基于cnn卷积神经网络的手写数字识别.zip

# -*- coding: utf-8 -*- import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) #MNIST数据集所在路径 lr = 1e-3 max_step =30000 def weight_variable(shape): #初始化weight initial = tf.truncated_normal(shape,stddev = 0.1) return tf.Variable(initial) def bias_variable(shape): #初始化bias initial = tf.constant(0.1,shape = shape) return tf.Variable(initial) def conv2d(x,W): #卷积层 return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = 'SAME') def max_pool_2x2(x): #pooling层 return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') x = tf.placeholder(tf.float32, [None, 784]) y_ = tf.placeholder(tf.float32, [None, 10]) #建立卷积层 x_image = tf.reshape(x,[-1,28,28,1]) W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) #建立卷积层 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) #建立全连接层 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) #建立全连接层 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) #定义交叉熵损失函数 cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) #定义优化器 train_step = tf.train.AdamOptimizer(lr ).minimize(cross_entropy) #计算准确率 correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) saver = tf.train.Saver() #定义saver with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(max_step+1): batch = mnist.train.next_batch(50) if i % 3000 == 0: train_accuracy = accuracy.eval(feed_dict={ x: batch[0], y_: batch[1], keep_prob: 1.0}) #print(x,y_) print('step %d, training accuracy %.3f' % (i, float(train_accuracy))) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) saver.save(sess, 'SAVE/model.ckpt') #模型储存位置 print('test accuracy %g' % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))