Tensorflow训练mnist数据集源代码解析

最近一直在看tensorflow，记录一下自己训练mnist数据集的代码，可以直接运行使用！

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt     
import scipy.misc
import matplotlib.image as mpimg
from skimage import io

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

x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
sess = tf.InteractiveSession()



def weight_variable(shape):
#这里是构建初始变量
  initial = tf.truncated_normal(shape, mean=0,stddev=0.1)
#创建变量
  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])

#调整x的大小
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])

#对h_pool2数据进行铺平
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
#进行relu计算，matmul表示(wx+b)计算
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 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

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


cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

train_step = tf.train.AdamOptimizer(1e-3).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))
#初始化变量
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for i in range(20000):
  batch = mnist.train.next_batch(10)
  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})

#保存模型
save_path = saver.save(sess, "./model/save_net.ckpt")

print("test accuracy %g"%accuracy.eval(feed_dict={
    x: mnist.test.images[:3000], y_: mnist.test.labels[:3000], keep_prob: 1.0}))