标签：init app vpd 图像 efault rap return for lang
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 2 13:23:27 2018
@author: myhaspl
@email:myhaspl@myhaspl.com
tf.nn.conv2d+tf.nn.maxpool
"""
import tensorflow as tf
from PIL import Image
import numpy as np
g=tf.Graph()
with g.as_default():
def getImageData(fileNameList):
imageData=[]
for fn in fileNameList:
testImage = Image.open(fn).convert(‘L‘)
testImage.show()
imageData.append(np.array(testImage)[:,:,None])
return np.array(imageData,dtype=np.float32)
imageFn=("tractor.png",)
imageData=getImageData(imageFn)
testData=tf.constant(imageData)
kernel=tf.constant(np.array(
[
[[[0.]],[[1.]],[[0.]]],
[[[1.]],[[-4.]],[[1.]]],
[[[0.]],[[1.]],[[0.]]]
])
,dtype=tf.float32)#3*3*1*1
convData=tf.nn.conv2d(testData,kernel,strides=[1,1,1,1],padding="SAME")
poolData=tf.nn.max_pool(convData,ksize=[1,2,2,1],strides=[1,1,1,1],padding=‘VALID‘)
y1=tf.cast(convData, dtype=tf.int32)
y2=tf.cast(poolData, dtype=tf.int32)
init_op = tf.global_variables_initializer()
with tf.Session(graph=g) as sess:
print testData.get_shape()
print kernel.get_shape()
resultData1=sess.run(y1)[0]
resultData2=sess.run(y2)[0]
resultData1=resultData1.reshape(resultData1.shape[0],resultData1.shape[1])
resulImage1=Image.fromarray(np.uint8(resultData1),mode=‘L‘)
resulImage1.show()
resultData2=resultData2.reshape(resultData2.shape[0],resultData2.shape[1])
resulImage2=Image.fromarray(255-np.uint8(resultData2),mode=‘L‘)
resulImage2.show()
print y1.get_shape()
中间那个图是卷积，右边那个图是池化，自己对比一下，就明白池化的威力是很大的~
图像的卷积神经网络的操作流程就是：
CNN->DNN
DNN类似于普通神经网络，但属于深度神经网络，而CNN则强调
下面的过程
卷积->池化->卷积-池化
标签：init app vpd 图像 efault rap return for lang
原文地址：http://blog.51cto.com/13959448/2320137
