# tensorflow 在 cpu 下运行 mnist 的例子很慢，正常吗？

import tensorflow as tf
import time
from tensorflow.examples.tutorials.mnist import input_data

batch_size=100
n_batch=mnist.train.num_examples

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):
def max_pool_2x2(x):

x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])

x_image=tf.reshape(x,[-1,28,28,1])

W_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])

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_fcl=weight_variable([7*7*64,1024])
b_fcl=bias_variable([1024])

h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])
h_fcl=tf.nn.relu(tf.matmul(h_pool2_flat,W_fcl)+b_fcl)

keep_prob=tf.placeholder(tf.float32)
h_fcl_frop=tf.nn.dropout(h_fcl,keep_prob)

W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])

prediction=tf.nn.softmax(tf.matmul(h_fcl_frop,W_fc2)+b_fc2)

cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
correct_prediction=tf.equal(tf.argmax(prediction,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())
print("begin to loop 21 times n_batch("+ str(n_batch) + ") Testing")
for epoch in range(21):
for batch in range(n_batch):
if(batch%100==0):
begin=time.time()
print("begin "+ str(batch) + ",Testing")
batch_xs,batch_ys=mnist.train.next_batch(batch_size)
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.7})
if(batch%100==0):
end=time.time()
print("end   "+ str(batch) + ",Testing "+str(end-begin))

acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
print("Iter "+ str(epoch) + ",Testing Accuracy "+str(acc))

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tensorflow安装时候，可以设置为GPU运行的。CPU仅用于研究和学习，对速度就不要有什么指望了，靠一条一条的指令集运算去跑向量矩阵算法本来就难。还有，我记得官方MNIST的例子，使用测试数据进行回归测试时，是用概率算法从2W条样本数据里随机挑选几块数据进行比对的，貌似你这里拿全部数据来验证参数啊。