变分自动编码器（keras）中的新错误
new bug in a variational autoencoder (keras)
我曾经用这个代码来训练变分自动编码器（我在论坛上找到了代码并根据我的需要进行了调整）：
import pickle
from pylab import mpl,plt
#lecture des résultats
filename=r'XXX.pic'
data_file=open(filename,'rb')
X_sec = pickle.load(data_file)#[:,3000:]
data_file.close()
size=X_sec.shape[0]
prop=0.75
cut=int(size*prop)
X_train=X_sec[:cut]
X_test=X_sec[cut:]
std=X_train.std()
X_train /= std
X_test /= std
import keras
from keras import layers
from keras import backend as K
from keras.models import Model
import numpy as np
#encoding_dim = 12
sig_shape = (3600,)
batch_size = 128
latent_dim = 12
input_sig = keras.Input(shape=sig_shape)
x = layers.Dense(128, activation='relu')(input_sig)
x = layers.Dense(64, activation='relu')(x)
shape_before_flattening = K.int_shape(x)
x = layers.Dense(32, activation='relu')(x)
z_mean = layers.Dense(latent_dim)(x)
z_log_var = layers.Dense(latent_dim)(x)
encoder=Model(input_sig,[z_mean,z_log_var])
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),
mean=0., stddev=1.)
return z_mean + K.exp(z_log_var) * epsilon
z = layers.Lambda(sampling)([z_mean, z_log_var])
decoder_input = layers.Input(K.int_shape(z)[1:])
x = layers.Dense(np.prod(shape_before_flattening[1:]),activation='relu')(decoder_input)
x = layers.Reshape(shape_before_flattening[1:])(x)
x = layers.Dense(128, activation='relu')(x)
x = layers.Dense(3600, activation='linear')(x)
decoder = Model(decoder_input, x)
z_decoded = decoder(z)
class CustomVariationalLayer(keras.layers.Layer):
def vae_loss(self, x, z_decoded):
x = K.flatten(x)
z_decoded = K.flatten(z_decoded)
xent_loss = keras.metrics.mae(x, z_decoded)
kl_loss = -5e-4 * K.mean(
1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return K.mean(xent_loss + kl_loss)
def call(self, inputs):
x = inputs[0]
z_decoded = inputs[1]
loss = self.vae_loss(x, z_decoded)
self.add_loss(loss, inputs=inputs)
return x
y = CustomVariationalLayer()([input_sig, z_decoded])
vae = Model(input_sig, y)
vae.compile(optimizer='rmsprop', loss=None)
vae.summary()
vae.fit(x=X_train, y=None,shuffle=True,epochs=100,batch_size=batch_size,validation_data=(X_test, None))
它过去工作顺利，但我更新了我的库，现在我收到这个错误：
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 1619, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
InvalidArgumentError: Duplicate node name in graph:
'lambda_1/random_normal/shape'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File
"I:\Documents\Nico\Python\finance\travail_amont\autoencoder_variationnel_bruit.py",
line 74, in
z = layers.Lambda(sampling)([z_mean, z_log_var])
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\backend\tensorflow_backend.py",
line 75, in symbolic_fn_wrapper
return func(*args, **kwargs)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\engine\base_layer.py",
line 506, in call
output_shape = self.compute_output_shape(input_shape)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\layers\core.py",
line 674, in compute_output_shape
x = self.call(xs)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\layers\core.py",
line 716, in call
return self.function(inputs, **arguments)
File
"I:\Documents\Nico\Python\finance\travail_amont\autoencoder_variationnel_bruit.py",
line 71, in sampling
mean=0., stddev=1.)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\backend\tensorflow_backend.py",
line 4329, in random_normal
shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\keras\backend.py",
line 5602, in random_normal
shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\random_ops.py",
line 69, in random_normal
shape_tensor = tensor_util.shape_tensor(shape)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\tensor_util.py",
line 994, in shape_tensor
return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 1314, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\array_ops.py",
line 1368, in _autopacking_conversion_function
return _autopacking_helper(v, dtype, name or "packed")
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\array_ops.py",
line 1304, in _autopacking_helper
return gen_array_ops.pack(elems_as_tensors, name=scope)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py",
line 5704, in pack
"Pack", values=values, axis=axis, name=name)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\op_def_library.py",
line 742, in _apply_op_helper
attrs=attr_protos, op_def=op_def)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\func_graph.py",
line 595, in _create_op_internal
compute_device)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 3322, in _create_op_internal
op_def=op_def)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 1786, in init
control_input_ops)
File
"C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py",
line 1622, in _create_c_op
raise ValueError(str(e))
ValueError: Duplicate node name in graph:
'lambda_1/random_normal/shape'
我不知道这个错误：“图中的节点名称重复”。有人知道吗？谢谢
我曾经用这个代码来训练变分自动编码器（我在论坛上找到了代码并根据我的需要进行了调整）：
import pickle
from pylab import mpl,plt
#lecture des résultats
filename=r'XXX.pic'
data_file=open(filename,'rb')
X_sec = pickle.load(data_file)#[:,3000:]
data_file.close()
size=X_sec.shape[0]
prop=0.75
cut=int(size*prop)
X_train=X_sec[:cut]
X_test=X_sec[cut:]
std=X_train.std()
X_train /= std
X_test /= std
import keras
from keras import layers
from keras import backend as K
from keras.models import Model
import numpy as np
#encoding_dim = 12
sig_shape = (3600,)
batch_size = 128
latent_dim = 12
input_sig = keras.Input(shape=sig_shape)
x = layers.Dense(128, activation='relu')(input_sig)
x = layers.Dense(64, activation='relu')(x)
shape_before_flattening = K.int_shape(x)
x = layers.Dense(32, activation='relu')(x)
z_mean = layers.Dense(latent_dim)(x)
z_log_var = layers.Dense(latent_dim)(x)
encoder=Model(input_sig,[z_mean,z_log_var])
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),
mean=0., stddev=1.)
return z_mean + K.exp(z_log_var) * epsilon
z = layers.Lambda(sampling)([z_mean, z_log_var])
decoder_input = layers.Input(K.int_shape(z)[1:])
x = layers.Dense(np.prod(shape_before_flattening[1:]),activation='relu')(decoder_input)
x = layers.Reshape(shape_before_flattening[1:])(x)
x = layers.Dense(128, activation='relu')(x)
x = layers.Dense(3600, activation='linear')(x)
decoder = Model(decoder_input, x)
z_decoded = decoder(z)
class CustomVariationalLayer(keras.layers.Layer):
def vae_loss(self, x, z_decoded):
x = K.flatten(x)
z_decoded = K.flatten(z_decoded)
xent_loss = keras.metrics.mae(x, z_decoded)
kl_loss = -5e-4 * K.mean(
1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return K.mean(xent_loss + kl_loss)
def call(self, inputs):
x = inputs[0]
z_decoded = inputs[1]
loss = self.vae_loss(x, z_decoded)
self.add_loss(loss, inputs=inputs)
return x
y = CustomVariationalLayer()([input_sig, z_decoded])
vae = Model(input_sig, y)
vae.compile(optimizer='rmsprop', loss=None)
vae.summary()
vae.fit(x=X_train, y=None,shuffle=True,epochs=100,batch_size=batch_size,validation_data=(X_test, None))
它过去工作顺利，但我更新了我的库，现在我收到这个错误：
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1619, in _create_c_op c_op = c_api.TF_FinishOperation(op_desc)
InvalidArgumentError: Duplicate node name in graph: 'lambda_1/random_normal/shape'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "I:\Documents\Nico\Python\finance\travail_amont\autoencoder_variationnel_bruit.py", line 74, in z = layers.Lambda(sampling)([z_mean, z_log_var])
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper return func(*args, **kwargs)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\engine\base_layer.py", line 506, in call output_shape = self.compute_output_shape(input_shape)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\layers\core.py", line 674, in compute_output_shape x = self.call(xs)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\layers\core.py", line 716, in call return self.function(inputs, **arguments)
File "I:\Documents\Nico\Python\finance\travail_amont\autoencoder_variationnel_bruit.py", line 71, in sampling mean=0., stddev=1.)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\keras\backend\tensorflow_backend.py", line 4329, in random_normal shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\keras\backend.py", line 5602, in random_normal shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\random_ops.py", line 69, in random_normal shape_tensor = tensor_util.shape_tensor(shape)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\tensor_util.py", line 994, in shape_tensor return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1314, in convert_to_tensor ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\array_ops.py", line 1368, in _autopacking_conversion_function return _autopacking_helper(v, dtype, name or "packed")
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\array_ops.py", line 1304, in _autopacking_helper return gen_array_ops.pack(elems_as_tensors, name=scope)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 5704, in pack "Pack", values=values, axis=axis, name=name)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\op_def_library.py", line 742, in _apply_op_helper attrs=attr_protos, op_def=op_def)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 595, in _create_op_internal compute_device)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3322, in _create_op_internal op_def=op_def)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1786, in init control_input_ops)
File "C:\Users\user\AppData\Local\conda\conda\envs\my_root\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1622, in _create_c_op raise ValueError(str(e))
ValueError: Duplicate node name in graph: 'lambda_1/random_normal/shape'
我不知道这个错误：“图中的节点名称重复”。有人知道吗？谢谢