convolutional neural network example

= 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

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 container layers class source first introduced

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

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

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) 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

,... 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

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

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/

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