我在Keras中构建了一个CNN 1d自动编码器，遵循此SO问题中的建议，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，写成Decoder()Encoder()(x)其中x是输入，当我这样做时，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，然后让解码器对它们进行解码。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()
按照霸王金龙的回答编辑：
我在答案中实现了该建议，在同一个文件中编写了以下内容：
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))
但是，代码运行训练 AE
1(我得到score和predictions之差的值>>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最后一条评论之后的新文件。 我在下面发布了错误。
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()
代码的主要问题：缺乏使用随机种子。我指的是对这个答案的完整解释，并在这里关注您的特定实例。
解释：
- 模型实例化的顺序很重要，因为它会更改初始化的权重
- 在使用中，如果训练多个模型，则应在模型实例化时和训练之前重置种子
- 您的 AE、编码器和解码器定义冗余地使用了
Input，并使内省复杂化(例如.summary()(;Encoder()和Decoder()已经处理好了
要检查加载的解码器权重 - 是否与保存的训练 AE 解码器权重匹配，请参阅下面的示例
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;参考
您的AE定义通过以下方式限制内省，例如.summary();相反，请按如下方式定义。
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)