用TensorFlow实现Mnist数字识别

import tensorflow as tf
from dataset import *
import time

#时间标记起始点
time.clock()
#导入数据
x_train, y_train, x_validating, y_validating, x_test, y_test = data_set()
#定义参数
layer_dimensions = [784, 30, 50, 25, 33, 25, 15, 20, 10]
regularization_rate = 0.001
w_cache, b_cache = initialization(layer_dimensions)
#数据输入接口
x = tf.placeholder(tf.float32, shape=(None,784), name='x_input')
y_ = tf.placeholder(tf.float32, shape=(None,10), name='y_input')
#前向传播
y, regularizer_cache = propagation_forward(x, w_cache, b_cache, regularization_rate)
#普通损失函数,均方失真
cross_entropy_mean = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=y, labels=tf.argmax(y_, 1))     #logits为未归一化的量
loss = cross_entropy_mean + tf.accumulate_n(regularizer_cache)
#优化方法
global_step = tf.Variable(0)
learning_rate = tf.train.exponential_decay(0.01, global_step, 100, 0.99, staircase=True)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step)
#正确度计算
correct_prediction = tf.equal(tf.arg_max(y, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#创建会话
with tf.Session() as sess:
    #初始化变量
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    STEPS = 50000
    batch_size = 256
    x_axis = [i for i in range(STEPS+1) if (i % 100 == 0)]
    train_line = []
    validating_line = []
    test_line = []
    for i in range(STEPS+1):
        xs, ys = batch(x_train, y_train, batch_size, i)
        sess.run(train_step, feed_dict={x: xs, y_: ys})
        #计算正确率
        if i % 100 == 0:
            train_acc = sess.run(accuracy, feed_dict={x: x_train, y_: y_train})
            validating_acc = sess.run(accuracy, feed_dict={x: x_validating, y_: y_validating})
            test_acc = sess.run(accuracy, feed_dict={x: x_test, y_: y_test})
            train_line.append(train_acc)
            validating_line.append(validating_acc)
            test_line.append(test_acc)
            if i % 1000 == 0:
               print("after %d train steps, train accuracy on model is:%s" % (i, train_acc))
               #print("after %d train steps, validating accuracy on model is:%s" % (i, validating_acc))
               print("after %d train steps, test accuracy on model is:%s" % (i, test_acc))
    #输出运行时间
    print("total_time is:%s" % time.clock())
    #性能曲线图
    draw(x_axis, train_line, validating_line, test_line)

辅助函数：

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
def data_set():
  mnist = input_data.read_data_sets("D:/DL_dataset/TensorFlow/", one_hot=True)
  x_train = mnist.train.images    #(55000,784)            图片为28*28=784像素，以下也是
  y_train = mnist.train.labels    #(55000,10)
  x_validating = mnist.validation.images    #(5000,784)
  y_validating = mnist.validation.labels    #(5000,10)
  x_test = mnist.test.images    #(10000,784)
  y_test = mnist.test.labels     #(10000,10)
  return x_train, y_train, x_validating, y_validating, x_test, y_test

def initialization(layer_dimensions):
  w_cache = []
  b_cache = []
  for i in range(1, len(layer_dimensions)):
    w = tf.Variable(tf.random_normal((layer_dimensions[i-1], layer_dimensions[i]), stddev=1, seed=1))
    b = tf.Variable(tf.constant(0., shape=[1, layer_dimensions[i]]))
    w_cache.append(w)
    b_cache.append(b)
  return w_cache, b_cache

#前向传播与正则项计算
def propagation_forward(x, w_cache, b_cache, regularization_rate):
  a = x
  long = len(w_cache)
  regularizer_cache = []
  regularizer = tf.contrib.layers.l2_regularizer(regularization_rate)
  if long> 1:
      for i in range(long-1):
        a = tf.nn.tanh(tf.matmul(a, w_cache[i]) + b_cache[i])
        regularizer_cache.append(regularizer(w_cache[i]))
  a = tf.matmul(a, w_cache[long-1]) + b_cache[long-1]
  regularizer_cache.append(regularizer(w_cache[long-1]))
  return a, regularizer_cache

#画性能曲线
def draw(total_iteration, train, validating, test):
  plt.figure(figsize=(8, 6), dpi=80)
  plt.subplot(1, 1, 1)
  xmin, xmax = min(total_iteration), max(total_iteration)
  ymin, ymax = min(train), max(train)
  #dx = (xmax - xmin) * 0.2
  #dy = (ymax - ymin) * 0.2
  plt.plot(total_iteration, train, '-y', label='train_acc')
  plt.plot(total_iteration, validating, '-r', label='valicating_acc')
  plt.plot(total_iteration, test, '-g', label='test_acc')
  plt.legend(loc='lower right')
  # 设置轴记号
  plt.xticks(np.linspace(0, xmax, 11, endpoint=True))
  plt.yticks(np.linspace(0, 1.0, 11, endpoint=True))
  plt.show()

#设置批量
def batch(x_train, y_train, batch_size, i):
  #整除运算，返回批数量
  batch_nums = x_train.shape[0] // batch_size
  i = i % batch_size
  #舍弃掉最后批整数倍以外的多余数据
  start = batch_size * i
  end = batch_size * (i + 1) - 1
  xs = x_train[start:end]
  ys = y_train[start:end]
  return xs, ys

结果：训练集与测试集正确率分别为：99.83%， 97.39%