dropout neural network code

, where ' DW ', ' DB ' is for easy representation in Python code, and the real meaning is the right equation (differential): ' DW ' = DJ/DW = (dj/dz) * (DZ/DW) = x* (a-y) t/m ' db ' = dj/db = SUM (a-y)/M So the new values are: w = w–α* DW b = b–α* db, where alpha is the learning rate, with the new W, b in the next iteration. Set the number of iterations, after the iteration, is the final parameter W, b, using test cases to verify the recognition accur

, forcing the algorithm to adjust the score according to the size of the data set of the different classifications. This is not the ideal solution. In correspondence with simplicity (naive), a text classifier does not attempt to understand the meaning of a sentence, but simply classifies it. It is important to understand that the so-called intelligent chat robot does not really understand the human language, but that is another matter. If you're new to artificial

If you use 100k batch in this model, and combine the decay of learning rate (that is, the rate of learning is reduced by a ratio every once in a while), the correct rate can be as high as 86%. There are about 1 million parameters to be trained in the model, and the total amount of arithmetic to be estimated is about 20 million times. So this convolution neural network model, using some techniques.(1) Regula

') print "%s:loss after num_examples_seen=%d epoch=%d:%f"% (time, Num_examples_seen, epoch, Loss) # Adjust The learning rate if loss increases if (Len (losses) > 1 and losses[-1][ 1] > Losses[-2][1]): learning_rate = learning_rate * 0.5 print "Setting Learni NG rate to%f "% learning_rate Sys.stdout.flush () # added! Saving model Oarameters save_model_parameters_numpy ("./data/rnn-numpy-%d-%d-%s.npz"% (Self.hidden_dim, SE Lf.word_dim, time), self] # for each training example ...

layer nodes The number of hidden layer nodes has little effect on the recognition rate, but the number of nodes increases the computational capacity and makes training SLOW. selection of the activation function The activation function has a significant effect on the recognition rate or the rate of convergence. The precision of s-shape function is much higher than that of linear function in the approximation of the higher curve, but the computational amount is much larger. the choice of learning

"Self-built Neural Networks" is an e-book. It is the first and only Neural Network book on the market that uses Java. What self-built Neural Networks teach you: Understand the principles and various design methods of neural networks, and make it easy to use ground gas; Unde

, computer vision and other fields. The Neuro directory under the Aforge.net source code contains a neural network class library.Aforge.net Home: http://www.aforgenet.com/Aforge.net Code Download: http://code.google.com/p/aforge/The class diagram for the Aforge.neuro project is as follows:Figure 10. Class diagram of Af

UFLDL Learning notes and programming Jobs: convolutional neural Network (convolutional neural Networks)UFLDL out a new tutorial, feel better than before, from the basics, the system is clear, but also programming practice.In deep learning high-quality group inside listen to some predecessors said, do not delve into other machine learning algorithms, you can direc

example, you is going to generate an image of the Louvre Museum in Paris (content image C), mixed with a painting By Claude Monet, a leader of the Impressionist movement (style image S). Let's see how you can do this. 2-transfer Learning Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of. The idea of using a network

Deep learning--the artificial neural network and the upsurge of researchHu XiaolinThe artificial neural network originates from the last century 40 's, to today already 70 years old. Like a person's life, has experienced the rise and fall, has had the splendor, has had the dim, has had the noisy, has been deserted. Gen

][neuron]. An array of O[neuron] records the output of a neuron through an activation function, Outputdata[out] stores the BP neural network.Process of program executionHere, the execution details of the specific function are not considered, and the procedure is introduced in general. With pseudo-code to represent, the specific content of the following step-by-step introduction, as follows:The main function

convolutional Neural Network (convolutional neural network,cnn), weighted sharing (weight sharing) network structure reduces the complexity of the model and reduces the number of weights, which is the hotspot of speech analysis and image recognition. No artificial feature ex

1 Introduction An XOR operation is a commonly used calculation in a computer: 0 XOR 0 = 0 0 XOR 1 = 1 1 XOR 0 = 1 1 XOR 1 = 0 We can use the code in the first article to calculate this result Http://files.cnblogs.com/gpcuster/ANN1.rar (need to modify the training set), we can find that the results of learning does not satisfy us, because the single layer of neural

;iThe core code for the feedback operation of the sub-sampling layer is as follows:Subsamplinglayer::bprop (INPUT,OUTPUT,IN_DX,OUT_DX) { //gradient passed dsigmoid sum_dx = dsigmoid (OUT_DX); Calculates the gradient for bias and Coeff for (i=0;i5. The advantages of CNNConvolutional Neural Networks CNN is mainly used to identify two-dimensional graphs of displacement, scaling and other forms of distorted

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

UFLDL Learning notes and programming Jobs: multi-layer neural Network (Multilayer neural networks + recognition handwriting programming)UFLDL out a new tutorial, feel better than before, from the basics, the system is clear, but also programming practice.In deep learning high-quality group inside listen to some predecessors said, do not delve into other machine l

. ）The original input 227\227 pixel image will become 6\*6 so small, the main reason is due to the reduction of sampling (pooling layer),Of course, the convolution layer will also make the image smaller, one layer down, the image is getting smaller.4, module Six, seven or eightModules six and seven is the so-called fully connected layer, the whole connection layer and the structure of artificial neural network