# -*- coding: utf-8 -*- """ Created on Mon Sep 11 10:16:34 2017 single layer softmax regression @author: Wangjc code from TensorFlow """ import tensorflow.examples.tutorials.mnist.input_data as input_data #need to show the full address, or error occus. mnist = input_data.read_data_sets('MNIST_data', one_hot=True) #use read_data_sets to download and load the mnist data set. if has the data, then load. #need a long time about 5 minutes import tensorflow as tf sess = tf.InteractiveSession() #link the back-end of C++ to compute. #in norm cases, we should create the map and then run in the sussion. #now, use a more convenient class named InteractiveSession which could insert compute map when running map. x=tf.placeholder("float",shape=[None,784]) y_=tf.placeholder("float",shape=[None,10]) #x for input date,28*28 #y_ for the classfication w=tf.Variable(tf.zeros([784,10])) b=tf.Variable(tf.zeros([10])) #w for weight:784 input and 10 output #b for bias:10 output sess.run(tf.initialize_all_variables()) #initial the variables y=tf.nn.softmax(tf.matmul(x,w)+b) #predict the probablity of the compute result cross_entropy=-tf.reduce_sum(y_*tf.log(y)) #compute the cross entry by reduce_sum method. train_step=tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) #find the loss Gradient by Automatic differentiation through the minimal of cross entropy by step of 0.01 for i in range(1000): batch=mnist.train.next_batch(50) train_step.run(feed_dict={x:batch[0],y_:batch[1]}) # in every iteration, load 50 samples, and run train_step once. # place the train data to placeholder by feed_dict correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) #see if the predict is equal to the compute result #argmax return the max value of an array in the assigned dimention accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) #conver bool to float,and calculate the accuracy print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})) #evaluate the accuracy
多层神经网络代码:
# -*- coding: utf-8 -*- """ Created on Mon Sep 11 10:16:34 2017 multy layers softmax regression @author: Wangjc code from TensorFlow """ import tensorflow as tf import tensorflow.examples.tutorials.mnist.input_data as input_data #need to show the full address, or error occus. mnist = input_data.read_data_sets('MNIST_data', one_hot=True) #use read_data_sets to download and load the mnist data set. if has the data, then load. #need a long time about 5 minutes sess = tf.InteractiveSession() #link the back-end of C++ to compute. #in norm cases, we should create the map and then run in the sussion. #now, use a more convenient class named InteractiveSession which could insert compute map when running map. x=tf.placeholder("float",shape=[None,784]) y_=tf.placeholder("float",shape=[None,10]) def weight_variable(shape): #use normal distribution numbers with stddev 0.1 to initial the weight initial=tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): #use constant value of 0.1 to initial the bias initial=tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x,W): #convolution by filter of W,with step size of 1, 0 padding size #x should have the dimension of [batch,height,width,channels] #other dimension of strides or ksize is the same with x return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') def max_pool_2x2(x): #pool by windows of ksize,with step size of 2, 0 padding size 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]) #build the first conv layer: #get 32 features from every 5*5 patch, so the shape is [5,5,1(channel),32] x_image = tf.reshape(x, [-1,28,28,1]) #to use conv1, need to convert x to 4D, in form of [batch,height,width,channels] # -1 means default 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]) #build the 2nd conv layer: #get 64 features from every 5*5 patch, so the shape is [5,5,32(channel),64] h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) #---------------------------------------- #image size reduce to 7*7 by pooling #we add a full connect layer contains 1027 nuere #need to flat pool tensor for caculate 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) #------------------------------------ #output layer keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob) #to decrease overfit, we add dropout before output layer. #use placeholder to represent the porbability of a neure's output value unchange 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) #--------------------------------- #train and evaluate the module #use a ADAM 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(5000): 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}))
可参考,解释代码较详细
中间结果可视化,可参考