用卷积神经网络对mnist进行数字识别程序（tensorflow）

#下载数据集 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data",one_hot = True) #引入tensorflow import tensorflow as tf #建立session对象 sess = tf.InteractiveSession() #占位符（图像和标签） x = tf.placeholder(tf.float32, shape=[None, 784]) y_ = tf.placeholder(tf.float32, shape=[None, 10]) #权重函数 def weight_variable(shape): initial = tf.truncated_normal(shape,stddev = 0.1) return tf.Variable(initial) #偏置函数 def bias_variable(shape): initial = tf.constant(0.1,shape = shape) return tf.Variable(initial) #卷积函数 def conv2d(x,W): return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = "SAME") #池化函数 def max_pool_2x2(x): return tf.nn.max_pool(x, ksize = [1,2,2,1], strides = [1,2,2,1], padding = "SAME") #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) #全连接层 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) keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) #softmax层 W_fc2 = weight_variable([1024,10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1_drop, W_fc2)+b_fc2 #训练和评价模型 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_,1), tf.argmax(y_conv,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) sess.run(tf.global_variables_initializer()) 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 %0.4f"%(i,train_accuracy)) train_step.run(feed_dict = {x:batch[0],y_:batch[1],keep_prob:0.5}) print("test accuracy %0.4f"