#!/usr/bin/python
#coding=utf-8
''' face detect convolution'''
# pylint: disable=invalid-name
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 weightVariable(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 biasVariable(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
W1 = weightVariable([3, 3, 3, 32]) # 卷积核大小(3,3)， 输入通道(3)， 输出通道(32)
b1 = biasVariable([32])
conv1 = tf.nn.relu(conv2d(x_data, W1) + b1)
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 = weightVariable([3, 3, 32, 64])
b2 = biasVariable([64])
conv2 = tf.nn.relu(conv2d(drop1, W2) + b2)
pool2 = maxPool(conv2)
drop2 = dropout(pool2, keep_prob_5) # 64 * 16 * 16
# 第三层
W3 = weightVariable([3, 3, 64, 64])
b3 = biasVariable([64])
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 = weightVariable([8*16*32, 512])
bf = biasVariable([512])
drop3_flat = tf.reshape(drop3, [-1, 8*16*32])
dense = tf.nn.relu(tf.matmul(drop3_flat, Wf) + bf)
dropf = dropout(dense, keep_prob_75)
# 输出层
Wout = weightVariable([512, classnum])
bout = weightVariable([classnum])
#out = tf.matmul(dropf, Wout) + bout
out = tf.add(tf.matmul(dropf, Wout), bout)
return out
def train(train_x, train_y, tfsavepath):
''' train'''
log.debug('train')
out = cnnLayer(train_y.shape[1])
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out, labels=y_data))
train_step = tf.train.AdamOptimizer(0.01).minimize(cross_entropy)
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(y_data, 1)), tf.float32))
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):
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