cnn environment

this is: in convolutional neural network CNN, the area of the input layer corresponding to an element in a certain layer of output is called the receptive field.???? Here is a partial link for reference:???? 1. The Field of sensation in deep neural networks (receptive fields)???? 2. What's a receptive field in a convolutional neural network????? 3. Painless understanding of the receptive field in CNN???? 4

the tutorial you don't really need to run the code here, but if you have a CUDA-enabled GPU on hand and want to follow the steps of the tutorial to execute the code, here are a few guidelines:1, let's say your support for Cuda's GPU is already ready, and Python2.7.x,numpy,pandas,matplotlib,scikit-learn is already installed.2, install a virtualenv, and activate the virtual environment.3. Install lasagne directly from GitHub, run the following command

use the same algorithm as BP networks. Therefore, using the existing BP network can be achieved. Open source Neural Network code Faan can be exploited. This open source implementation uses a number of code optimization techniques, with double precision, single-precision, fixed-point operation three different versions. Because the classical BP network is a one-dimensional node distribution arrangement, convolutional Neural Network is a two-dimensional network structure. So, in order to map ea

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural network structure is needed to effectively reduce the number of parameters in the neural network. convolutional Neural Networks (convolutional neural network,cnn) can do that.

CNN convolutional Neural network key points:{1} is a non-fully connected network (sparse network), compared to the BP neural network (fully connected network), of course, CNN can also have a portion of the layer is the full connection layer.{2} weight sharing (the same weight factor), which is the same as the convolution kernel (location independent), 1 convolution cores. 1 convolution cores correspond to a

1 PrefaceIn my previous blog, I introduced some of the ways to run CNN on iOS. But, in general, we need a powerful machine to run the CNN, we just need to use the resulting results for the mobile side. Before the code modified using UFLDL in MATLAB ran the 3-layer CNN of hand recognition, here we consider porting Matlab to Xcode.Step 1:matlab Turn CThe first thin

Cnn-convolutional Neural NetworksIn recent years in the field of machine vision is a very fire of acquiescence, first proposed by Yan LeCun.If you want to learn the details, see Li Feifei cs231n courseHow does it work?Give a picture, each circle is responsible for processing part of the picture.These circles form a filter.Filter identifies whether the specified pattern exists in the picture and in which region.There are 4 filter in the same color, the

This paper combines the application of deep learning, convolution neural Network for some basic applications, referring to LeCun's document 0.1 for partial expansion, and results display (in Python).Divided into the following parts:1. Convolution (convolution)2. Pooling (down sampling process)3. CNN Structure4. Run the experimentThe following are described separately.PS: This blog for the ESE machine learning short-term class reference (20140516 cours

For nearly one or two years, CNN has developed rapidly in the detection of this piece, and the following details review the development of the entire CNN testing domain model, as well as the development of time performance.First, RCNN process:Extract region (off model) + Extract features (on model) + classifyregions according feature (SVM or Softmax)Performance:Precision:Second, spp-net process:Do conv Firs

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convolutional Neural Network is the first multi-layered neural network structure which has been successfully trained, and has strong fault tolerance, self-learning and parallel processing ability.First, the basic principle1.CNN algorithm Ideasconvolutional neural network can be regarded as a special case of Feedforward network, which simplifies and improves Feedforward network mainly in network structure, in theory, the inverse propagation algorithm c

TINY-CNN is a CNN-based open Source library whose license is the BSD 3-clause. The author has also been maintaining the update, which is helpful for further mastering CNN, so the following is the compilation and use of tiny-cnn in Windows7 64bit vs2013.1. Download the source code from HTTPS://GITHUB.COM/NYANP/TINY-

Describes how tensorflow trains its own dataset to implement CNN image classification, tensorflowcnn Training image data using convolutional neural networks involves the following steps: 1. Read image files2. Generate a batch for training3. Define the Training Model (including initialization parameters, convolution, pooling layer, and other parameters and networks)4. Training 1. Read image files def get_files(filename): class_train = [] label_train

1968, Hubel on the study of the visual cortex cells of cats, put forward the concept of receptive filed, the visual cells can be divided into simple cells and complex cells, respectively, the range of the field of perception, on the basis of biology, the study of two-dimensional image convolution neural network.Traditional image classification: Feature extraction + feature expression + Classification CNN sets these methods together,One, convolutional

Keras is a Theano and TensorFlow-compatible neural network Premium package that uses him to component a neural network more quickly, and several statements are done. and a wide range of compatibility allows Keras to run unhindered on Windows and MacOS or Linux.Today to compare learning to use Keras to build the following common neural network: Regression RNN regression Classification CNN Categories RNN classification Self-