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。我将数据分为1500个样本进行训练，将500个样本进行测试。我要保存解码器部分。这是我的代码：

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: []

我应该如何调整线条

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

这样输入就可以保存经过训练的解码器？

Answer 1

问题是，您在encoded中的Model并不是先前期望的Input层-您的解码器定义断开了Input-Output图的连接。为此，您需要针对解码器模型分别重构解码器，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 not 连接到AE的E层。

下面是一个清理代码，定义了自动编码器和解码器并保存了它们。

## 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')

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

1 个答案: