先贴报错信息（部分，y_i调用的部分都报错了，选了一处贴上）：
Caused by op 'y_input', defined at:
File "D:/Anaconda3/envs/tensorflow/untitled1/Mnist.py", line 87, in <module>
tf.app.run()
File "D:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 124, in run
_sys.exit(main(argv))
File "D:/Anaconda3/envs/tensorflow/untitled1/Mnist.py", line 83, in main
train(mnist)
File "D:/Anaconda3/envs/tensorflow/untitled1/Mnist.py", line 32, in train
y_i = tf.placeholder(tf.float32, shape=[None, OUTPUT_NODE], name="y_input")
File "D:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1680, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "D:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 4105, in _placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "D:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "D:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 3160, in create_op
op_def=op_def)
File "D:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 1625, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'y_input' with dtype float and shape [?,10]
[[Node: y_input = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
源代码：
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference
import os
import requests
from requests.adapters import HTTPAdapter
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH="MNIST_model/"
MODEL_NAME="mnist_model"
def train(mnist):
x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')
regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
y = mnist_inference.inference(x, regularizer)
global_step = tf.Variable(0, trainable=False)
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.argmax(y_, 1), logits=y)
cross_entropy_mean = tf.reduce_mean(cross_entropy)
loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
learning_rate = tf.train.exponential_decay(
LEARNING_RATE_BASE,
global_step,
mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,
staircase=True)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
with tf.control_dependencies([train_step, variables_averages_op]):
train_op = tf.no_op(name='train')
saver = tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()
for i in range(TRAINING_STEPS):
xs, ys = mnist.train.next_batch(BATCH_SIZE)
_, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})
if i % 1000 == 0:
print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)
def main(argv=None):
mnist = input_data.read_data_sets("../../../datasets/MNIST_data", one_hot=True)
train(mnist)
if __name__ == '__main__':
tf.app.run()

# 迭代优化模型#
#可以帮忙解释一下，下面代码的功能含义吗
for i in range(20000):
# 每次取50个样本进行训练
batch = mnist.train.next_batch(50)
if i%200 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob: 1.0}) # 模型中间不使用dropout
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}))
可以帮忙解释下吗谢谢大佬们了

我同样遇到了这样的问题，

你的问题解决了吗？

同求 应该是placeholder的问题
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype int32 and shape [64]
[[Node: Placeholder_1 = Placeholder[dtype=DT_INT32, shape=[64], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

楼主，给我也发一份，我也遇到这个问题了，不知道怎么解决？？

楼主，请问这个问题解决了吗？被这个问题困扰了好多天，还没解决，求解决方法，谢谢