mnist.py

#!/usr/bin/env python
# _*_ coding:utf-8 _*_

"""
@author: JiangZongKang
@contact: top_jzk@163.com
@file: mnist.py
@time: 2018/9/10 16:55

"""
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'


FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('is_train', 0, '指定程序是预测还是训练')


def full_connected():

    # 获取真实的数据
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
    # 1、建立数据的占位符  x [None, 784] y_true [None, 10]
    with tf.variable_scope('data'):
        x = tf.placeholder(tf.float32, [None, 784])
        print(x)
        y_true = tf.placeholder(tf.int32, [None, 10])
    # 2、建立一个全连接层的神经网络 w [784, 10] b [10]
    with tf.variable_scope('full_model'):
        # 随机初始化权重和偏置
        weight = tf.Variable(tf.random_normal([784, 10], name='weight'))
        bias = tf.Variable(tf.constant(0.0, shape=[10]))

        # 预测None个样本的输出结果 matrix [None, 784]*[784, 10] +[10] = [None, 10]
        y_predict = tf.matmul(x, weight) + bias

    # 求出所有样本的损失，然后求平均值
    with tf.variable_scope('soft_cross'):
        # 求平均交叉熵损失
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
            labels=y_true, logits=y_predict))

    # 梯度下降求出损失
    with tf.variable_scope('optimizer'):
        train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

    # 计算准确率
    with tf.variable_scope('acc'):
        equal_list = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_predict, 1))
        accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))

    # 收集变量，单个数字值收集
    tf.summary.scalar('losses', loss)
    tf.summary.scalar('acc', accuracy)

    # 高纬度变量收集
    tf.summary.histogram('weigthes', weight)
    tf.summary.histogram('biases', bias)

    # 定义一个初始化变量的op
    init_op = tf.global_variables_initializer()

    # 定义一个合并变量的op
    merged = tf.summary.merge_all()

    # 创建一个saver
    saver = tf.train.Saver()

    # 开启会话去训练
    with tf.Session() as sess:
        # 初始化变量
        sess.run(init_op)

        # 建立events 文件，然后写入
        filewriter = tf.summary.FileWriter(
            './tmp/summary/test/', graph=sess.graph)
        # 迭代步数去训练，更新参数来预测
        if FLAGS.is_train == 1:
            for i in range(2000):
                # 取出真实存在的目标值和特征值
                mnist_x, mnist_y = mnist.train.next_batch(50)
                # 运行train_op 训练
                sess.run(train_op, feed_dict={x: mnist_x, y_true: mnist_y})

                # 写入每步训练的值
                summary = sess.run(merged, feed_dict={
                                   x: mnist_x, y_true: mnist_y})
                filewriter.add_summary(summary, i)
                print('训练第%d步，准确率为：%f' % (i, sess.run(
                    accuracy, feed_dict={x: mnist_x, y_true: mnist_y})))

            # 保存模型
            saver.save(sess, './tmp/summary/test/fc_model')
        else:
            # 加载模型
            saver.restore(sess, './tmp/summary/test/fc_model')
            # 如果是false，则做出预测
            for i in range(100):
                # 每次测试一张图片
                x_test, y_test = mnist.test.next_batch(1)
                print('第%d张图片，手写数字的目标值是：%d,预测结果是：%d,准确率为：%f' % (
                    i,
                    tf.argmax(y_test, 1).eval(),
                    tf.argmax(sess.run(y_predict, feed_dict={
                              x: x_test, y_true: y_test}), 1).eval(),
                    sess.run(accuracy, feed_dict={x: x_test, y_true: y_test})

                ))

    return None


if __name__ == '__main__':
    full_connected()