错误 - 无法为Tensor提供形状X的值

Question

我使用MNIST数据库在Tensorflow页面上运行了所有示例。现在我试图运行我自己的例子而且我真的没有得到它。

说我有这个csv表：

它有5000行。最后一列是每行的标签，这一行由多个功能组成。 现在我的第一个具体例子。我想在这些数据上训练一个NN，在这里我已经完成了：

    import tensorflow as tf
    import  numpy as np
    import  csv
    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
    # read training data
    Training_file  = open('onetest.csv', 'r', newline='')
    reader = csv.reader(Training_file)
    row = next(reader)
    number_of_rows = 2431
    x = tf.placeholder('float',[None,len(row[:-1])])
    w = tf.Variable(tf.zeros([len(row[:-1]),25]))
    b = tf.Variable(tf.zeros([25]))
    model = tf.add(tf.matmul(x,w),b)
    y_ = tf.placeholder('float',[25,None])
    y =  tf.nn.softmax(model)

    cross_entropy= -tf.reduce_sum(y_*tf.log(y))
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    index =1
    batch_xs =[]
    batch_ys= []
    for row in reader:
        batch_xs.append(row[:-1])
        batch_ys.append(row[-1])
        print(len(batch_xs),len(batch_ys))
        index +=1
        if index%10==0:
            sess.run(train_step,feed_dict={x:batch_xs, y_:batch_ys})
            correct_prediction = tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1))
            accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
            batch_xs.clear();
            batch_ys.clear();

这是我得到的错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-11-4dbaa38c4d9c> in <module>()
     29     index +=1
     30     if index%10==0:
---> 31         sess.run(train_step,feed_dict={x:batch_xs, y_:batch_ys})
     32         correct_prediction = tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1))
     33         accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))

c:\users\engine\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

c:\users\engine\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    942                 'Cannot feed value of shape %r for Tensor %r, '
    943                 'which has shape %r'
--> 944                 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
    945           if not self.graph.is_feedable(subfeed_t):
    946             raise ValueError('Tensor %s may not be fed.' % subfeed_t)

ValueError: Cannot feed value of shape (9,) for Tensor 'Placeholder_17:0', which has shape '(25, ?)'

我已经改变了索引的价值，但它没有解决它，所以我想我误解了一些东西。将不胜感激任何解释。

Answer 1

在此行y_ = tf.placeholder('float',[25,None])中，y_被定义为具有25行（以及任意数量的列）的数据的占位符。然后在您的代码中，由于if index%10==0:行batch_ys有10行，这就是您收到此错误的原因。

Answer 2

所以我明白了，这是鳕鱼，它可能会帮助那里的人：

    import tensorflow as tf
import  numpy as np
import  csv
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# read training data
Training_file  = open('onetest.csv', 'r', newline='')
reader = csv.reader(Training_file)
row = next(reader)

x = tf.placeholder('float',[None,len(row[:-1])])
w = tf.Variable(tf.zeros([len(row[:-1]),25]))
b = tf.Variable(tf.zeros([25]))
model = tf.add(tf.matmul(x,w),b)
y_ = tf.placeholder('float',[None,25])
y =  tf.nn.softmax(model)
print(y)

cross_entropy= -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
index =0
batch_xs =[]
ys= []
for row in reader:
    batch_xs.append(row[:-1])
    ys.append(row[-1])

    index +=1
    if index%25==0:
            batch_ys = np.reshape(ys,(1,25))

            sess.run(train_step,feed_dict={x:batch_xs,y_:batch_ys})
            correct_prediction = tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1))
            accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
            print(sess.run(accuracy,feed_dict={x:batch_xs,y_:batch_ys}))
            batch_xs.clear()
            ys.clear()