当我尝试在帖子末尾运行代码时出现以下错误。但我不清楚我的代码有什么问题。有人能告诉我调试张量流程序的技巧吗？
$ ./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)]]
代码在这里。它使用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}))
答案 0 :(得分：34)
更改这两行的顺序：
opt=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
init=tf.global_variables_initializer()
由于AdamOptimizer拥有自己的变量，您应该在 init之后定义启动器opt ，而不是之前的 >