from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import argparse

import sys

import tempfile

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None

def weight_variable(shape):

    init=tf.truncated_normal(shape=shape,stddev=0.1,mean=1.)

    return tf.Variable(init)

def bias_variable(shape):

    init=tf.constant(0.1,shape=shape)

    return tf.Variable(init)

def conv2d(x,w):

    return tf.nn.conv2d(x,w,[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")

def deepnn(x):

    with tf.name_scope('reshape'):

        x_image=tf.reshape(x,[-1,28,28,1])

    #第一层卷积和池化

    with tf.name_scope('conv1'):

        #输入为1张图片 卷积核为5*5 生成32个特征图

        w_conv1=weight_variable([5,5,1,32])

        b_conv1=bias_variable([32])

        h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1)

    with tf.name_scope('pool1'):

        h_pool1=max_pool_2x2(h_conv1)

    #第二层卷积和池化

    with tf.name_scope("conv2"):

        #输入为32张特征图，卷积核为5*5 输出64张特征图

        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)

    with tf.name_scope("pool2"):

        h_pool2=max_pool_2x2(h_conv2)

    #第一层全连接层，将特征图展开为特征向量，与1024个节点连接

    with tf.name_scope("fc1"):

        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)

    #dropout层，训练时随机让某些隐含层节点权重不工作

    with tf.name_scope("dropout1"):

        keep_prob=tf.placeholder(tf.float32)

        h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)

    #第二个全连接层，连接1024个节点，输出one-hot预测

    with tf.name_scope("fc2"):

        w_fc2=weight_variable([1024,10])

        b_fc2=bias_variable([10])

        h_fc2=tf.matmul(h_fc1_drop,w_fc2)+b_fc2

    return h_fc2,keep_prob

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import argparse

import sys

import tempfile

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

import mnist_model

FLAGS = None

def main(_):

  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  #设置输入变量

  x=tf.placeholder(dtype=tf.float32,shape=[None,784])

  #设置输出变量

  y_real=tf.placeholder(dtype=tf.float32,shape=[None,10])

  #实例化网络

  y_pre,keep_prob=mnist_model.deepnn(x)

  #设置损失函数

  with tf.name_scope("loss"):

      cross_entropy=tf.nn.softmax_cross_entropy_with_logits(logits=y_pre,labels=y_real)

      loss=tf.reduce_mean(cross_entropy)

  #设置优化器

  with tf.name_scope("adam_optimizer"):

      train_step=tf.train.AdamOptimizer(1e-4).minimize(loss)

  #计算正确率：

  with tf.name_scope("accuracy"):

      correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y_real, 1))

      correct_prediction = tf.cast(correct_prediction, tf.float32)

  accuracy = tf.reduce_mean(correct_prediction)

  #将神经网络图模型保存

  graph_location=tempfile.mkdtemp()

  print('saving graph to %s'%graph_location)

  train_writer=tf.summary.FileWriter(graph_location)

  train_writer.add_graph(tf.get_default_graph())

  #将训练的网络保存下来

  saver=tf.train.Saver()

  with tf.Session() as sess:

      sess.run(tf.global_variables_initializer())

      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_real: batch[1], keep_prob: 1.0})

              print('step %d, training accuracy %g' % (i, train_accuracy))

          sess.run(train_step,feed_dict={x: batch[0], y_real: batch[1], keep_prob: 0.5})

      #在测试集上进行测试

      test_accuracy = 0

      for i in range(200):

          batch = mnist.test.next_batch(50)

          test_accuracy += accuracy.eval(feed_dict={x: batch[0], y_real: batch[1], keep_prob: 1.0}) / 200;

      print('test accuracy %g' % test_accuracy)

      save_path = saver.save(sess, "mnist_cnn_model.ckpt")

if __name__ == '__main__':

  parser = argparse.ArgumentParser()

  parser.add_argument('--data_dir', type=str,

                      default='./',

                      help='Directory for storing input data')

  FLAGS, unparsed = parser.parse_known_args()

  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)