from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
batch = mnist.train.next_batch(50)
train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
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}))

#!/usr/bin/python3.5
# -*- coding: utf-8 -*- 
 
import os
 
import numpy as np
import tensorflow as tf
 
from PIL import Image
 
 
# 第一次遍历图片目录是为了获取图片总数
input_count = 0
for i in range(0,10):
  dir = './custom_images/%s/' % i         # 这里可以改成你自己的图片目录，i为分类标签
  for rt, dirs, files in os.walk(dir):
    for filename in files:
      input_count += 1
 
# 定义对应维数和各维长度的数组
input_images = np.array([[0]*784 for i in range(input_count)])
input_labels = np.array([[0]*10 for i in range(input_count)])
 
# 第二次遍历图片目录是为了生成图片数据和标签
index = 0
for i in range(0,10):
  dir = './custom_images/%s/' % i         # 这里可以改成你自己的图片目录，i为分类标签
  for rt, dirs, files in os.walk(dir):
    for filename in files:
      filename = dir + filename
      img = Image.open(filename)
      width = img.size[0]
      height = img.size[1]
      for h in range(0, height):
        for w in range(0, width):
          # 通过这样的处理，使数字的线条变细，有利于提高识别准确率
          if img.getpixel((w, h)) > 230:
            input_images[index][w+h*width] = 0
          else:
            input_images[index][w+h*width] = 1
      input_labels[index][i] = 1
      index += 1
 
 
# 定义输入节点，对应于图片像素值矩阵集合和图片标签(即所代表的数字)
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
 
x_image = tf.reshape(x, [-1, 28, 28, 1])
 
# 定义第一个卷积层的variables和ops
W_conv1 = tf.Variable(tf.truncated_normal([7, 7, 1, 32], stddev=0.1))
b_conv1 = tf.Variable(tf.constant(0.1, shape=[32]))
 
L1_conv = tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1], padding='SAME')
L1_relu = tf.nn.relu(L1_conv + b_conv1)
L1_pool = tf.nn.max_pool(L1_relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 
# 定义第二个卷积层的variables和ops
W_conv2 = tf.Variable(tf.truncated_normal([3, 3, 32, 64], stddev=0.1))
b_conv2 = tf.Variable(tf.constant(0.1, shape=[64]))
 
L2_conv = tf.nn.conv2d(L1_pool, W_conv2, strides=[1, 1, 1, 1], padding='SAME')
L2_relu = tf.nn.relu(L2_conv + b_conv2)
L2_pool = tf.nn.max_pool(L2_relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 
 
# 全连接层
W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1))
b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]))
 
h_pool2_flat = tf.reshape(L2_pool, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
 
 
# dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
 
# readout层
W_fc2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1))
b_fc2 = tf.Variable(tf.constant(0.1, shape=[10]))
 
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
 
# 定义优化器和训练op
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=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, tf.float32))
 
 
with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
 
  print ("一共读取了 %s 个输入图像， %s 个标签" % (input_count, input_count))
 
  # 设置每次训练op的输入个数和迭代次数，这里为了支持任意图片总数，定义了一个余数remainder，譬如，如果每次训练op的输入个数为60，图片总数为150张，则前面两次各输入60张，最后一次输入30张（余数30）
  batch_size = 60
  iterations = 100
  batches_count = int(input_count / batch_size)
  remainder = input_count % batch_size
  print ("数据集分成 %s 批, 前面每批 %s 个数据，最后一批 %s 个数据" % (batches_count+1, batch_size, remainder))
 
  # 执行训练迭代
  for it in range(iterations):
    # 这里的关键是要把输入数组转为np.array
    for n in range(batches_count):
      train_step.run(feed_dict={x: input_images[n*batch_size:(n+1)*batch_size], y_: input_labels[n*batch_size:(n+1)*batch_size], keep_prob: 0.5})
    if remainder > 0:
      start_index = batches_count * batch_size;
      train_step.run(feed_dict={x: input_images[start_index:input_count-1], y_: input_labels[start_index:input_count-1], keep_prob: 0.5})
 
    # 每完成五次迭代，判断准确度是否已达到100%，达到则退出迭代循环
    iterate_accuracy = 0
    if it%5 == 0:
      iterate_accuracy = accuracy.eval(feed_dict={x: input_images, y_: input_labels, keep_prob: 1.0})
      print ('iteration %d: accuracy %s' % (it, iterate_accuracy))
      if iterate_accuracy >= 1:
        break;
 
  print ('完成训练!')

# 定义对应维数和各维长度的数组
input_images = np.array([[0]*784 for i in range(input_count)])
input_labels = np.array([[0]*10 for i in range(input_count)])

x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])