问题描述
我收到一个占位符错误.
I am getting a placeholder error.
我不知道这是什么意思，因为我在sess.run(..., {_y: y, _X: X})上正确映射了...在这里，我提供了一个功能齐全的MWE来再现错误:
I do not know what it means, because I am mapping correctly on sess.run(..., {_y: y, _X: X})... I provide here a fully functional MWE reproducing the error:
import tensorflow as tf
import numpy as np
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
class NeuralNet:
def __init__(self, hidden):
self.hidden = hidden
def __del__(self):
self.sess.close()
def fit(self, X, y):
_X = tf.placeholder('float', [None, None])
_y = tf.placeholder('float', [None, 1])
w0 = init_weights([X.shape[1], self.hidden])
b0 = tf.Variable(tf.zeros([self.hidden]))
w1 = init_weights([self.hidden, 1])
b1 = tf.Variable(tf.zeros([1]))
self.sess = tf.Session()
self.sess.run(tf.initialize_all_variables())
h = tf.nn.sigmoid(tf.matmul(_X, w0) + b0)
self.yp = tf.nn.sigmoid(tf.matmul(h, w1) + b1)
C = tf.reduce_mean(tf.square(self.yp - y))
o = tf.train.GradientDescentOptimizer(0.5).minimize(C)
correct = tf.equal(tf.argmax(_y, 1), tf.argmax(self.yp, 1))
accuracy = tf.reduce_mean(tf.cast(correct, "float"))
tf.scalar_summary("accuracy", accuracy)
tf.scalar_summary("loss", C)
merged = tf.merge_all_summaries()
import shutil
shutil.rmtree('logs')
writer = tf.train.SummaryWriter('logs', self.sess.graph_def)
for i in xrange(1000+1):
if i % 100 == 0:
res = self.sess.run([o, merged], feed_dict={_X: X, _y: y})
else:
self.sess.run(o, feed_dict={_X: X, _y: y})
return self
def predict(self, X):
yp = self.sess.run(self.yp, feed_dict={_X: X})
return (yp >= 0.5).astype(int)
X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1]])
y = np.array([[0],[1],[1],[0]]])
m = NeuralNet(10)
m.fit(X, y)
yp = m.predict(X)[:, 0]
print accuracy_score(y, yp)
错误:
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 8
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 8
0.847222222222
W tensorflow/core/common_runtime/executor.cc:1076] 0x2340f40 Compute status: Invalid argument: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float
[[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
W tensorflow/core/common_runtime/executor.cc:1076] 0x2340f40 Compute status: Invalid argument: You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Traceback (most recent call last):
File "neuralnet.py", line 64, in <module>
m.fit(X[tr], y[tr, np.newaxis])
File "neuralnet.py", line 44, in fit
res = self.sess.run([o, merged], feed_dict={self._X: X, _y: y})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 368, in run
results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 444, in _do_run
e.code)
tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float
[[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op u'Placeholder_1', defined at:
File "neuralnet.py", line 64, in <module>
m.fit(X[tr], y[tr, np.newaxis])
File "neuralnet.py", line 16, in fit
_y = tf.placeholder('float', [None, 1])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 673, in placeholder
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 463, in _placeholder
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 664, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1834, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1043, in __init__
self._traceback = _extract_stack()
如果我从self.sess.run([o, merged], ...)中删除tf.merge_all_summaries()或删除merged，那么它运行正常.
If I remove the tf.merge_all_summaries() or remove merged from self.sess.run([o, merged], ...) then it runs okay.
这看起来类似于这篇文章:在TensorFlow中计算汇总时出错但是，我没有使用iPython ...
This looks similar to this post:Error when computing summaries in TensorFlowHowever, I am not using iPython...
推荐答案
tf.merge_all_summaries()函数很方便，但也有些危险:它将所有摘要合并到默认图中，其中包括所有摘要来自以前的#显然未连接的—代码的调用，这些代码也将摘要节点添加到了默认图形中.如果旧的摘要节点依赖于旧的占位符，则会出现类似问题中显示的错误(以及上一个)的错误. > 问题.
The tf.merge_all_summaries() function is convenient, but also somewhat dangerous: it merges all summaries in the default graph, which includes any summaries from previous—apparently unconnected—invocations of code that also added summary nodes to the default graph. If old summary nodes depend on an old placeholder, you will get errors like the one you have shown in your question (and like previous questions as well).
有两种独立的解决方法:
There are two independent workarounds:
-
确保您明确收集要计算的摘要.这就像使用显式
tf.merge_summary()在您的示例中:
Ensure that you explicitly collect the summaries that you wish to compute. This is as simple as using the explicit
tf.merge_summary()op in your example:
accuracy_summary = tf.scalar_summary("accuracy", accuracy)
loss_summary = tf.scalar_summary("loss", C)
merged = tf.merge_summary([accuracy_summary, loss_summary])
确保每次创建新的摘要集时，都在新图中进行.推荐的样式是使用显式的默认图形:
Ensure that each time you create a new set of summaries, you do so in a new graph. The recommended style is to use an explicit default graph:
with tf.Graph().as_default():
# Build model and create session in this scope.
#
# Only summary nodes created in this scope will be returned by a call to
# `tf.merge_all_summaries()`
或者，如果您使用的是TensorFlow的最新开源版本(或即将发布的0.7.0版本)，则可以调用 tf.reset_default_graph() 重置图的状态并删除所有旧的摘要节点.
Alternatively, if you are using the latest open-source version of TensorFlow (or the forthcoming 0.7.0 release), you can call tf.reset_default_graph() to reset the state of the graph and remove any old summary nodes.
这篇关于TensorFlow:使用tf.merge_all_summaries()时出现PlaceHolder错误的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！