tensorflow识别自己手写数字

tensorflow作为google开源的项目，现在赶超了caffe，好像成为最受欢迎的深度学习框架。确实在编写的时候更能感受到代码的真实存在，这点和caffe不同，caffe通过编写配置文件进行网络的生成。环境tensorflow是0.10的版本，注意其他版本有的语句会有错误，这是tensorflow版本之间的兼容问题。

还需要安装PIL：pip install Pillow

图片的格式： 

– 图像标准化，可安装在20×20像素的框内，同时保留其长宽比。
– 图片都集中在一个28×28的图像中。
– 像素以列为主进行排序。像素值0到255，0表示背景（白色），255表示前景（黑色）。

创建一个.png的文件，背景是白色的，手写的字体是黑色的，

下面是数据测试的代码，一个两层的卷积神经网，然后用save进行模型的保存。

# coding: UTF-8 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import input_data ''''' 得到数据 ''' mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  training = mnist.train.images trainlable = mnist.train.labels testing = mnist.test.images testlabel = mnist.test.labels  print ("MNIST loaded") # 获取交互式的方式 sess = tf.InteractiveSession() # 初始化变量 x = tf.placeholder("float", shape=[None, 784]) y_ = tf.placeholder("float", shape=[None, 10]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) ''''' 生成权重函数，其中shape是数据的形状 ''' def weight_variable(shape):   initial = tf.truncated_normal(shape, stddev=0.1)   return tf.Variable(initial) ''''' 生成偏执项 其中shape是数据形状 ''' def bias_variable(shape):   initial = tf.constant(0.1, shape=shape)   return tf.Variable(initial)  def conv2d(x, W):   return tf.nn.conv2d(x, W, strides=[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')  W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1])  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1)  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) h_pool2 = max_pool_2x2(h_conv2)   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)  keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10])  y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)  cross_entropy = -tf.reduce_sum(y_*tf.log(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, "float"))  # 保存网络训练的参数 saver = tf.train.Saver() sess.run(tf.initialize_all_variables()) for i in range(8000):  batch = mnist.train.next_batch(50)  if i%100 == 0:   train_accuracy = accuracy.eval(feed_dict={     x:batch[0], y_: batch[1], keep_prob: 1.0})   print "step %d, training accuracy %g"%(i, train_accuracy)  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})  save_path = saver.save(sess, "model_mnist.ckpt") print("Model saved in life:", save_path)  print "test accuracy %g"%accuracy.eval(feed_dict={   x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})