python - 为什么 TensorFlow 会提示我将错误的形状和类型输入到占位符中？

Question

我无法弄清楚这一点。我一直在反复思考，我知道我可以复制并粘贴工作教程，但我想了解为什么这不起作用。

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data


mnist = input_data.read_data_sets('MNIST_data', one_hot=True)   

#simple constants
learning_rate = .01
batch_size = 100
training_epoch = 10
t = 0
l = t

#gather the data
x_train = mnist.train.images
y_train = mnist.train.labels
batch_count = int(len(x_train)/batch_size)

#Set the variables
Y_ = tf.placeholder(tf.float32, [None,10], name = 'Labels')
X = tf.placeholder(tf.float32,[None,784], name = 'Inputs')
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

#Build the graph (Y = WX + b)
Y = tf.nn.softmax(tf.matmul(X,W) + b, name = 'softmax')

cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y)) * 1000.0

correct_prediction = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy) 

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())


    for epoch in range(training_epoch):
        for i in range(batch_count):
            t += batch_size
            print(y_train[l:t].shape)
            print(x_train[l:t].shape)
            print(y_train[l:t].dtype)
            sess.run(train_step,feed_dict={X: x_train[l:t], Y: y_train[l:t]})
            l = t
        print('Epoch = ', epoch)
    print("Accuracy: ", accuracy.eval(feed_dict={X: x_test, Y_: y_test})) 
    print('Done') 

错误信息:

InvalidArgumentError: You must feed a value for placeholder tensor 'Labels_2' with dtype float and shape [?,10]
     [[Node: Labels_2 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device="/job:localhost/replica:0/task:0/device:GPU:0"]

我也明白，为了让它发挥作用，我需要添加的东西比我需要的多，但我现在想自己努力解决这个问题。我在 jupyter 笔记本上运行它。我确信 y_train 具有形状 (100, 10) 和类型 float64。

我已经被困了几天了，非常感谢您的帮助。

Answer 1

将这一行中的 Y 更改为 Y_:

sess.run(train_step,feed_dict={X: x_train[l:t], Y_: y_train[l:t]})