cnn_build.py

#-*- coding:utf-8 -*-


import os
import logging as log
import matplotlib.pyplot as plt
import common
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import cv2

SIZE = 64
x_data = tf.placeholder(tf.float32, [None, SIZE, SIZE, 3])
y_data = tf.placeholder(tf.float32, [None, None])

keep_prob_5 = tf.placeholder(tf.float32)
keep_prob_75 = tf.placeholder(tf.float32)


def weight_variable(shape):
    ''' build weight variable'''
    init = tf.random_normal(shape, stddev=0.01)
    #init = tf.truncated_normal(shape, stddev=0.01)
    return tf.Variable(init)

def bias_variable(shape):
    ''' build bias variable'''
    init = tf.random_normal(shape)
    #init = tf.truncated_normal(shape, stddev=0.01)
    return tf.Variable(init)

def conv2d(x, W):
    ''' conv2d by 1, 1, 1, 1'''
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def maxPool(x):
    ''' max pooling'''
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def dropout(x, keep):
    ''' drop out'''
    return tf.nn.dropout(x, keep)

def cnnLayer(classnum):
    ''' create cnn layer'''
    # 第一层
    #将图像长宽变小，拉长
    W1 = weight_variable([3, 3, 3, 32]) # 卷积核大小(3,3)， 输入通道(3)， 输出通道(32)
    b1 = bias_variable([32])
    # 64 * 64 * 32
    conv1 = tf.nn.relu(conv2d(x_data, W1) + b1) # 计算激活函数，阵中每行的非最大值置0
    #最大值池化
    pool1 = maxPool(conv1)
    # 减少过拟合，随机让某些权重不更新
    drop1 = dropout(pool1, keep_prob_5) # 32 * 32 * 32 多个输入channel 被filter内积掉了

    # 第二层
    W2 = weight_variable([3, 3, 32, 64])
    b2 = bias_variable([64])
    # 32 * 32 * 32
    conv2 = tf.nn.relu(conv2d(drop1, W2) + b2)
    pool2 = maxPool(conv2)
    drop2 = dropout(pool2, keep_prob_5) # 64 * 16 * 16

    # 第三层
    W3 = weight_variable([3, 3, 64, 64])
    b3 = bias_variable([64])
    # 64 * 16 * 16
    conv3 = tf.nn.relu(conv2d(drop2, W3) + b3) #
    pool3 = maxPool(conv3)
    drop3 = dropout(pool3, keep_prob_5) # 64 * 8 * 8

    # 全连接层
    Wf = weight_variable([8*16*32, 512])
    bf = bias_variable([512])
    # (64*8*8) => (1,8*16*32)
    drop3_flat = tf.reshape(drop3, [-1, 8*16*32])
    # => (1,512)
    dense = tf.nn.relu(tf.matmul(drop3_flat, Wf) + bf)
    dropf = dropout(dense, keep_prob_75)

    # 输出层
    Wout = weight_variable([512, classnum])
    bout = weight_variable([classnum])
    # => (1*classnum)
    out = tf.add(tf.matmul(dropf, Wout), bout)

    return out

def train(train_x, train_y, tfsavepath):
    ''' train'''
    log.debug('train')
    #调用卷积
    out = cnnLayer(train_y.shape[1])

    #求loss。先做softmax求每个样本的概率,然后cross_entropy求交叉熵,reduce_mean求熵的均值（即loss)
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=out, labels=y_data))
    train_step = tf.train.AdamOptimizer(0.01).minimize(cross_entropy)
    #tf.cast转化数据类型
    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(y_data, 1)), tf.float32))

    #只能保存Variable
    saver = tf.train.Saver()
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        batch_size = 10
        num_batch = len(train_x) // 10
        for n in range(10):
            #生成len个乱序数列
            r = np.random.permutation(len(train_x))
            train_x = train_x[r, :]
            train_y = train_y[r, :]

            for i in range(num_batch):
                batch_x = train_x[i*batch_size : (i+1)*batch_size]
                batch_y = train_y[i*batch_size : (i+1)*batch_size]
                _, loss = sess.run([train_step, cross_entropy],\
                                   feed_dict={x_data:batch_x, y_data:batch_y,
                                              keep_prob_5:0.75, keep_prob_75:0.75})

                print(n*num_batch+i, loss)

        # 获取测试数据的准确率
        acc = accuracy.eval({x_data:train_x, y_data:train_y, keep_prob_5:1.0, keep_prob_75:1.0})
        print('after 10 times run: accuracy is ', acc)
        saver.save(sess, tfsavepath)

def validate(test_x, tfsavepath):
    ''' validate '''
    output = cnnLayer(2)
    #predict = tf.equal(tf.argmax(output, 1), tf.argmax(y_data, 1))
    predict = output

    saver = tf.train.Saver()
    with tf.Session() as sess:
        #sess.run(tf.global_variables_initializer())
        saver.restore(sess, tfsavepath)
        res = sess.run([predict, tf.argmax(output, 1)],
                       feed_dict={x_data: test_x,
                                  keep_prob_5:1.0, keep_prob_75: 1.0})
        return res

if __name__ == '__main__':
    pass