python - 在 TensorFlow 中计算摘要时出错

Question

我正在尝试使用 TensorFlow 生成摘要并使用 TensorBoard 将它们可视化。但是，我收到一个我不明白的错误(InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float)。

这是我程序的完整源代码:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)

_ = tf.histogram_summary("weights", W)
_ = tf.histogram_summary("biases", b)
_ = tf.histogram_summary("y", y)


y_ = tf.placeholder(tf.float32, [None, 10])

with tf.name_scope("xent") as scope:
    cross_entropy = -tf.reduce_sum(y_*tf.log(y))
    _ = tf.scalar_summary("cross entropy", cross_entropy)

with tf.name_scope("train") as scope:
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.initialize_all_variables()

sess =  tf.Session()
sess.run(init)

with tf.name_scope("test") as scope:
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    _ = tf.scalar_summary("accuracy", accuracy)

merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter("/tmp/mnist_nn", sess.graph_def)

for i in range(1000):
    if (i % 10) == 0:
        feed = {x: mnist.test.images, y_: mnist.test.labels}
        result = sess.run([merged, accuracy], feed_dict=feed) 
        summary_str = result[0]
        acc = result[1]
        print("Accuracy at step %s: %s" % (i, acc))
    else:
        batch_xs, batch_ys = mnist.train.next_batch(100)
        feed = {x: batch_xs, y_: batch_ys}
        sess.run(train_step, feed_dict=feed)

print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))

但是，当我尝试运行上面的代码时，出现了以下错误:

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-23-584a7bc91816> in <module>()
     39     if (i % 10) == 0:
     40         feed = {x: mnist.test.images, y_: mnist.test.labels}
---> 41         result = sess.run([merged, accuracy], feed_dict=feed)
     42         summary_str = result[0]
     43         acc = result[1]

/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict)
    366 
    367     # Run request and get response.
--> 368     results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
    369 
    370     # User may have fetched the same tensor multiple times, but we

/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, target_list, fetch_list, feed_dict)
    442         # pylint: disable=protected-access
    443         raise errors._make_specific_exception(node_def, op, error_message,
--> 444                                               e.code)
    445         # pylint: enable=protected-access
    446       six.reraise(e_type, e_value, e_traceback)

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:

从错误来看，我的源代码中的占位符似乎没有被提供适当的值。据我所知，我正在为所有占位符(x 和 y_)提供值。

如果您需要，我会将完整的日志添加到这个问题。

我还发现，当我第一次获取 mnist 时，它确实有效(具有以下输出)但仍然没有生成 TensorBoard 可视化:

Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting MNIST_data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
Tensor("MergeSummary/MergeSummary:0", shape=TensorShape([]), dtype=string)
merged
Accuracy at step 0: 0.098
Accuracy at step 10: 0.7404
Accuracy at step 20: 0.8041
Accuracy at step 30: 0.814 ...

Answer 1

从您的错误消息来看，您似乎正在使用 IPython。使用 IPython 构建 TensorFlow 模型时的一个陷阱是函数类似于 tf.merge_all_summaries()将记住在当前 session 中创建的每个摘要，包括因错误而失败的单元格。这是 TensorFlow 使用默认图 收集流程中创建的所有操作、摘要等的结果，除非您明确指定图。我怀疑您对 tf.merge_all_summaries() 的调用返回的不仅仅是您在代码中创建的三个直方图摘要，而较旧的摘要将依赖于先前创建 占位符。

有两种主要的方法可以解决这个问题。最简单的是显式合并摘要，而不是使用 tf.merge_all_summaries():

weights_summary = tf.histogram_summary("weights", W)
biases_summary = tf.histogram_summary("biases", b)
y_summary = tf.histogram_summary("y", y)

merged = tf.merge_summary([weights_summary, biases_summary, y_summary])

另一种方法是在构建模型之前设置一个显式默认图。如果您想跨多个 IPython 单元拆分模型，这会很尴尬，但应该也可以:

# Sets a new default graph, and stores it in `g`.
with tf.Graph().as_default() as g:  

    x = tf.placeholder(tf.float32, [None, 784])
    # ...

    # Summaries are added to `g`.
    _ = tf.histogram_summary("weights", W)
    _ = tf.histogram_summary("biases", b)
    _ = tf.histogram_summary("y", y)

    # ...

    # `merged` contains only summaries from `g`.
    merged = tf.merge_all_summaries()

    # ...