Deep Learning——TensorFlow平台下MNIST的实现（改进)(基于convolutional neural network）

****改进版mnist****采用卷积神经网络

1.下载input_data.py文件 （此文件代码主要是实现下载数据集）

2.切换到python命令模式下

import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  #这里的第一个参数是下载的文件所放的位置
3.下载完成之后，建一个文件test_tensor_flow_mnist_conv.py

#!/usr/bin/env python

import input_data

import tensorflow as tf

mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

sess = tf.InteractiveSession()

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

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("float")

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_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_sum(y_*tf.log(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, "float"))

sess.run(tf.initialize_all_variables())

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})

然后在Linux命令模式下执行该程序

python test_tensor_flow_mnist_conv.py

改进后大有改观，基本在98%以上，不过对机器的要求要是很压力的，本人机子内存32G的跑了2个小时。大家可以把20000改小点！