使用tf.merge_all_summaries()时TensorFlow:PlaceHolder错误

Question

我收到占位符错误.

我不知道它意味着什么,因为我正确映射sess.run(..., {_y: y, _X: X})...我在这里提供了一个功能齐全的MWE重现错误:

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()

如果我从中删除tf.merge_all_summaries()或删除它运行正常.mergedself.sess.run([o, merged], ...)

这看起来类似于这篇文章: 在TensorFlow中计算摘要时出错 但是,我没有使用iPython ...

Answer 1

该tf.merge_all_summaries()函数很方便,但也有点危险:它将默认图形中的所有摘要合并,其中包括以前显然未连接的代码调用的任何摘要,这些代码也将摘要节点添加到默认图形中.如果旧的摘要节点依赖于旧的占位符,您将收到错误,例如您在问题中显示的错误(以及之前的 问题).

有两个独立的解决方法:

1. 确保明确收集要计算的摘要.这与tf.merge_summary()在示例中使用显式op 一样简单:

   accuracy_summary = tf.scalar_summary("accuracy", accuracy)
   loss_summary = tf.scalar_summary("loss", C)
   
   merged = tf.merge_summary([accuracy_summary, loss_summary])
   

2. 确保每次创建新的摘要集时,都可以在新图表中执行此操作.建议的样式是使用显式默认图:

   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()以重置图形的状态并删除任何旧的摘要节点.