MNIST

# -*- coding: utf-8 -*- import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets import tensorflow.examples.tutorials.mnist.input_data as input_data mnist=input_data.read_data_sets("MNIST_data/",one_hot=True) sess = tf.InteractiveSession() x = tf.placeholder("float", shape=[None, 784]) y_ = tf.placeholder("float", shape=[None, 10]) w=tf.Variable(tf.zeros([784,10])) b=tf.Variable(tf.zeros([10])) y=tf.nn.softmax(tf.matmul(x,w)*b) #权重初始化 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) #卷积层和池化层的定义 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') #卷积和池化:第一层 W_conv1=weight_Variable([5,5,1,32]) #前两个维度是patch的大小，接着是输入的通道数目，最后是输出的通道数目,每一个输出通道都有一个对应的偏置量 b_conv1=bias_Variable([32]) x_image=tf.reshape(x,[-1,28,28,1]) #[-1,宽，高，颜色通道数] 作为卷积层的输入 h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1) h_pool1=max_pool_2x2(h_conv1) #卷积和池化:第二层 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) #全连接层 W_fc1=weight_Variable([7*7*64,1024]) #1024个神经元的全连接层,why 10247? 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) #Dropout keep_prob=tf.placeholder("float")#用一个placeholder来代表一个神经元的输出在dropout中保持不变的概率 h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob) #输出层 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) #训练和评估模型 #tf.argmax 是一个非常有用的函数，它能给出某个tensor对象在某一维上的其数据最大值所在的索引值, # 由于标签向量是由0,1组成，因此最大值1所在的索引位置就是类别标签比如tf.argmax(y,1)返回的是模型对于任一输入x预测到的标签值， # 而 tf.argmax(y_,1) 代表正确的标签，我们可以用 tf.equal 来检测我们的预测是否真实标签匹配(索引位置一样表示匹配)。 cross_entropy=-tf.reduce_sum(y_*tf.log(y_conv)) train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_predicton=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))# tf.equal 来检测我们的预测是否真实标签匹配(索引位置一样表示匹配) accuracy=tf.reduce_mean(tf.cast(correct_predicton,"float"))# 将correct_predicton转换为float型 sess.run(tf.initialize_all_variables()) for i in range(20000): batch=mnist.train.next_batch(50)# #按批次训练，每批50行数据 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"