神经网络 （2）- Alexnet Training on MNIST

文章目录

• Win10 Anaconda下配置tensorflow+jupyter notebook环境
• AlexNet 识别MNIST

Win10 Anaconda下配置tensorflow+jupyter notebook环境

1.安装anaconda

到Anaconda官网下载，我是用的是Anaconda3-4.8.0版本（Python3对应的是Anaconda3，Python2对应的是Anaconda2），根据需要下载即可。下载好之后点击exe文件安装没什么好讲的。

唯一需要特别说明的是，安装的过程中要把添加路径到环境中选项选中！安装完成之后到命令行输入命令验证是否成功安装：

conda --version

1. 安装tensorflow 官方步骤创建环境，
   
   If you installed a TensorFlow as it said in official documentation: https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html#overview

I mean creating an environment called tensorflow and tested your installation in python, but TensorFlow can not be imported in jupyter, you have to install jupyter in your tensorflow environment too:

conda install jupyter notebook

After that I run a jupyter and it can import TensorFlow too:

jupyter notebook

AlexNet 识别MNIST

以上是AlexNet的结构，上下其实是一样的，共同用一套参数。 Similar structure to LeNet, AlexNet has more filters per layer, deeper and stacked. There are 5 convolutional layers, 3 fully connected layers and with Relu applied after each of them, and dropout applied before the first and second fully connected layer.AlexNet是2012年ImageNet比赛的冠军，虽然过去了很长时间，但是作为深度学习中的经典模型，AlexNet不但有助于我们理解其中所使用的很多技巧，而且非常有助于提升我们使用深度学习工具箱的熟练度。

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
import tensorflow as tf

#
learning_rate = 0.001
training_iters = 200000
batch_size = 64
display_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.}))