经典神经网络进行MNIST手写数字识别系列（一）：ALEXNET

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本系列基于

谷歌开源的TensorFlow

Python 3.5

Ubuntu 16.04

作为初学神经网络的学生，暂时还没有自己设计网络结构和调整超参数的丰富经验，就先熟悉经典结构，完成入门的手写数字识别项目。

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ALEXNET在2012年的imagnet比赛中获得了冠军。由LeNet发展过来。详见：https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt

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Alexnet主要的分类图像颜色通道为3，MNIST颜色通道为1，在常用代码上需要进行稍许改动。

from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)import tensorflow as tf# learning_rate = 0.001training_iters = 200000batch_size = 64display_step = 20# n_input = 784 # n_classes = 10 # dropout = 0.8 # Dropout # x = tf.placeholder(tf.float32, [None, n_input])y = tf.placeholder(tf.float32, [None, n_classes])keep_prob = tf.placeholder(tf.float32)# def conv2d(name, l_input, w, b):    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b), name=name)# def max_pool(name, l_input, k):    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)# 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)#  def alex_net(_X, _weights, _biases, _dropout):    #     _X = tf.reshape(_X, shape=[-1, 28, 28, 1])    #     conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])    #     pool1 = max_pool('pool1', conv1, k=2)    #     norm1 = norm('norm1', pool1, lsize=4)    # Dropout    norm1 = tf.nn.dropout(norm1, _dropout)    #     conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])    #     pool2 = max_pool('pool2', conv2, k=2)    #     norm2 = norm('norm2', pool2, lsize=4)    # Dropout    norm2 = tf.nn.dropout(norm2, _dropout)    #     conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])    conv4 = conv2d('conv4', conv3, _weights['wc4'], _biases['bc4'])    conv5 = conv2d('conv5', conv4, _weights['wc5'], _biases['bc5'])     pool5 = max_pool('pool5', conv5, k=2)    #     norm5 = norm('norm5', pool5, lsize=4)    # Dropout    norm5 = tf.nn.dropout(norm5, _dropout)    #     dense1 = tf.reshape(norm5, [-1, _weights['wd1'].get_shape().as_list()[0]])     dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')     #     dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation    #     out = tf.matmul(dense2, _weights['out']) + _biases['out']    return out# weights = {    'wc1': tf.Variable(tf.random_normal([11, 11, 1, 64])),    'wc2': tf.Variable(tf.random_normal([5, 5, 64, 192])),    'wc3': tf.Variable(tf.random_normal([3, 3, 192, 384])),    'wc4': tf.Variable(tf.random_normal([3, 3, 384, 256])),    'wc5': tf.Variable(tf.random_normal([3, 3, 256, 256])),    'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),    'wd2': tf.Variable(tf.random_normal([1024, 1024])),    'out': tf.Variable(tf.random_normal([1024, 10]))}biases = {    'bc1': tf.Variable(tf.random_normal([64])),    'bc2': tf.Variable(tf.random_normal([192])),    'bc3': tf.Variable(tf.random_normal([384])),    'bc4': tf.Variable(tf.random_normal([256])),    'bc5': tf.Variable(tf.random_normal([256])),    'bd1': tf.Variable(tf.random_normal([1024])),    'bd2': tf.Variable(tf.random_normal([1024])),    'out': tf.Variable(tf.random_normal([n_classes]))}# pred = alex_net(x, weights, biases, keep_prob)# 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)# correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# init = tf.initialize_all_variables()# with tf.Session() as sess:    sess.run(init)    step = 1    # Keep training until reach max iterations    while step * batch_size < training_iters:        batch_xs, batch_ys = mnist.train.next_batch(batch_size)        #         sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})        if step % display_step == 0:            #             acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})            #             loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})            print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))        step += 1    print ("Optimization Finished!")    #     print ("Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))

运行结果为：

最终正确率在98.4%

..............................................................................................................Reference.......................................................................................................................................

1.https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py