神经网络喂数据出错

Question

x=tf.placeholder(tf.float32,[None,784],name='x_input')

y_=tf.placeholder(tf.float32,[None,10],name='y_input')

w1=tf.Variable(tf.random_normal([784,100],stddev=1,seed=1))

b1=tf.Variable(tf.random_normal([100],stddev=1))

w2=tf.Variable(tf.random_normal([100,10],stddev=1,seed=2))

b2=tf.Variable(tf.random_normal([10],stddev=1))

middle_y=tf.nn.relu(tf.matmul(x,w1)+b1)

y=tf.matmul(middle_y,w2)+b2

#定义损失函数

#1.神经网络输出值需要怎么处理？

#cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,)))

cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1))

#计算在当前batch中所有样例的交叉熵平均值

cross_entropy_mean=tf.reduce_mean(cross_entropy)

#计算正则化损失函数

REGULARIZATION_RATE=0.0001

regularizer=tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)

regularization=regularizer(w1)+regularizer(w2)

#总损失等于交叉熵损失和正则化损失之和

loss=cross_entropy_mean+regularization

train_step=tf.train.AdamOptimizer(0.001).minimize(loss)

#创建会话执行图

with tf.Session() as sess:

init=tf.global_variables_initializer()

sess.run(init)

#经过i轮迭代

STEPS=30000

#BATCH_SIZE=100

for i in range(STEPS):

batch_size = 100

xs, ys = mnist.train.next_batch(batch_size)    # 从train的集合中选取batch_size个训练数据。

print ("X shape:", xs.shape)                     

print ("Y shape:", ys.shape) 

result=sess.run(train_step,feed_dict={x:xs,y:ys})

if i%1000==0:

print("After %d training steps,tvalidation accuracy using average model is %g "%(STEPS,result))

数据维度没错，为什么会出现上面的错误？

Answer 1

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