gender_training.py

#coding=utf-8
  

import  tensorflow as tf
import  gender_train_data as train_data
import  cv2
import  random
import  matplotlib.pyplot as plt
import  numpy as np
from sklearn.metrics import confusion_matrix, classification_report

train_epochs=3000
batch_size = 9
drop_prob = 0.4
learning_rate=0.00001



def weight_init(shape):
    weight = tf.truncated_normal(shape,stddev=0.1,dtype=tf.float32)
    return tf.Variable(weight)


def bias_init(shape):
    bias = tf.random_normal(shape,dtype=tf.float32)
    return tf.Variable(bias)

images_input = tf.placeholder(tf.float32,[None,112*92*3],name='input_images')
labels_input = tf.placeholder(tf.float32,[None,2],name='input_labels')

def fch_init(layer1,layer2,const=1):
    min = -const * (6.0 / (layer1 + layer2));
    max = -min;
    weight = tf.random_uniform([layer1, layer2], minval=min, maxval=max, dtype=tf.float32)
    return tf.Variable(weight)

def conv2d(images,weight):
    return tf.nn.conv2d(images,weight,strides=[1,1,1,1],padding='SAME')

def max_pool2x2(images,tname):
    return tf.nn.max_pool(images,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name=tname)


x_input = tf.reshape(images_input,[-1,112,92,3])



# 卷积核3*3*3 16个     第一层卷积
w1 = weight_init([3,3,3,16])
b1 = bias_init([16])
# 结果 NHWC  N H W C
conv_1 = conv2d(x_input,w1)+b1
relu_1 = tf.nn.relu(conv_1,name='relu_1')
max_pool_1 = max_pool2x2(relu_1,'max_pool_1')


# 卷积核3*3*16  32个  第二层卷积
w2 = weight_init([3,3,16,32])
b2 = bias_init([32])
conv_2 = conv2d(max_pool_1,w2) + b2
relu_2 = tf.nn.relu(conv_2,name='relu_2')
max_pool_2 = max_pool2x2(relu_2,'max_pool_2')

w3 = weight_init([3,3,32,64])
b3 = bias_init([64])
conv_3 = conv2d(max_pool_2,w3)+b3
relu_3 = tf.nn.relu(conv_3,name='relu_3')
max_pool_3 = max_pool2x2(relu_3,'max_pool_3')





f_input = tf.reshape(max_pool_3,[-1,14*12*64])

#全连接第一层 31*31*32,512
f_w1= fch_init(14*12*64,512)
f_b1 = bias_init([512])
f_r1 = tf.matmul(f_input,f_w1) + f_b1
f_relu_r1 = tf.nn.relu(f_r1)
f_dropout_r1 = tf.nn.dropout(f_relu_r1,drop_prob)

f_w2 = fch_init(512,128)
f_b2 = bias_init([128])
f_r2 = tf.matmul(f_dropout_r1,f_w2) + f_b2
f_relu_r2 = tf.nn.relu(f_r2)
f_dropout_r2 = tf.nn.dropout(f_relu_r2,drop_prob)


#全连接第二层 512,2
f_w3 = fch_init(128,2)
f_b3 = bias_init([2])
f_r3 = tf.matmul(f_dropout_r2,f_w3) + f_b3

f_softmax = tf.nn.softmax(f_r3,name='f_softmax')


#定义交叉熵
cross_entry =  tf.reduce_mean(tf.reduce_sum(-labels_input*tf.log(f_softmax)))
optimizer  = tf.train.AdamOptimizer(learning_rate).minimize(cross_entry)

#计算准确率
arg1 = tf.argmax(labels_input,1)
arg2 = tf.argmax(f_softmax,1)
cos = tf.equal(arg1,arg2)
acc = tf.reduce_mean(tf.cast(cos,dtype=tf.float32))


init = tf.global_variables_initializer()

sess = tf.Session()

sess.run(init)




Cost = []
Accuracy=[]
for i in range(train_epochs):
    idx=random.randint(0,len(train_data.images)-20)
    batch= random.randint(6,18)
    train_input = train_data.images[idx:(idx+batch)]
    train_labels = train_data.labels[idx:(idx+batch)]
    result,acc1,cross_entry_r,cos1,f_softmax1,relu_1_r= sess.run([optimizer,acc,cross_entry,cos,f_softmax,relu_1],feed_dict={images_input:train_input,labels_input:train_labels})
    print acc1
    Cost.append(cross_entry_r)
    Accuracy.append(acc1)

# 代价函数曲线
fig1,ax1 = plt.subplots(figsize=(10,7))
plt.plot(Cost)
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Cost')
plt.title('Cross Loss')
plt.grid()
plt.show()

# 准确率曲线
fig7,ax7 = plt.subplots(figsize=(10,7))
plt.plot(Accuracy)
ax7.set_xlabel('Epochs')
ax7.set_ylabel('Accuracy Rate')
plt.title('Train Accuracy Rate')
plt.grid()
plt.show()


#测试
arg2_r = sess.run(arg2,feed_dict={images_input:train_data.test_images,labels_input:train_data.test_labels})
arg1_r = sess.run(arg1,feed_dict={images_input:train_data.test_images,labels_input:train_data.test_labels})

print (classification_report(arg1_r, arg2_r))

#保存模型
saver = tf.train.Saver()
saver.save(sess, './model/my-gender-v1.0')