Tensorflow: Versuch, nicht initialisierten Wert beta1_power zu verwenden

Question

Ich habe den folgenden Fehler, wenn ich versuche, den Code am Ende des Beitrags ausführen. Aber mir ist nicht klar, was mit meinem Code nicht stimmt. Kann mir jemand die Tricks beim Debuggen eines Tensorflow-Programms erklären?

$ ./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)]] 

Der Code ist hier. Es verwendet 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

Ändern Sie die Reihenfolge dieser beiden Linien:

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

Da AdamOptimizer hat seine eigenen Variablen ist, sollten Sie die initilizer initnachopt, nicht vor definieren.