new bug in a variational autoencoder (keras)

Question

I used to use this code to train variational autoencoder (I found the code on a forum and adapted it to my needs) :

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))

it used to work smoothly but I have updated my librairies and now I get this error :

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'

I do not know this error : "Duplicate node name in graph". Does anyone has a clue ? Thanks.

Innat · Accepted Answer · 2021-05-18 06:50:08Z

2

If you're using tf 2.x, then import your keras modules as follows.

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.kerasimport backend as K
from tensorflow.keras.models import Model

More related on this, #36509, #130

answered May 18, 2021 at 6:50

Innat

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2 Comments

johnnyp Over a year ago

Thanks for the answer, it does solve one part of the issue but then I run into a "TypeError: An op outside of the function building code is being passed a "Graph" tensor.". My script was OK with tf 1.14 but it seems it does not work anymore with tf 2.1. The error is raised at the "fit" step.

johnnyp Over a year ago

OK, I have the answer even if I do not fully undersatnd it : it works alright with only the change in the imports for the line : from tensorflow.keras import backend as K, the rest should be kept as before.

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