#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Jun 2 09:54:06 2017 @author: xyzheng """ import tensorflow as tf 加载数据集 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) sess = tf.InteractiveSession() in_units = 784 h1_units = 300 w1 = tf.Variable(tf.truncated_normal([in_units,h1_units],stddev=0.1)) b1 = tf.Variable(tf.zeros([h1_units])) w2 = tf.Variable(tf.zeros([h1_units,10])) b2 = tf.Variable(tf.zeros([10])) x = tf.placeholder(tf.float32,[None,in_units]) keep_prob = tf.placeholder(tf.float32) 定义模型结构 hidden1 = tf.nn.relu(tf.matmul(x,w1)+b1) hidden1_drop = tf.nn.dropout(hidden1,keep_prob) y = tf.nn.softmax(tf.matmul(hidden1_drop,w2) + b2) 定义损失函数和选择优化器来优化loss y_ = tf.placeholder(tf.float32,[None,10]) cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y),reduction_indices = [1])) train_step = tf.train.AdagradOptimizer(0,3).minimize(cross_entropy) tf.global_variables_initializer().run() for i in range(3000): batch_xs, batch_ys = mnist.train.next_batch(100) train_step.run({x: batch_xs, y_: batch_ys,keep_prob:0.75}) correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))版权声明：本文内容由互联网用户自发贡献，该文观点仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请联系我们举报，一经查实，本站将立刻删除。
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