Tensorflow: tentative d'utilisation de la valeur non initialisée beta1_power

Question

J'ai eu l'erreur suivante lorsque j'essaie d'exécuter le code à la fin du message. Mais je ne vois pas clairement ce qui ne va pas avec mon code. Quelqu'un pourrait-il me faire savoir les astuces pour déboguer un programme tensorflow?

$ ./main.py 
Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
2017-12-11 22:53:16.061163: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
Traceback (most recent call last):
  File "./main.py", line 55, in <module>
    sess.run(opt, feed_dict={x: batch_x, y: batch_y})
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 889, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1120, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1317, in _do_run
    options, run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1336, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value beta1_power
     [[Node: beta1_power/read = Identity[T=DT_FLOAT, _class=["loc:@Variable"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](beta1_power)]]

Caused by op u'beta1_power/read', defined at:
  File "./main.py", line 46, in <module>
    opt=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 353, in minimize
    name=name)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 474, in apply_gradients
    self._create_slots([_get_variable_for(v) for v in var_list])
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/adam.py", line 130, in _create_slots
    trainable=False)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 1927, in variable
    caching_device=caching_device, name=name, dtype=dtype)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 213, in __init__
    constraint=constraint)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 356, in _init_from_args
    self._snapshot = array_ops.identity(self._variable, name="read")
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 125, in identity
    return gen_array_ops.identity(input, name=name)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2071, in identity
    "Identity", input=input, name=name)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value beta1_power
     [[Node: beta1_power/read = Identity[T=DT_FLOAT, _class=["loc:@Variable"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](beta1_power)]]

Le code est ici. Il utilise LSTM.

#!/usr/bin/env python
# vim: set noexpandtab tabstop=2 shiftwidth=2 softtabstop=-1 fileencoding=utf-8:

import tensorflow as tf
from tensorflow.contrib import rnn

#import mnist dataset
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("/tmp/data/", one_hot=True)

learning_rate=0.001

#defining placeholders
#input image placeholder
time_steps=28
n_input=28
x=tf.placeholder("float", [None, time_steps, n_input])

#processing the input tensor from [batch_size,n_steps,n_input] to "time_steps" number of [batch_size,n_input] tensors
input=tf.unstack(x, time_steps, 1)

#defining the network
num_units=128
lstm_layer = rnn.BasicLSTMCell(num_units, forget_bias=1)
outputs,_ = rnn.static_rnn(lstm_layer, input, dtype="float32")

#weights and biases of appropriate shape to accomplish above task
n_classes=10
out_weights=tf.Variable(tf.random_normal([num_units, n_classes]))
out_bias=tf.Variable(tf.random_normal([n_classes]))

#converting last output of dimension [batch_size,num_units] to [batch_size,n_classes] by out_weight multiplication
prediction=tf.matmul(outputs[-1], out_weights) + out_bias

y=tf.placeholder("float", [None, n_classes])
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
#optimization

#model evaluation
correct_prediction=tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

#initialize variables
init=tf.global_variables_initializer()
batch_size=128
opt=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
with tf.Session() as sess:
    sess.run(init)
    iter=1
    while iter<800:
        batch_x, batch_y = mnist.train.next_batch(batch_size=batch_size)

        batch_x=batch_x.reshape((batch_size, time_steps, n_input))

        sess.run(opt, feed_dict={x: batch_x, y: batch_y})

        if iter %10==0:
            acc=sess.run(accuracy,feed_dict={x:batch_x,y:batch_y})
            los=sess.run(loss,feed_dict={x:batch_x,y:batch_y})
            print("For iter ",iter)
            print("Accuracy ",acc)
            print("Loss ",los)
            print("__________________")

        iter=iter+1


#calculating test accuracy
test_data = mnist.test.images[:128].reshape((-1, time_steps, n_input))
test_label = mnist.test.labels[:128]
print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label}))

Answer 1

Modifiez l'ordre de ces deux lignes:

opt=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
init=tf.global_variables_initializer()

Puisque AdamOptimizer a ses propres variables, vous devez définir l'initiateur initaprèsopt, pas avant.