ValueError:图形已断开连接：无法获取张量的值（保存自动编码器的解码器）

Question

我在Keras建立了CNN自动编码器。我的数据（未提供）是501点的2000个样本。我把数据分成1500个样本进行训练，500个样本进行测试。我想保存解码器部分。这是我的密码：

from tensorflow.keras.layers import Input, Dense, BatchNormalization, Flatten, Lambda, Activation, Conv1D, MaxPooling1D, UpSampling1D, Reshape
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers

import sys
import matplotlib.pyplot as plt
import numpy as np
import copy


# read data
data = # some data
# shuffle
import random
random.seed(4)
random.shuffle(data)

# split train/test
X_train = data[:1500]
X_test = data[1500:]

# reshaping for CNN
X_training = np.reshape(X_train, [1500, 501, 1])
X_testing = np.reshape(X_test, [500, 501, 1])

# normalize input
X_mean = X_training.mean()
X_training -= X_mean
X_std = X_training.std()
X_training /= X_std

X_testing -= X_mean
X_testing /= X_std


## MODEL ###
# ENCODER
input_sig = Input(batch_shape=(None,501,1))
x = Conv1D(256,3, activation='tanh', padding='valid')(input_sig)
x1 = MaxPooling1D(2)(x)
x2 = Conv1D(32,3, activation='tanh', padding='valid')(x1)
x3 = MaxPooling1D(2)(x2)
flat = Flatten()(x3)
encoded = Dense(32,activation = 'tanh')(flat)

# DECODER 
x2_ = Conv1D(32, 3, activation='tanh', padding='valid')(x3)
x1_ = UpSampling1D(2)(x2_)
x_ = Conv1D(256, 3, activation='tanh', padding='valid')(x1_)
upsamp = UpSampling1D(2)(x_)
flat = Flatten()(upsamp)
decoded = Dense(501)(flat)
decoded = Reshape((501,1))(decoded)

autoencoder = Model(input_sig, decoded)
autoencoder.compile(optimizer='adam', loss='mse', metrics=['accuracy'])


### TRAINING ###
epochs = 50
batch_size = 100
validation_split = 0.2
# train the model
history = autoencoder.fit(x = X_training, y = X_training,
                    epochs=epochs,
                    batch_size=batch_size,
                    validation_split=validation_split)

# Decoder
decoder = Model(inputs=encoded, outputs=decoded, name='decoder')
# save decoder
decoder.save('decoder.hdf5')

我得到的错误是

W1013 12:08:17.131777 140693540189952 network.py:1619] Model inputs must come from `tf.keras.Input` (thus holding past layer metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to "decoder" was not an Input tensor, it was generated by layer dense.
Note that input tensors are instantiated via `tensor = tf.keras.Input(shape)`.
The tensor that caused the issue was: dense/Tanh:0
Traceback (most recent call last):
  File "Autoenc_CNN_ISOTROPIC_oscillations.py", line 191, in <module>
    decoder = Model(inputs=encoded, outputs=decoded, name='decoder')
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/training.py", line 122, in __init__
    super(Model, self).__init__(*args, **kwargs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/network.py", line 138, in __init__
    self._init_graph_network(*args, **kwargs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/training/tracking/base.py", line 456, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/network.py", line 284, in _init_graph_network
    self.inputs, self.outputs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/network.py", line 1814, in _map_graph_network
    str(layers_with_complete_input))
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 501, 1), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []

我该如何调整线路

decoder = Model(inputs=encoded, outputs=decoded, name='decoder')

这样我就能保存经过训练的解码器了？你知道吗

Answer 1

问题是，您在Model中的encoded不是Input层，这是前者所期望的-并且您的解码器定义断开了输入输出图的连接。为此，您需要为解码器模型分别重建解码器，因为自动编码器（AE）解码器将AE的编码器层输出作为输入，而单独的解码器模型将不连接到AE的E层。你知道吗

下面是定义自动编码器和解码器并保存两者的清理代码。你知道吗

## ENCODER
encoder_input = Input(batch_shape=(None,501,1))
x  = Conv1D(256,3, activation='tanh', padding='valid')(encoder_input)
x  = MaxPooling1D(2)(x)
x  = Conv1D(32,3, activation='tanh', padding='valid')(x)
x  = MaxPooling1D(2)(x)
_x = Flatten()(x)
encoded = Dense(32,activation = 'tanh')(_x)

## DECODER (autoencoder)
y = Conv1D(32, 3, activation='tanh', padding='valid')(x)
y = UpSampling1D(2)(y)
y = Conv1D(256, 3, activation='tanh', padding='valid')(y)
y = UpSampling1D(2)(y)
y = Flatten()(y)
y = Dense(501)(y)
decoded = Reshape((501,1))(y)

autoencoder = Model(encoder_input, decoded)
autoencoder.save('autoencoder.hdf5')

## DECODER (independent)
decoder_input = Input(batch_shape=K.int_shape(x))  # import keras.backend as K
y = Conv1D(32, 3, activation='tanh', padding='valid')(decoder_input)
y = UpSampling1D(2)(y)
y = Conv1D(256, 3, activation='tanh', padding='valid')(y)
y = UpSampling1D(2)(y)
y = Flatten()(y)
y = Dense(501)(y)
decoded = Reshape((501,1))(y)

decoder = Model(decoder_input, decoded)
decoder.save('decoder.hdf5')