tensorflow学习笔记七----------卷积神经网络

卷积神经网络比神经网络稍微复杂一些，因为其多了一个卷积层(convolutional layer)和池化层(pooling layer)。

使用mnist数据集，n个数据，每个数据的像素为28*28*1=784。先让这些数据通过第一个卷积层，在这个卷积上指定一个3*3*1的feature，这个feature的个数设为64。接着经过一个池化层，让这个池化层的窗口为2*2。然后在经过一个卷积层，在这个卷积上指定一个3*3*64的feature，这个featurn的个数设置为128,。接着经过一个池化层，让这个池化层的窗口为2*2。让结果经过一个全连接层，这个全连接层大小设置为1024，在经过第二个全连接层，大小设置为10，进行分类。

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('data/', one_hot=True)
trainimg   = mnist.train.images
trainlabel = mnist.train.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
print ("MNIST ready")

#像素点为784
n_input  = 784
#十分类
n_output = 10
#wc1，第一个卷积层参数，3*3*1，共有64个
#wc2，第二个卷积层参数，3*3*64，共有128个
#wd1，第一个全连接层参数，经过两个池化层被压缩到7*7
#wd2，第二个全连接层参数
weights  = {
        'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64], stddev=0.1)),

        'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128], stddev=0.1)),
        'wd1': tf.Variable(tf.random_normal([7*7*128, 1024], stddev=0.1)),
        'wd2': tf.Variable(tf.random_normal([1024, n_output], stddev=0.1))
    }
biases   = {
        'bc1': tf.Variable(tf.random_normal([64], stddev=0.1)),
        'bc2': tf.Variable(tf.random_normal([128], stddev=0.1)),
        'bd1': tf.Variable(tf.random_normal([1024], stddev=0.1)),
        'bd2': tf.Variable(tf.random_normal([n_output], stddev=0.1))
    }

定义前向传播函数。先将输入数据预处理，变成tensorflow支持的四维图像；进行第一层的卷积层处理，调用conv2d函数；将卷积结果用激活函数进行处理（relu函数）；将结果进行池化层处理，ksize代表窗口大小；将池化层的结果进行随机删除节点；进行第二层卷积和池化...；进行全连接层，先将数据进行reshape（此处为7*7*128）；进行激活函数处理；得出结果。前向传播结束。

def conv_basic(_input, _w, _b, _keepratio):
        # INPUT
        _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])
        # CONV LAYER 1
        _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME')
        _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1, _b['bc1']))
        _pool1 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
        _pool_dr1 = tf.nn.dropout(_pool1, _keepratio)
        # CONV LAYER 2
        _conv2 = tf.nn.conv2d(_pool_dr1, _w['wc2'], strides=[1, 1, 1, 1], padding='SAME')
        _conv2 = tf.nn.relu(tf.nn.bias_add(_conv2, _b['bc2']))
        _pool2 = tf.nn.max_pool(_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
        _pool_dr2 = tf.nn.dropout(_pool2, _keepratio)
        # VECTORIZE
        _dense1 = tf.reshape(_pool_dr2, [-1, _w['wd1'].get_shape().as_list()[0]])
        # FULLY CONNECTED LAYER 1
        _fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1, _w['wd1']), _b['bd1']))
        _fc_dr1 = tf.nn.dropout(_fc1, _keepratio)
        # FULLY CONNECTED LAYER 2
        _out = tf.add(tf.matmul(_fc_dr1, _w['wd2']), _b['bd2'])
        # RETURN
        out = { 'input_r': _input_r, 'conv1': _conv1, 'pool1': _pool1, 'pool1_dr1': _pool_dr1,
            'conv2': _conv2, 'pool2': _pool2, 'pool_dr2': _pool_dr2, 'dense1': _dense1,
            'fc1': _fc1, 'fc_dr1': _fc_dr1, 'out': _out
        }
        return out
print ("CNN READY")

定义损失函数，定义优化器

x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_output])
keepratio = tf.placeholder(tf.float32)

# FUNCTIONS

_pred = conv_basic(x, weights, biases, keepratio)['out']
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y))
optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
_corr = tf.equal(tf.argmax(_pred,1), tf.argmax(y,1)) 
accr = tf.reduce_mean(tf.cast(_corr, tf.float32)) 
init = tf.global_variables_initializer()
    
# SAVER
save_step = 1
saver = tf.train.Saver(max_to_keep=3) 

print ("GRAPH READY")

进行迭代

do_train = 1
sess = tf.Session()
sess.run(init)

training_epochs = 15
batch_size      = 16
display_step    = 1
if do_train == 1:
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Fit training using batch data
            sess.run(optm, feed_dict={x: batch_xs, y: batch_ys, keepratio:0.7})
            # Compute average loss
            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})/total_batch

        # Display logs per epoch step
        if epoch % display_step == 0: 
            print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
            train_acc = sess.run(accr, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})
            print (" Training accuracy: %.3f" % (train_acc))
            #test_acc = sess.run(accr, feed_dict={x: testimg, y: testlabel, keepratio:1.})
            #print (" Test accuracy: %.3f" % (test_acc))print ("OPTIMIZATION FINISHED")