为什么自动编码器与编码器 + 解码器的预测不同？答案

【问题标题】：Why do predictions differ for Autoencoder vs. Encoder + Decoder?为什么自动编码器与编码器 + 解码器的预测不同？
【发布时间】：2020-02-10 12:45:55
【问题描述】：

我按照in this SO question 的建议在 Keras 中构建了一个 CNN 1d 自动编码器，其中编码器和解码器是分开的。我的目标是在自动编码器经过训练后重新使用解码器。我的自动编码器的中心层是Dense 层，因为我想在以后学习它。

我的问题是，如果我编译并拟合整个自动编码器，写为Decoder()Encoder()(x)，其中x 是输入，我会得到不同的预测

autoencoder.predict(training_set)

w.r.t.如果我首先将训练集编码为一组中心特征，然后让解码器解码它们。一旦自动编码器经过训练，这两种方法应该给出相同的答案。

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
from tensorflow.keras.layers import GaussianNoise
import keras.backend as K
from tensorflow.keras.layers import Add

import tensorflow as tf

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


training = # some training set, 1500 samples of 501 point each
testing = # some testing set, 500 samples of 501 point each

# reshaping for CNN
training = np.reshape(training, [1500, 501, 1])
testing = np.reshape(testing, [500, 501, 1])


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


copy_of_test = copy.copy(testing)
testing -= X_mean
testing /= X_std

### MODEL ###

def Encoder():
    encoder_input = Input(batch_shape=(None, 501, 1))  
    e1 = Conv1D(256,3, activation='tanh', padding='valid')(encoder_input)
    e2 = MaxPooling1D(2)(e1)
    e3 = Conv1D(32,3, activation='tanh', padding='valid')(e2)
    e4 = MaxPooling1D(2)(e3)
    e5 = Flatten()(e4)
    encoded = Dense(32,activation = 'tanh')(e5)
    return Model(encoder_input, encoded)


def Decoder():
    encoded_input = Input(shape=(32,))  
    encoded_reshaped = Reshape((32,1))(encoded_input)
    d1 = Conv1D(32, 3, activation='tanh', padding='valid', name='decod_conv1d_1')(encoded_reshaped)
    d2 = UpSampling1D(2, name='decod_upsampling1d_1')(d1)
    d3 = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(d2)
    d4 = UpSampling1D(2, name='decod_upsampling1d_2')(d3)
    d5 = Flatten(name='decod_flatten')(d4)
    d6 = Dense(501, name='decod_dense1')(d5)
    decoded = Reshape((501,1), name='decod_reshape')(d6)
    return Model(encoded_input, decoded)


# define input to the model:
x = Input(batch_shape=(None, 501, 1))
y = Input(shape=(32,))

# make the model:
autoencoder = Model(x, Decoder()(Encoder()(x)))

# compile the model:
autoencoder.compile(optimizer='adam', loss='mse')
for layer in autoencoder.layers: print(K.int_shape(layer.output))


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

# Encoder
encoder = Model(inputs=x, outputs=Encoder()(x), name='encoder')
print('enc:')
for layer in encoder.layers: print(K.int_shape(layer.output))
features = encoder.predict(training) # features

# Decoder
decoder = Model(inputs=y, outputs=Decoder()(y), name='decoder')
print('dec:')
for layer in decoder.layers: print(K.int_shape(layer.output))
score = decoder.predict(features) # 
score = np.squeeze(score)    

predictions = autoencoder.predict(training)
predictions = np.squeeze(predictions)

# plotting one random case
# score should be equal to predictions!
# because score is obtained from the trained decoder acting on the encoded features, while predictions are obtained form the Autoencoder acting on the training set 
plt.plot(score[100], label='eD')
plt.plot(predictions[100], label='AE')
plt.legend()
plt.show()
plt.close()

编辑跟随 OverLordGoldDragon 的回答：

我在答案中实现了建议，在同一个文件中写了以下内容：

def reset_seeds():
    np.random.seed(1)
    random.seed(2)
    if tf.__version__[0] == '2':
        tf.random.set_seed(3)
    else:
        tf.set_random_seed(3)
    print("RANDOM SEEDS RESET")


def Encoder():
    encoder_input = Input(batch_shape=(None, 501, 1))  
    e1 = Conv1D(256,3, activation='tanh', padding='valid')(encoder_input)
    e2 = MaxPooling1D(2)(e1)
    e3 = Conv1D(32,3, activation='tanh', padding='valid')(e2)
    e4 = MaxPooling1D(2)(e3)
    e5 = Flatten()(e4)
    encoded = Dense(32,activation = 'tanh')(e5)
    encoded = Reshape((32,1))(encoded)
    return Model(encoder_input, encoded)


def Decoder():
    encoded_input = Input(shape=(32,))  
    encoded_reshaped = Reshape((32,1))(encoded_input)
    d1 = Conv1D(32, 3, activation='tanh', padding='valid', name='decod_conv1d_1')(encoded_reshaped)
    d2 = UpSampling1D(2, name='decod_upsampling1d_1')(d1)
    d3 = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(d2)
    d4 = UpSampling1D(2, name='decod_upsampling1d_2')(d3)
    d5 = Flatten(name='decod_flatten')(d4)
    d6 = Dense(501, name='decod_dense1')(d5)
    decoded = Reshape((501,1), name='decod_reshape')(d6)
    return Model(encoded_input, decoded)


def DecoderAE(encoder_input, encoded_input):
    encoded_reshaped = Reshape((32,1))(encoded_input)
    d1 = Conv1D(32, 3, activation='tanh', padding='valid',
                       name='decod_conv1d_1')(encoded_reshaped)
    d2 = UpSampling1D(2, name='decod_upsampling1d_1')(d1)
    d3 = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(d2)
    d4 = UpSampling1D(2, name='decod_upsampling1d_2')(d3)
    d5 = Flatten(name='decod_flatten')(d4)
    d6 = Dense(501, name='decod_dense1')(d5)
    decoded = Reshape((501,1), name='decod_reshape')(d6)
    return Model(encoder_input, decoded)


def load_weights(model, filepath):
    with h5py.File(filepath, mode='r') as f:
        file_layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
        model_layer_names = [layer.name for layer in model.layers]

        weight_values_to_load = []
        for name in file_layer_names:
            if name not in model_layer_names:
                print(name, "is ignored; skipping")
                continue
            g = f[name]
            weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]

            weight_values = []
            if len(weight_names) != 0:
                weight_values = [g[weight_name] for weight_name in weight_names]
            try:
                layer = model.get_layer(name=name)
            except:
                layer = None
            if layer is not None:
                symbolic_weights = (layer.trainable_weights + 
                                    layer.non_trainable_weights)
                if len(symbolic_weights) != len(weight_values):
                    print('Model & file weights shapes mismatch')
                else:
                    weight_values_to_load += zip(symbolic_weights, weight_values)

        K.batch_set_value(weight_values_to_load)


X = np.random.randn(10, 501, 1)
reset_seeds()
encoder = Encoder()
AE = DecoderAE(encoder.input, encoder.output)
AE.compile(optimizer='adam', loss='mse')


epochs = 10
batch_size = 100
validation_split = 0.2
# train the model
history = AE.fit(x = training, y = training,
                    epochs=epochs,
                    batch_size=batch_size,
                    validation_split=validation_split)


reset_seeds()
encoder = Encoder()
decoder = Decoder()

# Test equality
features = encoder.predict(X)
features = np.squeeze(features) # had to add this otherwise it would complain because of wrong shapes
score = decoder.predict(features) 
predictions = AE.predict(X)
print(np.sum(score - predictions))
# I am actually getting values >> 1


AE.save_weights('autoencoder_weights.h5')
AE_saved_weights = AE.get_weights()

decoder = Decoder()
load_weights(decoder, 'autoencoder_weights.h5')  # see "reference"
decoder_loaded_weights = decoder.get_weights()

AE_decoder_weights = AE_saved_weights[-len(decoder_loaded_weights):]
for w1, w2 in zip(AE_decoder_weights, decoder_loaded_weights):
    print(np.sum(w1 - w2))

代码运行训练 AE，但是

1) 对于score 和predictions 之间的差异，我得到值>>1

2) 代码停止生成

(u'input_1', 'is ignored; skipping')
(u'conv1d', 'is ignored; skipping')
(u'max_pooling1d', 'is ignored; skipping')
(u'conv1d_1', 'is ignored; skipping')
(u'max_pooling1d_1', 'is ignored; skipping')
(u'flatten', 'is ignored; skipping')
(u'dense', 'is ignored; skipping')
(u'reshape', 'is ignored; skipping')
(u'reshape_1', 'is ignored; skipping')
Traceback (most recent call last):
  File "Autoenc.py", line 256, in <module>
    load_weights(decoder, 'autoencoder_weights.h5')  # see "reference"
  File "Autoenc.py", line 219, in load_weights
    K.batch_set_value(weight_values_to_load)
  File "/home/user/anaconda3/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 2725, in batch_set_value
    assign_placeholder = tf.placeholder(tf_dtype,
AttributeError: 'module' object has no attribute 'placeholder'

2 编辑

这是我在@OverLordGoldDragon 最后评论之后的新文件。我收到下面发布的错误。

def reset_seeds():
    np.random.seed(1)
    random.seed(2)
    if tf.__version__[0] == '2':
        tf.random.set_seed(3)
    else:
        tf.set_random_seed(3)
    print("RANDOM SEEDS RESET")


def Encoder():
    encoder_input = Input(batch_shape=(None, 501, 1))  
    e1 = Conv1D(256,3, activation='tanh', padding='valid')(encoder_input)
    e2 = MaxPooling1D(2)(e1)
    e3 = Conv1D(32,3, activation='tanh', padding='valid')(e2)
    e4 = MaxPooling1D(2)(e3)
    e5 = Flatten()(e4)
    encoded = Dense(32,activation = 'tanh')(e5)
    encoded = Reshape((32,1))(encoded)
    return Model(encoder_input, encoded)


def Decoder():
    encoded_input = Input(shape=(32,))  
    encoded_reshaped = Reshape((32,1))(encoded_input)
    d1 = Conv1D(32, 3, activation='tanh', padding='valid', name='decod_conv1d_1')(encoded_reshaped)
    d2 = UpSampling1D(2, name='decod_upsampling1d_1')(d1)
    d3 = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(d2)
    d4 = UpSampling1D(2, name='decod_upsampling1d_2')(d3)
    d5 = Flatten(name='decod_flatten')(d4)
    d6 = Dense(501, name='decod_dense1')(d5)
    decoded = Reshape((501,1), name='decod_reshape')(d6)
    return Model(encoded_input, decoded)


def DecoderAE(encoder_input, encoded_input):
    encoded_reshaped = Reshape((32,1))(encoded_input)
    d1 = Conv1D(32, 3, activation='tanh', padding='valid',
                       name='decod_conv1d_1')(encoded_reshaped)
    d2 = UpSampling1D(2, name='decod_upsampling1d_1')(d1)
    d3 = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(d2)
    d4 = UpSampling1D(2, name='decod_upsampling1d_2')(d3)
    d5 = Flatten(name='decod_flatten')(d4)
    d6 = Dense(501, name='decod_dense1')(d5)
    decoded = Reshape((501,1), name='decod_reshape')(d6)
    return Model(encoder_input, decoded)


def load_weights(model, filepath):
    with h5py.File(filepath, mode='r') as f:
        file_layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
        model_layer_names = [layer.name for layer in model.layers]

        weight_values_to_load = []
        for name in file_layer_names:
            if name not in model_layer_names:
                print(name, "is ignored; skipping")
                continue
            g = f[name]
            weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]

            weight_values = []
            if len(weight_names) != 0:
                weight_values = [g[weight_name] for weight_name in weight_names]
            try:
                layer = model.get_layer(name=name)
            except:
                layer = None
            if layer is not None:
                symbolic_weights = (layer.trainable_weights + 
                                    layer.non_trainable_weights)
                if len(symbolic_weights) != len(weight_values):
                    print('Model & file weights shapes mismatch')
                else:
                    weight_values_to_load += zip(symbolic_weights, weight_values)

        K.batch_set_value(weight_values_to_load)


X = np.random.randn(10, 501, 1)
reset_seeds()
encoder = Encoder()
AE = DecoderAE(encoder.input, encoder.output)
AE.compile(optimizer='adam', loss='mse')


epochs = 2
batch_size = 100
validation_split = 0.2
# train the model
history = AE.fit(x = training, y = training,
                    epochs=epochs,
                    batch_size=batch_size,
                    validation_split=validation_split)


reset_seeds()
encoder = Encoder()
decoder = Decoder()
decoder.save_weights('decoder_weights.h5')



AE.save_weights('autoencoder_weights.h5')
AE_saved_weights = AE.get_weights()

decoder = Decoder()
load_weights(decoder, 'autoencoder_weights.h5')  # see "reference"
decoder_loaded_weights = decoder.get_weights()

# Test equality
features = encoder.predict(X)
features = np.squeeze(features) 
score = decoder.predict(features) 
predictions = AE.predict(X)
print(np.sum(score - predictions))


AE_decoder_weights = AE_saved_weights[-len(decoder_loaded_weights):]
for w1, w2 in zip(AE_decoder_weights, decoder_loaded_weights):
    print(np.sum(w1 - w2))



Traceback (most recent call last):
  File "Autoenc_pazzo.py", line 251, in <module>
    decoder_loaded_weights = decoder.get_weights()
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/training.py", line 153, in get_weights
    return super(Model, self).get_weights()
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1130, in get_weights
    return backend.batch_get_value(params)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/backend.py", line 3010, in batch_get_value
    return get_session(tensors).run(tensors)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 950, in run
    run_metadata_ptr)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1173, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1350, in _do_run
    run_metadata)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1370, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable decod_conv1d_1_2/bias from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/decod_conv1d_1_2/bias/N10tensorflow3VarE does not exist.
     [[node decod_conv1d_1_2/bias/Read/ReadVariableOp (defined at Autoenc_pazzo.py:168) ]]

Original stack trace for u'decod_conv1d_1_2/bias/Read/ReadVariableOp':
  File "Autoenc_pazzo.py", line 249, in <module>
    decoder = Decoder()
  File "Autoenc_pazzo.py", line 168, in Decoder
    d1 = Conv1D(32, 3, activation='tanh', padding='valid', name='decod_conv1d_1')(encoded_reshaped)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 591, in __call__
    self._maybe_build(inputs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1881, in _maybe_build
    self.build(input_shapes)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 174, in build
    dtype=self.dtype)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 384, in add_weight
    aggregation=aggregation)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/training/tracking/base.py", line 663, in _add_variable_with_custom_getter
    **kwargs_for_getter)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer_utils.py", line 155, in make_variable
    shape=variable_shape if variable_shape.rank else None)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 259, in __call__
    return cls._variable_v1_call(*args, **kwargs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 220, in _variable_v1_call
    shape=shape)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 198, in <lambda>
    previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 2495, in default_variable_creator
    shape=shape)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 263, in __call__
    return super(VariableMetaclass, cls).__call__(*args, **kwargs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 460, in __init__
    shape=shape)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 649, in _init_from_args
    value = self._read_variable_op()
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 935, in _read_variable_op
    self._dtype)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/ops/gen_resource_variable_ops.py", line 587, in read_variable_op
    "ReadVariableOp", resource=resource, dtype=dtype, name=name)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3616, in create_op
    op_def=op_def)
  File "/home/alessio/anaconda3/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2005, in __init__
    self._traceback = tf_stack.extract_stack()

【问题讨论】：

@OverLordGoldDragon
@MegaNightdude 也许你知道答案
这里的问题相当棘手，与自动编码器无关——尽管你的 AE 代码无论如何都关闭了。然而，在没有对回答的问题给出适当反馈的情况下跳过相关问题是一种不好的做法（尤其是如果再次询问回答者）。我得到了 100% 同意的预测——如果你想让我告诉你怎么做，让我们先完成你之前的问题。 P.S.，“@”不适用于用户未参与的问题 - 我独立偶然发现了这个问题。
@OverLordGoldDragon 道歉。我在stackoverflow.com/questions/58364974/… 中编辑了我的问题，以便为您提供有关我实施的程序的反馈。现在看起来，一旦给定编码特征，保存的解码器就不会产生与完全自动编码器相同的输出。总的来说，如果您也能告诉我为什么我的 AE 代码无论如何都关闭了，我将不胜感激。谢谢
保存/加载模型的最佳测试是在保存之前将模型权重分配给一个变量，然后在加载后与另一个变量相同，然后减去进行比较；如果您看到差异总和为 0（或每次舍入接近），则代码有效并且前一个问题得到解决。我会回答这个当前的问题，但不应放弃前一个问题（即不接受）

标签： python tensorflow keras neural-network autoencoder

【解决方案1】：

您的代码的主要问题：没有使用随机种子。我参考this answer 的完整解释，并在此处关注您的特定实例。

解释：

模型实例化的顺序很重要，因为它会改变初始化的权重
在使用中，如果您训练多个模型，则应在模型实例化时和在训练前重置种子
您的 AE、编码器和解码器定义重复使用了 Input，并使自省复杂化（例如 .summary()）； Encoder() 和 Decoder() 已经处理好了
要检查加载的解码器权重是否与保存的经过训练的 AE 解码器权重匹配，请参见下面的示例

解决方案：

reset_seeds()
X = np.random.randn(10, 501, 1)  # '10' arbitrary
encoder_input = Input(batch_shape=(None, 501, 1))

reset_seeds()
encoder = Encoder()
decoder = Decoder()
autoencoder = Model(x, decoder(encoder(x)))
autoencoder.compile(optimizer='adam', loss='mse')

reset_seeds()
encoder = Encoder()
decoder = Decoder()

predictions = autoencoder.predict(X)

features = encoder.predict(X)
score = decoder.predict(features)

print(np.sum(score - predictions))
# 0.0  <-- 100% agreement

保存/加载示例 + 首选 AE 定义； reference

您的 AE 定义通过例如限制自省.summary();相反，定义如下。

X = np.random.randn(10, 501, 1)
reset_seeds()
encoder = Encoder()
AE = DecoderAE(encoder.input, encoder.output)
AE.compile(optimizer='adam', loss='mse')

reset_seeds()
encoder = Encoder()
decoder = Decoder()

# Test equality
features = encoder.predict(X)
score = decoder.predict(features) 
predictions = AE.predict(X)
print(np.sum(score - predictions))
# 0.0  <-- exact or close to

AE.save_weights('autoencoder_weights.h5')
AE_saved_weights = AE.get_weights()

decoder = Decoder()
load_weights(decoder, 'autoencoder_weights.h5')  # see "reference"
decoder_loaded_weights = decoder.get_weights()

AE_decoder_weights = AE_saved_weights[-len(decoder_loaded_weights):]
for w1, w2 in zip(AE_decoder_weights, decoder_loaded_weights):
    print(np.sum(w1 - w2))
# 0.0
# 0.0
# ...

使用的功能：

def reset_seeds():
    np.random.seed(1)
    random.seed(2)
    if tf.__version__[0] == '2':
        tf.random.set_seed(3)
    else:
        tf.set_random_seed(3)
    print("RANDOM SEEDS RESET")

def DecoderAE(encoder_input, encoded_input):
    encoded_reshaped = Reshape((32,1))(encoded_input)
    d1 = Conv1D(32, 3, activation='tanh', padding='valid',
                       name='decod_conv1d_1')(encoded_reshaped)
    d2 = UpSampling1D(2, name='decod_upsampling1d_1')(d1)
    d3 = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(d2)
    d4 = UpSampling1D(2, name='decod_upsampling1d_2')(d3)
    d5 = Flatten(name='decod_flatten')(d4)
    d6 = Dense(501, name='decod_dense1')(d5)
    decoded = Reshape((501,1), name='decod_reshape')(d6)
    return Model(encoder_input, decoded)

【讨论】：

我很高兴并准备接受您的回答，非常感谢。我变得不同了scores 和predictions；查看我对问题的编辑。
@johnhenry 如果您没有看到我的代码的预期行为，那么这个问题没有得到“回答”，不用担心。问题是，您在运行 encoder.predict 和 decoder.predict 之前没有首先加载权重 - 因此将它们的预测与 受过训练的 AE 进行比较。另外，不知道是什么导致了错误——您使用的是哪个 (1) TensorFlow、(2) Keras 和 (3) Python 版本？除了您提供的代码之外，还有其他代码吗？
谢谢我通过从 Tensorflow 2 降级到 1.14 解决了第二个问题。现在我有一个问题，当使用函数load_weights 时，似乎无法找到解码器的权重，请参阅我的问题的最后编辑。请评论要遵循的步骤的确切顺序；我怀疑我没有遵循正确的顺序
@johnhenry 你确定它没有找到 decoder 权重吗？您的输出日志显示它精确地忽略了 encoder 权重。尝试准确地复制粘贴我的答案代码，看看它是否有效 - 之后可以根据需要进行调整。如果问题仍然存在，请告诉我
@johnhenry 是的，这绝对看起来像一个糟糕的安装 - 尝试步骤here。我提供的代码适用于正确的安装，与调试问题相比，您可能会节省大量时间重新安装。作为提示，升级到 Python 3.6+，3.7 甚至还附带了固定顺序的字典。