面向机器学习专家的 MNIST 高级教程代码

教程链接：面向机器学习专家的 MNIST 高级教程

利用CNN卷积神经网络训练MNIST手写字体，mnist手写字体素材为28*28像素的图片，本程序中采用两层卷积神经网络与密集连接层，利用ReLU激活函数与Adam梯度最速下降方法进行训练

代码如下：

#下载引入数据集from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)import tensorflow as tf#设置默认会话（session）sess = tf.InteractiveSession()#设置模型变量x = tf.placeholder(tf.float32, [None, 784])#W = tf.Variable(tf.zeros([784, 10]))#b = tf.Variable(tf.zeros([10]))#y = tf.nn.softmax(tf.matmul(x, W) + b)#权重初始化def weight_variable(shape):initial = tf.truncated_normal(shape,stddev=0.1)return tf.Variable(initial)def bias_variable(shape):initial = tf.constant(0.1, shape=shape)return tf.Variable(initial)#卷积与池化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')#构建模型#第一层卷积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)#第二层卷积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)#密集连接层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)#Dropoutkeep_prob = tf.placeholder("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)#输出层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)#训练模型#成本函数：交叉熵y_ = tf.placeholder(tf.float32, [None, 10])cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)#评估模型correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))#初始化变量#启动图（graph）sess.run(tf.initialize_all_variables())#训练for i in range(20000):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})print("test accuracy %g"%accuracy.eval(feed_dict = {x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))

运行结果：