TensorFlow MNIST AlexNet

原始的AlexNet用来处理277*277*3的数据集， 并且采用5层卷积，3层全连接层来处理图像分类。

具体结构和参数信息见

http://blog.csdn.net/chenhaifeng2016/article/details/72758053

这里在前面MNIST CNN LeNet的基础上，借鉴AlexNet的思想做进一步优化。（非标准AlexNet模型)

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data#加载数据mnist = input_data.read_data_sets('MNIST_data', one_hot=True)sess = tf.InteractiveSession()#训练数据inputs = tf.placeholder(tf.float32, shape=[None, 784])#训练标签数据labels = tf.placeholder(tf.float32, shape=[None, 10])#dropoutkeep_prob = tf.placeholder(tf.float32)#把inputs更改为4维张量，第1维代表样本数量，第2维和第3维代表图像长宽， 第4维代表图像通道数, 1表示黑白x = tf.reshape(inputs, [-1,28,28,1])#第一层卷积conv1_weights = tf.Variable(tf.random_normal([3, 3, 1, 64])) #卷积核大小为3*3, 当前层深度为1， 过滤器深度为64#卷积conv1 = tf.nn.conv2d(x, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') #移动步长为1, 使用全0填充conv1_biases = tf.Variable(tf.random_normal([64]))#激活函数Relu去线性化relu1 = tf.nn.relu( tf.nn.bias_add(conv1, conv1_biases) )#最大池化pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #池化层过滤器的大小为2*2, 移动步长为2，使用全0填充#规范化norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75)norm1 = tf.nn.dropout(norm1, keep_prob)print(norm1.shape) #14*14*64#第二层卷积conv2_weights = tf.Variable(tf.random_normal([3, 3, 64, 128])) #卷积核大小为3*3, 当前层深度为64， 过滤器深度为128conv2 = tf.nn.conv2d(norm1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME') #移动步长为1, 使用全0填充conv2_biases = tf.Variable(tf.random_normal([128]))relu2 = tf.nn.relu( tf.nn.bias_add(conv2, conv2_biases) )pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')norm2 = tf.nn.lrn(pool2, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75)norm2 = tf.nn.dropout(norm2, keep_prob)print(norm2.shape) #7*7*128#第三层卷积conv3_weights = tf.Variable(tf.random_normal([3, 3, 128, 256])) #卷积核大小为3*3, 当前层深度为128， 过滤器深度为256conv3 = tf.nn.conv2d(norm2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME') #移动步长为1, 使用全0填充conv3_biases = tf.Variable(tf.random_normal([256]))relu3 = tf.nn.relu( tf.nn.bias_add(conv3, conv3_biases) )pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')norm3 = tf.nn.lrn(pool3, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75)norm3 = tf.nn.dropout(norm3, keep_prob)print(norm3.shape) #4*4*256#全连接层 1fc1_weights = tf.Variable(tf.random_normal([4*4*256, 1024]))fc1_biases = tf.Variable(tf.random_normal([1024]))fc1 = tf.reshape(norm3, [ -1, fc1_weights.get_shape().as_list()[0] ] )fc1 = tf.add(tf.matmul(fc1, fc1_weights), fc1_biases)fc1 = tf.nn.relu(fc1)#全连接层 2fc2_weights = tf.Variable(tf.random_normal([1024, 1024]))fc2_biases = tf.Variable(tf.random_normal([1024]))fc2 = tf.reshape(fc1, [ -1, fc2_weights.get_shape().as_list()[0] ] )fc2 = tf.add(tf.matmul(fc2, fc2_weights), fc2_biases)fc2 = tf.nn.relu(fc2)#输出层out_weights = tf.Variable(tf.random_normal([1024, 10]))out_biases = tf.Variable(tf.random_normal([10]))pred = tf.add(tf.matmul(fc2, out_weights), out_biases)#定义交叉熵损失函数cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=labels))#选择优化器train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)#评估函数correct_prediction = tf.equal(tf.argmax(pred,1), tf.argmax(labels,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))#训练模型sess.run(tf.global_variables_initializer())for i in range(10000):    batch = mnist.train.next_batch(100)    sess.run(train_op, feed_dict={inputs: batch[0], labels: batch[1], keep_prob: 0.75})  # 训练阶段使用75%的Dropout    if i % 100 == 0:        train_accuracy = sess.run(accuracy, feed_dict={inputs:batch[0], labels: batch[1], keep_prob: 1.0}) #评估阶段不使用Dropout        print("step %d, training accuracy %g" % (i, train_accuracy))#评估模型#只使用256个测试数据，机器太差， 跑10000内存溢出了print("test accuracy %g" % sess.run(accuracy, feed_dict={inputs: mnist.test.images[:256], labels: mnist.test.labels[:256], keep_prob: 1.0})) #评估阶段不使用Dropout