cnn working

parameters, general settings k=2,n=5,α=1*e-4,β=0.75.The formula I indicates that the first core is in position (x, y) using the output of the activation function Relu, n is the number of neighboring kernel maps at the same location, and n is the total number of kernel.Reference: What is the Local Response normalization in convolutional neural Networks?Late controversial, LRN basically does not work, refer to very deep convolutional Networks for large-scale Image recognition.3. Overlapping pooli

The CNN Formula derivation 1 prefaceBefore looking at this blog, please make sure that you have read my top two blog "Deep learning note 1 (convolutional neural Network)" and "BP algorithm and Formula derivation". and has read the paper "Notes on convolutional neural Networks" in the literature [1]. Because this is the interpretation of the literature [1] The derivation process of the formula in the first part of the thesis 2

weight reproduction) and time or spatial sub-sampling to obtain some degree of displacement, scale and deformation invariance.3. CNN TrainingThe training algorithm is similar to the traditional BP algorithm. It consists of 4 steps, and these 4 steps are divided into two stages:The first stage, the forward propagation phase:A) Take a sample (X,YP) from the sample set and input X into the network;b) Calculate the corresponding actual output op.At this

In Hinton's tutorial, CNN, which is built using Python's Theano library, is an important part of it, and how is the so-called sgd-stochastic gradient descend algorithm implemented? Look at the following source (length consider only the test model function, the training function is just one more updates parameter):3 Classifier = Logisticregression (input=x, n_in=24 *, n_out=32) 7cost = classifier.negative _log_likelihood (y) test_model = t

Code reference: Http://deeplearning.net/tutorial/lenet.html#lenetCode Learning: http://blog.csdn.net/u012162613/article/details/43225445Experiment code download for this section: Github2015/4/9Experiment 1: Using the tutorial recommended CNN structural Experimentlearning_rate=0.1n_cv= 20 # First-layer convolution core 20N_vc=50 #第二层卷积核50n_epochs=200batch_size=500n_hidden=500Experimental results:Experiment 2: Add a hidden layer on the tutorial basislea

Write a tensorflow-based CNN to classify the fasion-mnist dataset. This is the fasion-mnist dataset. First, run the code and analyze: import tensorflow as tfimport pandas as pdimport numpy as npconfig = tf.ConfigProto()config.gpu_options.per_process_gpu_memory_fraction = 0.3train_data = pd.read_csv(‘test.csv‘)test_data = pd.read_csv(‘test.csv‘)def Weight(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial, tf.flo

Note: Organize the PPT from shiming teacherContent Summary 1 Development History2 Feedforward Network (single layer perceptron, multilayer perceptron, radial basis function network RBF) 3 Feedback Network (Hopfield network,Lenovo Storage Network, SOM,Boltzman and restricted Boltzmann machine rbm,dbn,cnn)Development History single-layer perceptron 1 Basic model2 If the excitation function is linear, the least squares can be calculated

This article mainly introduces about Pytorch + visdom CNN processing self-built image data set method, has a certain reference value, now share to everyone, have the need of friends can refer to Environment System: WIN10 Cpu:i7-6700hq gpu:gtx965m python:3.6 pytorch:0.3 Data download Source from Sasank chilamkurthy tutorial; Data: Download link. Download and then unzip to the project root directory: Data sets are used to classify ants and bees. There

After figuring out the fundamentals of convolutional Neural Networks (CNN), in this post we will discuss the algorithm implementation techniques based on Theano. We will also use mnist handwritten numeral recognition as an example to create a convolutional neural network (CNN) to train the network so that the recognition error reaches within 1%.We first need to read the set of training samples in mnist hand

OverviewAlthough the CNN deep convolution network in the field of image recognition has achieved significant results, but so far people to why CNN can achieve such a good effect is unable to explain, and can not put forward an effective network promotion strategy. Using the method of Deconvolution visualization in this paper, the author discovers some problems of alexnet, and makes some improvements on the

Deep Learning: Running CNN on iOS, deep learning ioscnn1 Introduction As an iOS developer, when studying deep learning, I always thought that I would run deep learning on the iPhone, whether on a mobile phone or using trained data for testing.Because the iOS development environment supports C ++, as long as your code is C/C ++, you can basically run it on iOS.How can we run CNN on iOS faster and better?2 Me

As examples of Caffe, CNN model is not a black box, Caffe provides tools to view all the outputs of the CNN layers1. View the structure of the activations values for each layer of the CNN (i.e. the output of each layer)The code is as follows:# 显示每一层for layer_name, blob in net.blobs.iteritems(): print layer_name + ‘\t‘ + str(blob.data.shape)The inner part of th

One of the key steps in the error back propagation of the CNN (Convolutional Neural network) is to pass the error of a convolution (convolve) layer to the pool layer on the previous layer, because it is 2D back in CNN, Unlike conventional neural networks where 1D is slightly different in detail, the following is a simple example of how to decompose this counter step in detail.Suppose that in a

Deep Learning: Running CNN on iOS1 Introduction As an iOS developer, when studying deep learning, I always thought that I would run deep learning on the iPhone, whether on a mobile phone or using trained data for testing.Because the iOS development environment supports C ++, as long as your code is C/C ++, you can basically run it on iOS.How can we run CNN on iOS faster and better?2 Method 1: Transcoding Us

7014Image 7044dtype: int64X.shape == (2140, 9216); X.min == 0.000; X.max == 1.000y.shape == (2140, 30); y.min == -0.920; y.max == 0.996This result tells us that the feature points of many graphs are incomplete, such as the right lip angle, only 2,267 samples. We dropped all the images with less than 15 feature points, and this line did it:DF = Df.dropna () # Drop all rows this has missing values in themTrain our network with the remaining 2140 pictures as a training se

more time. This time our network learned more general, theoretically speaking, learning more general law than to learn to fit is always more difficult.This network will take an hour of training time, and we want to make sure that the resulting model is saved after training. Then you can go to have a cup of tea or do housework, washing clothes is also a good choice.net3.fit(X, y)importas picklewith open(‘net3.pickle‘‘wb‘as f: pickle.dump(net3, f, -1)$ python kfkd.py...Epoch | Train Loss | V

effective, but when deep enough to die, because weight update, is by a lot of weight multiplied, the smaller, a bit like the gradient disappears meaning (this sentence is I added) 8: If training rnn or LSTM, It is important to ensure that the norm of the gradient is constrained to 15 or 5 (provided that the gradient is first normalized), which is significant in RNN and lstm. 9: Check the gradient below, if it is your own calculation. 10: If you use LSTM to solve the problem of long-time depende

/bottom[0]->num ();} Template__global__void Smoothl1backward (const int N, const dtype*inch, dtype*Out , Dtype sigma2) { F'(x) = Sigma * Sigma * x if |x| // =Sign (x) otherwise cuda_kernel_loop (index, N) {Dtype val=inch[index]; Dtype Abs_val=ABS (val);if(Abs_val sigma2) {Out[index]= Sigma2 *Val;} Else{Out[index]= (Dtype (0) Dtype (0)); } }} templatevoid Smoothl1losslayertop, const vectorbottom) { After forwards, Diff_ holds w_in * (B0-B1) int count=Diff_.count (); Smoothl1backward(Count, Diff_.

Call function print f (-2)Step 1 Define the input variablesA = Theano.tensor.scalar ()b =theano.tensor.matrix ()Simplified import theano.tensor as TStep 2 Define the relationship of the output variable to the input variableX1=t.matrix ()X2=t.matrix ()Y1=x1*x2Y2=t.dot (X1,X2) #矩阵乘法Step 3 declaring the functionF= theano.function ([x],y)The function input must be a list band []Example1 ImportTheano2 ImportTheano.tensor as T3 4A=T.matrix ()5b=T.matrix ()6c = A *b7D =T.dot (A, b)8f1=theano.