阿里云 mnist

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


start_time = time.time()

parser = argparse.ArgumentParser()

parser.add_argument('--buckets',type=str,default='',help='input data path')

parser.add_argument('--checkpointDir',type=str,default='',help='output model path')

FLAGS, _ = parser.parse_known_args()


mnist = input_data.read_data_sets(FLAGS.buckets, one_hot=True)


sess = tf.InteractiveSession()

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

y_ = tf.placeholder(tf.float32, shape=[None,10])

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

def weights_variable(shape):
return tf.Variable(tf.truncated_normal(shape,stddev=0.1))

def bias_variable(shape):
return tf.Variable(tf.constant(0.1,shape=shape))

def conv2d(x,W):
return tf.nn.conv2d(input=x,filter=W,strides=[1,1,1,1],padding='SAME')

def max_pool_2x2(x):
return tf.nn.max_pool(value=x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')


W_conv1 = weights_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 = weights_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 = weights_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 = weights_variable([1024,10])

b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)


cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv),reduction_indices=[1]))

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))


tf.global_variables_initializer().run()

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}))#0.9926
print('cost time:',time.time() - start_time)#115s