TensorFlow 无法为 Tensor 'Placeholder:0' 提供形状值 (100, 784) [英] TensorFlow Cannot feed value of shape (100, 784) for Tensor 'Placeholder:0'

问题描述

我正在学习 TensorFlow.因此，为了了解如何制作某些东西，我尝试从源代码中复制一些代码并执行它.但是我遇到了错误消息.所以我尝试了这个网站的一些解决方案，但它不起作用(我在评论中保留了我的测试).

I am learning TensorFLow. So to understand how to make something, I tried to copy some code from a source and execute it. But I'm hitting an error message. So I tried some solution from this website but it does not work (I kept my test in comments).

    """programme 1 """
import tensorflow as tf 
import numpy as np

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





X = tf.placeholder(tf.float32,[None, 28, 28, 1]) #28 * 28 taille image 1 = 1pixel car noir et blanc "X" valeur
W = tf.Variable(tf.zeros([784, 10])) # 28*28 = 784 , 10 -> 0 à 9  "W" = weight = poid
b = tf.Variable(tf.zeros([10])) #chiffre de 0 à 9 a reconnaitre "b" = constante 
init = tf.initialize_all_variables()

#model
Y = tf.nn.softmax(tf.matmul(tf.reshape(X,[-1, 784]), W) + b) #fonction "matmul": produit matriciel "-1": reussite obligatoire

#Place holder
Y_ = tf.placeholder(tf.float32, [None, 10])

#loss function
cross_entropy = -1 * tf.reduce_sum(Y_ * tf.log(Y)) #formule

# % of correct annwer found in batch
is_correct = tf.equal(tf.argmax(Y,1),tf.argmax(Y_,1))
accuracy = tf.reduce_mean(tf.cast(is_correct,tf.float32))

#training step
optimizer = tf.train.GradientDescentOptimizer(0.003) #petit pas
train_step = optimizer.minimize(cross_entropy)

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

for i in range(10000):
    #load batch of image and ocrrects answer
    batch_X, batch_Y = mnist.train.next_batch(100)
    batch_X = np.reshape(batch_X, (-1, 784))
    #batch_Y = np.reshape(batch_Y, (-1, 784))

    train_data = {X: batch_X, Y_: batch_Y}

    #train
    sess.run(train_step, feed_dict=train_data)

    a,c = sess.run([accuracy,cross_entropy],feed = train_data)

    test_data = {X:mnist.test.images, Y_:mnist.test.labels}
    a,c = sess.run([accuracy,cross_entropy],feed = test_data)

日志:

    Traceback (most recent call last):
  File "d:	ensorflow	est1.py", line 46, in <module>
    sess.run(train_step, feed_dict=train_data)
  File "C:UsersProprietaireAppDataLocalProgramsPythonPython35libsite-packages	ensorflowpythonclientsession.py", line 895, in run
    run_metadata_ptr)
  File "C:UsersProprietaireAppDataLocalProgramsPythonPython35libsite-packages	ensorflowpythonclientsession.py", line 1100, in _run
    % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (100, 784) for Tensor 'Placeholder:0', which has shape '(?, 28, 28, 1)'
2017-08-30 19:07:37.406994: W C:	f_jenkinshomeworkspace
el-winMwindowsPY35	ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.

第 46 行是

sess.run(train_step, feed_dict=train_data)

我该怎么做才能解决这个错误?

What can I do to resolve this error?

推荐答案

您收到该错误是因为您输入的内容与 TensorFlow 预期的内容不匹配.要解决此问题，您可能需要将 placeholder:0 处的数据重新整形，即 batch_X(?, 28, 28, 1).例如，您将执行以下操作:

You are getting that error because there is a mismatch between the shape of what you are feeding in and what the TensorFlow is expecting. To fix the issue, you might want to reshape your data at placeholder:0 which is batch_X to (?, 28, 28, 1). For example, you would do the following:

batch_X = np.reshape(batch_X, (-1, 28, 28, 1))

这篇关于TensorFlow 无法为 Tensor 'Placeholder:0' 提供形状值 (100, 784)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IT屋！