convolutional neural network simple example

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C + + convolutional Neural Network example: TINY_CNN code detailed (11)--Layer structure container layers class source analysis

are two functions head () and tail (), the implementation mechanism is very simple, I believe you can understand:As for how to access the specified layer, TINY_CNN provides two means, one is to define the at function and type conversion through dynamic_cast:Another method is to overload the "[]" operation, and to access the array as a classThe above two methods of access are indexed (index) to complete, more convenient.OK, about the layer structure c

convolutional Neural Network (convolutional neural network,cnn)

should focus on. It also reduces the parameters of the neural network. parameter Sharing (parameter sharing): The parameters of the filter in the same convolutional layer are shared, and a filter in the filter matrix is the same regardless of the location of the convolution operation. (Of course, the same layer different filter parameters, different layers be

"Original" Van Gogh oil painting with deep convolutional neural network What is the effect of 100,000 iterations? A neural style of convolutional neural networks

calculation, the result is the same.In this example, there are differences in the results, indicating that there must be random components in the system.The random parts of machine learning are usually as follows: 1. The disorderly sequence operation of the training sample; 2. Random gradient descent; 3. The model randomly assigns the initial value.In this example, there is one more: the initial input of t

TensorFlow Study Note Five: mnist example-convolutional neural Network (CNN)

= Tf.nn.relu (conv2d (h_pool1, w_conv2) + b_conv2)#A second convolutional layerH_pool2 = Max_pool (h_conv2)#Second pooled layerW_FC1= Weight_variable ([7 * 7 * 64, 1024]) B_fc1= Bias_variable ([1024]) H_pool2_flat= Tf.reshape (H_pool2, [-1, 7*7*64])#reshape into VectorH_FC1 = Tf.nn.relu (Tf.matmul (H_pool2_flat, W_FC1) + b_fc1)#first fully connected layerKeep_prob= Tf.placeholder ("float") H_fc1_drop= Tf.nn.dropout (H_FC1, Keep_prob)#Dropout LayerW_FC

convolutional Neural Network (convolutional neural network,cnn)

between the filter parameters are not the same.) Sharing the parameters of the filter allows the content in the image to be unaffected by the position. Take mnist handwritten numeral recognition as an example, whether the number "1" appears in the upper left or bottom right corner, the type of picture is unchanged. Sharing the parameters of the convolution filter can also drastically reduce the parameters on the

C ++ convolutional neural network example: tiny_cnn code explanation (10) -- layer_base and layer Class Structure Analysis

C ++ convolutional neural network example: tiny_cnn code explanation (10) -- layer_base and layer Class Structure Analysis In the previous blog posts, we have analyzed most of the layer structure classes. In this blog post, we plan to address the last two layers, it is also the two basic classes layer_base and layer th

convolutional Neural Networks convolutional neural Network (II.)

Transfer from http://blog.csdn.net/zouxy09/article/details/8781543CNNs is the first learning algorithm to truly successfully train a multi-layered network structure. It uses spatial relationships to reduce the number of parameters that need to be learned to improve the training performance of the general Feedforward BP algorithm. In CNN, a small part of the image (local sensing area) as the lowest layer of the input of the hierarchy, the information i

convolutional Neural Networks (convolutional neural Network)

convolutional neural network for CNN. The C-layer represents all the layers that are obtained after filtering the input image, also called "convolution layer". The S layer represents the layer that the input image is sampled (subsampling) to get. Where C1 and C3 are convolution layers, S2 and S4 are the next sampling layers. Each layer in the C, S layer consists

(reproduced) convolutional Neural Networks convolutional neural network

the stratum of BP network is no longer full connection, it is locally connected . This is the simplest one-dimensional convolutional network. If we extend this idea to two-dimensional, this is the convolutional neural network we

C ++ convolutional neural network example: tiny_cnn code explanation (9) -- partial_connected_layer Structure Analysis (bottom)

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 m

convolutional Neural Network (convolutional neural Networks)

convolutional neural Network (CNN) is the foundation of deep learning. The traditional fully-connected neural network (fully connected networks) takes numerical values as input.If you want to work with image-related information, you should also extract the features from the

Using CNN (convolutional neural nets) to detect facial key points Tutorial (iii): convolutional neural Network training and data augmentation

Part five The second model: convolutional neural NetworksDemonstrates the convolution operationLeNet-5-type convolutional neural network is the core of the great breakthrough in the field of computer vision recently. The convolution layer differs from the previous fully conn

UFLDL Learning notes and programming Jobs: convolutional neural Network (convolutional neural Networks)

,... filterdim,numfilt ers,pooldim,pred)% calcualte cost and gradient for a single layer convolutional% neural network followed by a Softmax Laye R with cross entropy% objective.%% parameters:% theta-unrolled parameter vector% ima Ges-stores images in Imagedim x Imagedim x numimges% array% Numclasses-number of classes to pred ict% Filterdim-dimension of

Deep learning Note (i) convolutional neural network (convolutional neural Networks)

output.Displays the size of the resulting output image with a 3x3 grid on the 28x28 image using different step sizes and fill methods:The following is an understanding of the convolution process with two dynamic graphs:The first is a convolution process that is effectively populated with a 3x3 grid on a 5x5 image:The second is the convolution process with the same padding on the 5x5 image using a 3x3 grid, moving in the following way: http://cs231n.github.io/

The parallelization model of convolutional neural network--one weird trick for parallelizing convolutional neural Networks

I've been focusing on CNN implementations for a while, looking at Caffe's code and Convnet2 's code. At present, the content of the single-machine multi-card is more interested, so pay special attention to Convnet2 about MULTI-GPU support.where Cuda-convnet2 's project address is published in: Google Code:cuda-convnet2A more important paper on MULTI-GPU is: one weird trick for parallelizing convolutional neural

convolutional neural Network (ii): convolutional neural network BP algorithm for CNN

,In the above formula, the * number is the convolution operation, the kernel function k is rotated 180 degrees and then the error term is related to the operation, and then summed.Finally, we study how to calculate the partial derivative of the kernel function connected with the convolution layer after obtaining the error terms of each layer, and the formula is as follows.The partial derivative of the kernel function can be obtained when the error item of the convolution layer is rotated 180 deg

Application of CNN convolutional Neural network in natural language processing

the matrix range default to 0. This makes it possible to filter each element of the input matrix and output a matrix of the same size or larger. The complement 0 method is also called the wide convolution, the method that does not use the complement zero is called the narrow convolution. Example of 1D:Narrow convolution vs wide convolution. The filter length is 5 and the input length is 7. Source: A convolutional

Python programming simple neural network algorithm example, python Neural Network

Python programming simple neural network algorithm example, python Neural Network This example describes the simple

005-convolutional Neural Network 01-convolutional layer

Network Steps to do: (a Chinese, teach Chinese, why write a bunch of English?) )1, sample Abatch of data (sampling)2,it through the graph, get loss (forward propagation, get loss value)3,backprop to calculate the geadiets (reverse propagation calculation gradient)4,update the paramenters using the gradient (using gradient update parameters)What convolutional neural

Spark MLlib Deep Learning convolution neural network (depth learning-convolutional neural network) 3.3

3. Spark MLlib Deep Learning convolution neural network (depth learning-convolutional neural network) 3.3Http://blog.csdn.net/sunbow0Chapter III Convolution neural Network (

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