import tensorflow as tf # 输入数据 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("E:\MNIST_data", one_hot=True) # 定义网络的超参数 learning_rate = 0.001 training_iters = 200000 batch_size = 128 display_step = 5 # 定义网络的参数 # 输入的维度 (img shape: 28*28) n_input = 784 # 标记的维度 (0-9 digits) n_classes = 10 # Dropout的概率，输出的可能性 dropout = 0.75 # 输入占位符 x = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_classes]) #dropout (keep probability) keep_prob = tf.placeholder(tf.float32) # 定义卷积操作 def conv2d(name,x, W, b, strides=1): # Conv2D wrapper, with bias and relu activation x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') x = tf.nn.bias_add(x, b) # 使用relu激活函数 return tf.nn.relu(x,name=name) # 定义池化层操作 def maxpool2d(name,x, k=2): # MaxPool2D wrapper return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME',name=name) # 规范化操作 def norm(name, l_input, lsize=4): return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0,beta=0.75, name=name) # 定义所有的网络参数 weights = { 'wc1': tf.Variable(tf.random_normal([11, 11, 1, 96])), 'wc2': tf.Variable(tf.random_normal([5, 5, 96, 256])), 'wc3': tf.Variable(tf.random_normal([3, 3, 256, 384])), 'wc4': tf.Variable(tf.random_normal([3, 3, 384, 384])), 'wc5': tf.Variable(tf.random_normal([3, 3, 384, 256])), 'wd1': tf.Variable(tf.random_normal([4*4*256, 4096])), 'wd2': tf.Variable(tf.random_normal([4096, 1024])), 'out': tf.Variable(tf.random_normal([1024, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([96])), 'bc2': tf.Variable(tf.random_normal([256])), 'bc3': tf.Variable(tf.random_normal([384])), 'bc4': tf.Variable(tf.random_normal([384])), 'bc5': tf.Variable(tf.random_normal([256])), 'bd1': tf.Variable(tf.random_normal([4096])), 'bd2': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes])) } # 定义整个网络 def alex_net(x, weights, biases, dropout): # 向量转为矩阵 Reshape input picture x = tf.reshape(x, shape=[-1, 28, 28, 1]) # 第一层卷积 # 卷积 conv1 = conv2d('conv1', x, weights['wc1'], biases['bc1']) # 下采样 pool1 = maxpool2d('pool1', conv1, k=2) # 规范化 norm1 = norm('norm1', pool1, lsize=4) # 第二层卷积 # 卷积 conv2 = conv2d('conv2', norm1, weights['wc2'], biases['bc2']) # 最大池化（向下采样） pool2 = maxpool2d('pool2', conv2, k=2) # 规范化 norm2 = norm('norm2', pool2, lsize=4) # 第三层卷积 # 卷积 conv3 = conv2d('conv3', norm2, weights['wc3'], biases['bc3']) # 规范化 norm3 = norm('norm3', conv3, lsize=4) # 第四层卷积 conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4']) # 第五层卷积 conv5 = conv2d('conv5', conv4, weights['wc5'], biases['bc5']) # 最大池化（向下采样） pool5 = maxpool2d('pool5', conv5, k=2) # 规范化 norm5 = norm('norm5', pool5, lsize=4) # 全连接层1 fc1 = tf.reshape(norm5, [-1, weights['wd1'].get_shape().as_list()[0]]) fc1 =tf.add(tf.matmul(fc1, weights['wd1']),biases['bd1']) fc1 = tf.nn.relu(fc1) # dropout fc1=tf.nn.dropout(fc1,dropout) # 全连接层2 fc2 = tf.reshape(fc1, [-1, weights['wd2'].get_shape().as_list()[0]]) fc2 =tf.add(tf.matmul(fc2, weights['wd2']),biases['bd2']) fc2 = tf.nn.relu(fc2) # dropout fc2=tf.nn.dropout(fc2,dropout) # 输出层 out = tf.add(tf.matmul(fc2, weights['out']) ,biases['out']) return out # 构建模型 pred = alex_net(x, weights, biases, keep_prob) # 定义损失函数和优化器 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 评估函数 correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # 初始化变量 init = tf.global_variables_initializer() # 开启一个训练 with tf.Session() as sess: sess.run(init) step = 1 # 开始训练，直到达到training_iters，即200000 while step * batch_size < training_iters: #获取批量数据 batch_x, batch_y = mnist.train.next_batch(batch_size) sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout}) if step % display_step == 0: # 计算损失值和准确度，输出 loss,acc = sess.run([cost,accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.}) print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)) step += 1 print ("Optimization Finished!") # 计算测试集的精确度 print ("Testing Accuracy:",sess.run(accuracy, feed_dict={x: mnist.test.images[:256],y: mnist.test.labels[:256],keep_prob: 1.}))
