convolution

问题 Hi. I'm new to Tensorflow and trying to run cifar10 dataset with CNN. My Network is constructed with three layers such as Convolution + Max Pooling Fully Connected Layer Softmax Layer Below is my tensorflow code of the model. 15 def model(X, w, w2, w_o, p_keep_conv, p_keep_hidden): 16 17 layer1 = tf.nn.relu(tf.nn.conv2d(X, w,strides=[1, 1, 1, 1], padding='SAME')) 18 layer1 = tf.nn.max_pool(l1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') 19 20 layer1 = tf.reshape(l1,[-1,w2.get

问题 I have the following code which is used to deconvolve a signal. It works very well, within my error limit...as long as I divide my final result by a very large factor (11000). width = 83.66; x = linspace(-400,400,1000); a2 = 1.205e+004 ; al = 1.778e+005 ; b1 = 94.88 ; c1 = 224.3 ; d = 4.077 ; measured = al*exp(-((abs((x-b1)./c1).^d)))+a2; rect = @(x) 0.5*(sign(x+0.5) - sign(x-0.5)); rt = rect(x/83.66); signal = conv(rt,measured,'same'); check = (1/11000)*conv(signal,rt,'same'); Here is what I

问题 When I use caffe for image classification, it often computes the image mean. Why is that the case? Someone said that it can improve the accuracy, but I don't understand why this should be the case. 回答1: Neural networks (including CNNs) are models with thousands of parameters which we try to optimize with gradient descent. Those models are able to fit a lot of different functions by having a non-linearity φ at their nodes. Without a non-linear activation function, the network collapses to a

问题 When I use caffe for image classification, it often computes the image mean. Why is that the case? Someone said that it can improve the accuracy, but I don't understand why this should be the case. 回答1: Neural networks (including CNNs) are models with thousands of parameters which we try to optimize with gradient descent. Those models are able to fit a lot of different functions by having a non-linearity φ at their nodes. Without a non-linear activation function, the network collapses to a

问题 Alright, so I am doing some signal processing and I have a signal that is a convolution of a slit of width 83.66 microns and a response function. I want to use the convolution property of fourier transforms to find the response. The code I used is as follows: num=xlsread('Data.xlsx','B2:C13'); y=num(13:end); x=num(1:12); width = 83.66; p = width*sqrt(1/(2*pi))*sinc((x)*(width/2)); slit = abs(fftshift(ifft(p))); figure subplot(5,1,1);plot(x,y);title('imported data') subplot(5,1,2);plot(x,p)

问题 Assuming I already have a working convolution matrix algorithm in place, I'm looking for practical examples of general matrices that are useful for image processing in particular. The canonical examples you'll find everywhere are non-gaussian box blur: 1 1 1 1 1 1 1 1 1 Image sharpening: 0 -1 0 -1 5 -1 0 -1 0 Edge detection: 0 1 0 1 -4 1 0 1 0 and emboss: -2 -1 0 -1 1 1 0 1 2 Are there more I'm missing? 回答1: have you ever played with jitter, the image processing framework that's part of max

问题 Assuming I already have a working convolution matrix algorithm in place, I'm looking for practical examples of general matrices that are useful for image processing in particular. The canonical examples you'll find everywhere are non-gaussian box blur: 1 1 1 1 1 1 1 1 1 Image sharpening: 0 -1 0 -1 5 -1 0 -1 0 Edge detection: 0 1 0 1 -4 1 0 1 0 and emboss: -2 -1 0 -1 1 1 0 1 2 Are there more I'm missing? 回答1: have you ever played with jitter, the image processing framework that's part of max

问题 Thought I'd try my hand at a little (auto)correlation/convolution today in openCV and make my own 2D filter kernel. Following openCV's 2D Filter Tutorial I discovered that making your own kernels for openCV's Filter2D might not be that hard. However I'm getting unhandled exceptions when I try to use one. Code with comments relating to the issue here: #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" #include <stdlib.h> #include <stdio.h> #include <iostream> using

问题 I have a function that returns a bounded noise. For example, let's imagine that out input range is [-1, 1]. With my method I can return a bounded/in range noise (depending on the biome we are currently). /// <summary> /// Converts the range. /// </summary> /// <param name="originalStart">The original start.</param> /// <param name="originalEnd">The original end.</param> /// <param name="newStart">The new start.</param> /// <param name="newEnd">The new end.</param> /// <param name="value">The

问题 I have a function that returns a bounded noise. For example, let's imagine that out input range is [-1, 1]. With my method I can return a bounded/in range noise (depending on the biome we are currently). /// <summary> /// Converts the range. /// </summary> /// <param name="originalStart">The original start.</param> /// <param name="originalEnd">The original end.</param> /// <param name="newStart">The new start.</param> /// <param name="newEnd">The new end.</param> /// <param name="value">The