Transferred from: http://blog.csdn.net/u014380165/article/details/77284921
We know that convolutional neural Network (CNN) has been widely used in the field of image, in general, a CNN network mainly includes convolutional layer, pool layer (pooling), fully connected layer,
edge to 256 D to get B, and then in the center of B take 256*256 square picture to get C, and then randomly extract 224*224 on C as a training sample, and then in the combination of image level inverse increase the sample to achieve data gain. This gain method is 2048 times times the sample increase, allowing us to run a larger network.(2) Adjust the RGB valueThe specific idea is: To do PCA analysis of three channel, get the main component, make some
. Most likely exceptions in TestMnist.exe 0x00007ffaf3531f28: Microsoft C + + exception: Cryptopp::aes_phm_decryption::i at memory location 0x0b4e7d60 Nvalidciphertextorkey. 0x00007ffaf3531f28 most likely exception in TestMnist.exe: Microsoft C + + exception: Fl::filesystem::P athnotfound at memory location 0x0014e218. 0x00007ffaf3531f28 most likely exception in TestMnist.exe: Microsoft C + + exception: Xsd_binder::malformeddocumenterror at memory location 0X0014CF10.Off-topic, if you need to pu
C ++ convolutional neural network example: tiny_cnn code explanation (9) -- partial_connected_layer Structure Analysis (bottom)
In the previous blog, we focused on analyzing the structure of the member variables of the partial_connected_layer class. In this blog, we will continue to give a brief introduction to other member functions in the partial_connected_laye
Why use convolution?
In traditional neural networks, such as Multilayer perceptron (MLP), whose input is usually a feature vector, requires manual design features, and then the values of these features to form a feature vector, in the past decades of experience, the characteristics of artificial found is not how to use, sometimes more, sometimes less, Sometimes the selected features do not work at all (the truly functional feature is inside the vast u
Content
Overview
Word Recognition system LeNet-5
Simplified LeNet-5 System
The realization of convolutional neural network
Deep neural network has achieved unprecedented success in the fields of speech recognition, image recognition and so on. I hav
Learning Goals
Understand the convolution operation
Understand the pooling operation
Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...)
Build a convolutional neural network
Example of an artificial neural network algorithm implemented by Python [Based on the back propagation algorithm], python Artificial Neural Network
This example describes the artificial neural
First, prefaceThis convolutional neural network is the further depth of the multilayer neural network described above, which introduces the idea of deep learning into the neural network
Learning Goals
Understand multiple foundational papers of convolutional neural networks
Analyze the dimensionality reduction of a volume in a very deep network
Understand and Implement a residual network
Build a deep neural
, sensitivity_array,activator): # padding Sensitivity map Expanded_array=self.exp And_sentivity_map (Sensitivity_array) expanded_width=expanded_array.shape[2] zp= (self.input_width+self.filter _width-1-expanded_width)/2 padded_array=padding (EXPANDED_ARRAY,ZP) Self.delta_array=self.create_delta_array ( ) for F in Range (Self.filter_number): filter=self.filter[f] Filpped_weights=np.array (Map (Lamb Da I:np.rot90 (i,2), Filter.get_weights ())) Delta_array=self.create_delta_array () for D in range
handwritten fonts. Detailed code Download: http://www.demodashi.com/demo/13010.html Introduction of basic knowledgeNeural network basic knowledge of the introduction part contains a lot of formulas and graphs, using the Web site of the online editor, implementation is inadequate. I wrote a 13-page Word document, put in the understanding of the pressure pack, everyone download to see, I recorded a video, we can roughly browse a bit.Two,
ExplainThis allows us to learn to predict a person ' s identity using a Softmax output unit, where the number of classes equals the Number of persons in the database plus 1 (for the final "not in Database" Class).Reasons for the above options error:1, plus 1 explanation error:Put someone's photo into the convolutional neural network, use the Softmax unit to outpu
convolution kernel shares an offset, which is no doubt, but does the multiple convolution cores share a bias?] No, a convolution kernel shares a bias item]Four. CNN Example LeNet-5LeNet-5 is a typical convolutional neural network used to identify numbers, which has a total of 7 layers. As shown below: http://yann.lecun.com/exdb/lenet/index.html.Figure 3 LeNet-5I
convolutional Neural NetworksReprint Please specify: http://blog.csdn.net/stdcoutzyx/article/details/41596663Since July this year, has been in the laboratory responsible for convolutional neural networks (convolutional neural
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