Python Tensorflow MNIST标签占位符形状不匹配错误

我已经为MNIST分类训练编写了以下代码，当我运行它时抛出

placeholder

形状不匹配错误

from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None,10])
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')
 W_conv1 = weight_variable([5, 5, 1, 32])
 b_conv1 = bias_variable([32])
 x_image = tf.reshape(x, [-1, 28, 28, 1])
 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
 h_pool1 = max_pool_2x2(h_conv1)
 W_conv2 = weight_variable([5, 5, 32, 64])
 b_conv2 = bias_variable([64])
 h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
 h_pool2 = max_pool_2x2(h_conv2)
 W_fc1 = weight_variable([7 * 7 * 64, 1024])
 b_fc1 = bias_variable([1024])
 h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
 keep_prob = tf.placeholder(tf.float32)
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 W_fc2 = weight_variable([1024, 10])
 b_fc2 = bias_variable([10])
 y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
     for i in range(20000):
         batch = mnist.train.next_batch(50)
         if i % 100 == 0:
             train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
             print('step %d, training accuracy %g' % (i, train_accuracy))
         train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
     print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

当我运行训练时，它抛出以下错误

ValueError:无法为具有形状“（？，10）”的张量“占位符_1:0”提供形状（50，）的值

换线

mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)

到

换线

mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)

到