#python3代码
#TensorFlow卷积神经网络实现，图片与对应标签的拟合
#程序不报错，一直显示正在运行
#请问错在哪里？
#数据集 https://pan.baidu.com/s/1Z7_ULAW3LFrUk4WkHC0s3Q
import os
import numpy as np
from PIL import Image
import tensorflow as tf
import matplotlib.pyplot as plt
#-------------获取数据集，构造批数据：batch-----------------------------------------
def get_files(file_dir):
pictures = []
numbers = []
for file in os.listdir(file_dir): # 7000.jpg
name = file.split(sep='.')
pictures.append(file_dir+"\\"+file)
numbers.append(name[0])
print('There are %d pictures'%(len(pictures)))
temp = np.array([pictures,numbers])
temp = temp.transpose()
np.random.shuffle(temp)
pictures_list = list(temp[:,0])
numbers_list = list(temp[:,1])
#numbers_list = [int(float(i)) for i in numbers_list]
return pictures_list,numbers_list
#返回两个list
def get_batch(image,label,image_W,image_H,batch_size,capacity): # image = pictures label = numbers
image = tf.cast(image,tf.string)
label = tf.cast(label,tf.int32)
#tf.cast()用来做类型转换
input_queue = tf.train.slice_input_producer([image,label])
#加入队列
label = input_queue[1]
image_contents = tf.read_file(input_queue[0])
image = tf.image.decode_jpeg(image_contents,channels=3)
#jpeg或者jpg格式都用decode_jpeg函数，其他格式可以去查看官方文档
image = tf.image.resize_image_with_crop_or_pad(image,image_W,image_H)
#resize
image = tf.image.per_image_standardization(image)
#对resize后的图片进行标准化处理
image_batch,label_batch = tf.train.batch([image,label],batch_size = batch_size,num_threads=16,capacity = capacity)
label_batch = tf.reshape(label_batch,[batch_size])
return image_batch,label_batch
#获取两个batch，两个batch即为传入神经网络的数据
#-----------卷积神经网络节点，搭建卷积神经网络----------------------------------------------------
def conv_op(input_op,name,kh,kw,n_out,dh,dw):
n_in = input_op.get_shape()[-1].value
with tf.name_scope(name) as scope:
kernel=tf.get_variable(scope+"w",
shape=[kh,kw,n_in,n_out],dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer_conv2d())
conv=tf.nn.conv2d(input_op,kernel,(1,dh,dw,1),padding='SAME')
bias_init_val=tf.constant(0.0,shape=[n_out],dtype=tf.float32)
biases=tf.Variable(bias_init_val,trainable=True,name='b')
z=tf.nn.bias_add(conv,biases)
activation=tf.nn.relu(z,name=scope)
return activation
def fc_op(input_op,name,n_out):
n_in=input_op.get_shape()[-1].value
with tf.name_scope(name) as scope:
kernel = tf.get_variable(scope+"w",shape=[n_in,n_out],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
biases=tf.Variable(tf.constant(0.1,
shape=[n_out],dtype=tf.float32),name='b')
activation=tf.nn.relu_layer(input_op,kernel,biases,name=scope)
return activation
def fc_end_layer(inputs,in_size,out_size,activation_function=None):
Weights=tf.Variable(tf.random_normal([in_size,out_size]))
biases=tf.Variable(tf.zeros([1,out_size])+0.1)
Wx_plus_b=tf.matmul(inputs,Weights)+biases
if activation_function is None:
outputs=Wx_plus_b
else:
outputs=activation_function(Wx_plus_b)
return outputs
def mpool_op(input_op,name,kh,kw,dh,dw):
return tf.nn.max_pool(input_op,ksize=[1,kh,kw,1],
strides=[1,dh,dw,1],padding='SAME',name=name)
def inference_op(input_op,keep_prob):
conv1_1=conv_op(input_op,name="conv1_1",kh=3,kw=3,n_out=64,dh=1,
dw=1)
conv1_2=conv_op(conv1_1,name="conv1_2",kh=3,kw=3,n_out=64,dh=1,
dw=1)
pool1=mpool_op(conv1_2,name="pool1",kh=2,kw=2,dw=2,dh=2)
conv2_1=conv_op(pool1,name="conv2_1",kh=3,kw=3,n_out=128,dh=1,
dw=1)
conv2_2=conv_op(conv2_1,name="conv2_2",kh=3,kw=3,n_out=128,dh=1,
dw=1)
pool2=mpool_op(conv2_2,name="pool2",kh=2,kw=2,dh=2,dw=2)
conv3_1=conv_op(pool2,name="conv3_1",kh=3,kw=3,n_out=256,dh=1,
dw=1)
conv3_2=conv_op(conv3_1,name="conv3_2",kh=3,kw=3,n_out=256,dh=1,
dw=1)
conv3_3=conv_op(conv3_2,name="conv3_3",kh=3,kw=3,n_out=256,dh=1,
dw=1)
pool3=mpool_op(conv3_3,name="pool3",kh=2,kw=2,dh=2,dw=2)
conv4_1=conv_op(pool3,name="conv4_1",kh=3,kw=3,n_out=512,dh=1,
dw=1)
conv4_2=conv_op(conv4_1,name="conv4_2",kh=3,kw=3,n_out=512,dh=1,
dw=1)
conv4_3=conv_op(conv4_2,name="conv4_3",kh=3,kw=3,n_out=512,dh=1,
dw=1)
pool4=mpool_op(conv4_3,name="pool4",kh=2,kw=2,dh=2,dw=2)
conv5_1=conv_op(pool4,name="conv5_1",kh=3,kw=3,n_out=512,dh=1,
dw=1)
conv5_2=conv_op(conv5_1,name="conv5_2",kh=3,kw=3,n_out=512,dh=1,
dw=1)
conv5_3=conv_op(conv5_2,name="conv5_3",kh=3,kw=3,n_out=512,dh=1,
dw=1)
pool5=mpool_op(conv5_3,name="pool5",kh=2,kw=2,dw=2,dh=2)
shp=pool5.get_shape()
flattened_shape=shp[1].value*shp[2].value*shp[3].value
resh1=tf.reshape(pool5,[-1,flattened_shape],name="resh1")
fc6=fc_op(resh1,name="fc6",n_out=4096)
fc6_drop=tf.nn.dropout(fc6,keep_prob,name="fc6_drop")
fc7=fc_op(fc6_drop,name="fc7",n_out=4096)
fc7_drop=tf.nn.dropout(fc7,keep_prob,name="fc7_drop")
fc8=fc_op(fc7_drop,name="fc8",n_out=1000)
prediction=fc_end_layer(fc8,1000,1,activation_function=None)
return prediction
#---------构造损失节点：loss 优化节点：optimizer 训练节点:train---------------------------------------
x=tf.placeholder(tf.float32,[None,28,28,3])
y=tf.placeholder(tf.float32,[None,1])
prediction = inference_op(x,0.5)
loss = tf.reduce_mean(tf.square(y - prediction))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
#--------运行网络--------------------------------------------------
IMG_W = 28
IMG_H = 28
BATCH_SIZE = 4
CAPACITY = 24
MAX_STEP = 100
learning_rate = 0.0005
train_dir = 'D:\VGGNet-16\fatigue_cycle_picture'
pictures_list,numbers_list = get_files(train_dir)
image_batch,label_batch = get_batch(pictures_list,numbers_list,IMG_W,IMG_H,BATCH_SIZE,CAPACITY)
#label_batch=tf.to_float(label_batch)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(100):
print(i)
print(sess.run(image_batch))
sess.run(train,feed_dict={x:image_batch.eval(session=sess),y:label_batch.eval(session=sess)})
sess.close()
