from __future__ import print_function from tensorflow.examples.tutorials.mnist import input_data #加载数据集 mnist = input_data.read_data_sets(r"C:/Users/HPBY/tem/data/",one_hot=True)#加载本地数据 以独热编码形式 import tensorflow as tf #设置超参 learning_rate = 0.01 #设置学习率 num_step = 500 #训练次数 batch_size = 128 #批次 display_step =100 #多少次显示一次结果 #设置网络参数 n_hidden_1 = 256 #隐含层1 256节点 n_hidden_2 = 256 #隐含层2 256节点 num_inputs = 784 #输入一位向量28*28 num_class = 10 #0-9的数字一共10个分类 X = tf.placeholder("float",[None, num_inputs]#占位符784输入 10输出 Y = tf.placeholder("float",[None, num_class]) # 储存网络层权重和偏置值 weights={#随机初始化并权重和偏置值 'h1' : tf.Variable(tf.random_normal([num_inputs, n_hidden_1])), 'h2' : tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 'out' : tf.Variable(tf.random_normal([n_hidden_2, num_class])) } biases = { 'b1' : tf.Variable(tf.random_normal([n_hidden_1])), 'b2' : tf.Variable(tf.random_normal([n_hidden_2])), 'out' :tf.Variable(tf.random_normal([num_class])) } #创建模型 def neural_net(x): #全连接隐含层1,2隐含层256个节点 layer_1 = tf.add(tf.matmul(x,weights['h1']), biases['b1'])#matmul是计算 layer_2 = tf.add(tf.matmul(layer_1, weights['h2']),biases['b2']) out_layer = tf.matmul(layer_2, weights['out'])+biases['out'] return out_layer #构建模型 logits = neural_net(X) #定义损失函数和优化器 loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=Y)) opt = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = opt.minimize(loss_op) #评价模型 correct_pred = tf.equal(tf.argmax(logits,1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) #初始化变量 init = tf.global_variables_initializer() #开始训练 with tf.Session() as sess: sess.run(init) for step in range(1,num_step+1): batch_x, batch_y =mnist.train.next_batch(batch_size) sess.run(train_op,feed_dict={X:batch_x, Y:batch_y}) if step % display_step == 0 or step == 1: loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y}) print("Step " + str(step) + ", Minibatch Loss= " + \ "{:.4f}".format(loss) + ", Training Accuracy= " + \ "{:.3f}".format(acc)) print("Optimization Finished!") # Calculate accuracy for MNIST test images print("Testing Accuracy:", \ sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels}))
数据集来自 http://yann.lecun.com/exdb/mnist/ 以本地加载方式加载数据集
神经网络模型如下：
独热编码参考https://www.cnblogs.com/zongfa/p/9305657.html
很简单的一种编码方式也经常用到
比如我们有“今天刀塔本子出了吗”这个形式的9个不同的词，那么我们独热编码就会形成一个九维的向量，
今是第1个词表示的向量为[1,0,0,0,0,0,0,0,0]
刀是第3个词表示的向量为[0,0,1,0,0,0,0,0,0]
神经网络原理与推导参考程序媛小姐姐的BP神经网络讲解，非常详细：http://www.cnblogs.com/charlotte77/p/5629865.html
