how to train convolutional neural network

require a lot of data and strong hardware computing power. Previously limited by data volume and computing power, has been tepid. In recent years the Internet has flourished, all kinds of information have been realized data, the amount of data is greatly increased, you think of your online shopping when you stay on the Internet information you know. In addition, the computer hardware in accordance with the "Moore's Law" development, the exponential growth of computing power, which provides a go

train the model on the side of the reservation. Another way to model compression is through weight sharing. Assuming that the adjacent two layers are fully connected, each layer has 1000 nodes, then there are 1000 times 1000 or 1 million weights (parameters) between the two layers. We can do a cluster of 1 million weights to see which weights are very close, we can use the mean of each class instead of these weights that belong to this category, so m

= Accuracy.eval (feed_dict={x:mnist.test.images, Y:mnist.test.labels, keep_prob:1.0}) print ("Step:%d cost:%.9f TRAIN accuracy:%.3f TES T accuracy:%.3f "% (I, Avg_cost, Train_accuracy, test_accuracy))" Print ("Done") After training iterations 1000 times, the test classification accuracy rate reached 98.6% step:999 cost:0.000048231 TRAIN accuracy:0.990 TEST accuracy:0.986 Reached 99.1% at 2000 times.

Learning notes TF055: TensorFlow neural network provides a simple one-dimensional quadratic function. tf055tensorflow TensorFlow running mode. Load data, define hyperparameters, build networks, train models, evaluate models, and predict. Construct raw data that satisfies the quadratic function y = ax ^ 2 + B, and construct the simplest

Perceptron, as the most basic unit in artificial neural network, has multiple inputs and an output component. Although our goal is to learn a lot of neural network interconnection, but we still need to first study the individual neural unit.The main flow of the perceptron al

Title: "Python realizes BP neural network recognition mnist data Set"date:2018-06-18t14:01:49+08:00Tags: [""]Categories: ["Python"] ObjectiveThe training set read in the. MAT format when testing the correct rate with a PNG-formatted pictureCode#!/usr/bin/env Python3# Coding=utf-8ImportMathImportSysImportOsImportNumPy asNp fromPILImportImageImportScipy.io asSiodefSigmoid (x):returnNp.array (List(Map(La

Reference: Artificial neural network-Han Liqun pptlooking at some of the language models based on neural networks, compared with traditional language models, there is no need for additional smoothing algorithms In addition to the amount of computational effort, which makes them surprisingly effective. These networks can see the shadow of BP, it can be said that

of calculating the hidden layer. Usually, the same excitation function is taken.Forward algorithm: Used to classify a piece of data. Through the above calculation method, we can obtain an output, compare this output with the threshold value, can produce the classification result.Back algorithm: Used to train the model, adjust the weights.e represents the error of the classification and true classification of Ann predictions. Our goal is to change e a

googlenet incepetion V1This is the earliest version of Googlenet, appearing in the 2014 going deeper with convolutions. It is called "googlenet" rather than "googlenet", and the article says it is to salute the early lenet.IntroducedDeep learning and the rapid development of neural networks, people are no longer focused on more hardware, larger datasets, larger models, but more attention to new idea, new algorithms and model improvements.In general, t

In 1987, American scholar Robert Hecht-nielsen proposed a dual propagation neural network model (Counter propagation NETWORK,CPN), which was first used to implement sample selection matching systems. CPN can store binary or analog values of the mode pair, so this network model can also be used for associative storage,

Undergraduate graduation design involves using machine learning methods to train predictive models, linear regression, SVM, RF and other methods are not ideal, so we need to use a simple neural network method to do comparative experiments. Without in-depth understanding of the optimization of the NN, the interface provided by the R package is called directly, whe

first, the initialization of Proper weight initialization can prevent gradients from exploding and disappearing. For Relu activation functions, weights can be initialized to: Also known as "he initialization". For Tanh activation functions, the weights are initialized to: Also known as "Xavier initialization". You can also use the following formula to initialize: In the above formula, L refers to the first layer of the neural

is engaged in the subject of intelligent prosthesis, need to use, this to my implementation to play a very good guiding significance, hereby reproduced, and hereby thank the author, https://blog.csdn.net/qingelife/article/details/78429508 Use the color sensor to read the color of the ph test strip and then get the ph value he represents. At first, I wanted to fit a function about RGB and ph, but it always worked poorly. The neural

Python Image Processing (14): Neural Network Classifier and python Image Processing Happy shrimp Http://blog.csdn.net/lights_joy/ Reprinted, but keep the author information Opencv supports neural network classifier. This article attempts to call it in python. Like the Bayesian classifier, the

The following figure shows the implementation of a back propagation algorithm for a three-layer neural network: Each neuron is composed of two cells. One is the weight and the input signal. The other is the nonlinear element, called the excitation function. The signal e is the excitation signal. y = f (e) is the output of the non-linear element, which is the output of the neuron. In order to

Foundation of Neural Network (Early Warning: This section begins with mathematical notation and the necessary calculus, linear algebra Operations) Overview of this section As mentioned in the previous lecture, "Learning" is about getting the computer to automatically implement a complex function that completes the mapping from input x to output Y. The basic framework of machine learning is shown in the fol

on the net said Relu case, the use of Gaussian random number initialization effect will be better. So want to try this, whether or not with the initialization of the horizontal vertical relationship. Are randomly initialized, the difference is so big ... And is sigmod to this no influence, relu situation to this absolutely fatal .... 2016-5-19: Repeat the test again for the above problem. Discovering that the initialization order is slightly different, the relu may diverge, and sigmod will not.

the fifth chapter uses the SVM and the neural network the license plate recognitionTags: license plate recognition 2014-03-13 21:23 1115 people Read reviews (0) Favorite report Category: Images (42) Directory (?) [+] "Original: http://blog.csdn.net/raby_gyl/article/details/11617875" Title: "Mastering OpenCV with practical computer Vision Projects" because added a * number, display garbled, do not know how

Although the neural network has a very complete and useful framework, and BP Neural network is a relatively simple and inefficient one, but for the purpose of learning to achieve this neural network is still meaningful, I think. T

Layer 2, respectively, the length of the antennae and wings, only responsible for input 2, the processing layer has 3 (through the experiment or certain experience to determine), has the responsibility calculates 3, the output layer has 2, is responsible for the calculation Then we have to use the existing data to determine the weight, the method used for the backward propagation algorithmThird, backward propagation algorithm Then it solves a nonlinear programming problem, which can be solve