent_dim = 8
N_chan= 5
input_x = tf.keras.layers.Input(shape=(60,N_chan))
input_y = tf.keras.layers.Input(shape=(1,))
x = tf.keras.layers.Masking(mask_value=999)(input_x)
x = tf.keras.layers.Conv1D(32, 3, activation="relu", strides=2, padding="same")(x)
x = tf.keras.layers.Conv1D(64, 3, activation="relu", strides=2, padding="same")(x)
x = tf.keras.layers.Conv1D(64, 3, activation="relu", strides=1, padding="same")(x)
x = tf.keras.layers.Conv1D(64, 3, activation="relu", strides=1, padding="same")(x)
x = tf.keras.layers.Flatten()(x)
conc = tf.keras.layers.Concatenate()([x, input_y])
x = tf.keras.layers.Dense(64, activation='relu')(conc)
# x = tf.keras.layers.Dense(latent_dim)(x)
z_mean = tf.keras.layers.Dense(latent_dim, name="z_mean")(x)
z_log_var = tf.keras.layers.Dense(latent_dim, name="z_log_var")(x)
z = Sampling()([z_mean, z_log_var])
encoder = tf.keras.Model(inputs=[input_x,input_y], outputs=[z_mean, z_log_var, z], name="encoder")
encoder.summary()
embedd = tf.keras.layers.Input(shape=(latent_dim,))
merged_input = tf.keras.layers.Concatenate()([embedd, input_y])
x = tf.keras.layers.Dense(15 * 64, activation='relu')(merged_input)
x = tf.keras.layers.Reshape(target_shape=(15, 64))(x)
x = tf.keras.layers.Conv1DTranspose(filters=64,kernel_size=3,strides=1,padding='same', activation='relu')(x)
x = tf.keras.layers.Conv1DTranspose(filters=64,kernel_size=3,strides=2,padding='same',activation='relu')(x)
x = tf.keras.layers.Conv1DTranspose(filters=64,kernel_size=3,strides=1,padding='same',activation='relu')(x)
x = tf.keras.layers.Conv1DTranspose(filters=32,kernel_size=3,strides=2,padding='same',activation="relu")(x)
decoder_outputs = tf.keras.layers.Conv1DTranspose(filters=N_chan,kernel_size=3,activation="sigmoid",padding="same")(x)
decoder = tf.keras.Model(inputs=embedd, outputs=decoder_outputs, name="decoder")
decoder.summary()
"""
## Define the VAE as a `Model` with a custom `train_step`
"""
class VAE(tf.keras.Model):
def __init__(self, encoder, decoder, **kwargs):
super(VAE, self).__init__(**kwargs)
self.encoder = encoder
self.decoder = decoder
self.total_loss_tracker = tf.keras.metrics.Mean(name="total_loss")
self.reconstruction_loss_tracker = tf.keras.metrics.Mean(
name="reconstruction_loss"
)
self.kl_loss_tracker = tf.keras.metrics.Mean(name="kl_loss")
self.kl_weight = 0.002
@property
def metrics(self):
return [
self.total_loss_tracker,
self.reconstruction_loss_tracker,
self.kl_loss_tracker,
]
def train_step(self, data):
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder(data)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(
tf.reduce_sum(
tf.keras.losses.binary_crossentropy(data, reconstruction), axis=(1)
)
)
kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
total_loss = reconstruction_loss + self.kl_weight*kl_loss
grads = tape.gradient(total_loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
self.total_loss_tracker.update_state(total_loss)
self.reconstruction_loss_tracker.update_state(reconstruction_loss)
self.kl_loss_tracker.update_state(kl_loss)
return {
"loss": self.total_loss_tracker.result(),
"reconstruction_loss": self.reconstruction_loss_tracker.result(),
"kl_loss": self.kl_loss_tracker.result(),
}
#%% load data
file_x = "x_cvae.npy"
file_y = "y_cvae_binary.npy"
data_x,data_y = np.load(file_x),np.expand_dims(np.load(file_y),-1)
#%%
NAME = "Test"
vae = VAE(encoder, decoder)
vae.compile(optimizer=tf.keras.optimizers.Adam())
tensorboard = TensorBoard(log_dir='logs\{}'.format(NAME),profile_batch=0)
vae.fit([data_x,data_y],data_x, epochs=10, batch_size=64
,validation_data=([data_x,data_y],data_x),callbacks=[tensorboard])```

Answer 1

问题是您的encoder和decoder模型共享相同的输入input_y，尽管它们是两个独立的模型。您必须为您的decoder定义一个单独的input_y，或者将编码器和解码器合并到一个模型中。下面是如何为编码器和解码器使用两个输入的示例。