hinton neural networks

(Net,s_train ', T);Y=sim (Net,s_sample ');Cla=y (:, 1);Case 9% single layer competitive neural networkMm=s_train (:);Mm=minmax (mm ');Q=repmat (Mm,size (s_train,2), 1);NET=NEWC (q,2,0.1);Net=init (NET);net.trainparam.showwindow=0;net.trainparam.epochs=20;Net=train (Net,s_train ');A=sim (Net,s_train ');Cla=vec2ind (s_sample ') ';Case 10%BP Neural NetworkMm=s_train (:);Mm=minmax (mm ');NET=NEWFF (Repmat (Mm,

[1] Z. Zhou, Y. Huang, W. Wang, L. Wang, T. Tan, Ieee, see the Forest for the Trees:joint Spatial and temporal recurrent Neural Networks for video-based person re-identification, 30th Ieee Conference on computer Vision and Pattern recognition, (Ieee, New York), pp. 6776-6785.Summary:Surveillance cameras are widely used in different scenarios. The need to identify people under different cameras is a pedestri

music, so the best algorithm convergence after the test . Many of the world's documents I've tested are like strum.2. Shortly after the start of the project, there is a forum dedicated to exchanging learning experiences and questions, point here. The above comment is the problem I encountered, if you encounter a new problem, you can post to the forum for help. I see some people generate music that has that weird Gothic-style haha.3. The specific principles behind this project I did not write, o

This paper summarizes some contents from the 1th chapter of Neural Networks and deep learning.learning with gradient descent algorithm (learning with gradient descent)1. TargetWe want an algorithm that allows us to find weights and biases so that the output y (x) of the network can fit all the training input x.2. Price functions (cost function)Define a cost function (loss function, objective function): The

Label: style blog HTTP color ar SP 2014 art log 1. Basic Structure of Neural Networks Neural Network: N inputs, m middle layers, and K output layers X indicates the input, W indicates the input weight to the middle layer, V indicates the weight from the middle layer to the output, and y indicates the network output. Threshold indicates the threshold of the in

Scalable Object Detection using deep neural Networksauthor : Dumitru Erhan, Christian szegedy, Alexander Toshev, and Dragomir Anguelovreferences : Erhan, Dumitru, et al. "Scalable object detection using deep neural networks." Proceedings of the IEEE Conference on computer Vision and Pattern recognition. 2014.citations : 181 (Google scholar, by 2016/11/23).Project

evolution of deep neural networks in image recognition applications"Minibatch" You use a data point to calculate to modify the network, may be very unstable, because you this point of the lable may be wrong. At this point you may need a Minibatch method that averages the results of a batch of data and modifies it in their direction. During the modification process, the change intensity (learning rate) can b

Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489.Alphago's thesis, the main use of the RL technology, do not know before the use of RL to do Weiqi.Proposed two networks, one is the strategy network, one is the value network, all through the self-battle realization.Policy Network:The strate

is: F ' (x) = f (x) * (1-f (x)), calculation is very convenient. The code is as follows:1%%compute gradients using backpropagation2 3%%% YOUR CODE here%%%4%Output Layer5Output =Zeros (Size (Pred_prob));6Output (Index) = 1;7Error = Pred_prob-output;8 9 forL = numhidden+1: -1:1Tengradstack{l}.b = SUM (error,2); One if(L = = 1) AGradstack{l}. W = Error * Data'; - Break; - Else theGradstack{l}. W = error * HACT{L-1}'; - End -Error = (Stack{l}. W'*error. * HACT{L-1}. * (1-hact{l-1}

,x_test) Y_prediction_train = Predict (W,b,x_train) # # # # END CODE here # # # print train/test Errors print ("Train accuracy: {}% ". Format (100-np.mean (Np.abs (y_prediction_train-y_train) *)) print (" Test accuracy: {}% ". Format (100-np.me An (Np.abs (y_prediction_test-y_test)) *) d = {"Costs": costs, "y_prediction_test": Y_prediction_test, "Y_prediction_train": Y_prediction_train, "W": W, "B": B, "learning_rate": Learnin G_rate, "Num_iteRations ": num_iterations} print (d[" costs

\):The chain rules are updated as follows:\[\begin{split}\frac{c_0}{\partial \omega_{jk}^{(L)}}= \frac{\partial z_j^{(L)}}{\partial \omega_{jk}^{(l)}}\ Frac{\partial a_j^{(L)}}{\partial z_j^{(l)}}\frac{\partial c_0}{\partial a_j^{(L)}}\=a^{l-1}_k \sigma\prime (z^ {(l)}_j) 2 (a^{(l)}_j-y_j) \end{split}\]And to push this formula to other layers ( \frac{c}{\partial \omega_{jk}^{(L)}}\) , only the \ (\frac{\partial c}{\partial a_j^{) in the formula is required ( L)}}\) .Summarized as follows:Therefo

useful when combined with a number of different random subsets of other neurons. The first two fully connected layers use dropout. Without dropout, our network would show a lot of overfitting. The dropout increases the number of iterations required for convergence by roughly one-fold.4. Image preprocessing① size NormalizationTo 256x256 all the pictures to the size of the scale, as for why not directly normalized to 224 (227), please refer to the above-mentioned expansion of the dataset operatio

1 Introduction In this article, we will introduce a framework aforge using C #, which allows you to easily manipulate artificial networks, computer vision, machine learning, image processing, genetic algorithms, etc. Introduction of 2 neural network design part framework Here, I want to emphasize: This piece of code is very beautiful, a code such as poetic beauty, let me charmed. This piece of code is i

http://blog.csdn.net/pipisorry/article/details/4397356Machine learning machines Learning-andrew NG Courses Study notesNeural Networks Representation Neural network representationnon-linear Hypotheses Nonlinear hypothesisNeurons and the brain neurons and brainsModel representation models representExamples and intuitions examples and intuitive knowledgeMulticlass Classification Multi-class classificationfrom:

self.nodesinLayers.append (int (SELF.OUTPUTDI)) #self. nodesinb=[] #self. nodesinb + = self. Nodesinhidden #self. Nodesinb.append (int (SELF.OUTPUTDI)) #for element in Self.nodesinlayers: #self . Nodesinlayers=int (Self.nodesinlayers[idx]) #weight matrix, it's a list and each element is a numpy matrix # Weight matrix, here are Wij, and in BP we could inverse it into Wji #here we store the matrix as Numpy.array SE Lf.weightmatrix=[] Self. B=[] for IDX in range (0,self.NL-1): #Xaxier ' s scaling

Idea: Using RNN to model users ' browsing order, using FNN to simulate CF, two networks learning togetherRNN Network structure:The state of the output layer represents a page that a user browses, which can be seen as a one-hot representation, and STATE0 to 3 is the page that is browsed in turn. Because RNN input number is limited, if the user browses too many pages, then will lose the first of those pages, paper in order to retain this part of the inf

of the word vector effect is also possible.Channel (Channels): An image can take advantage of (R, G, B) as a different channel, while the input channel of the text is usually a different way of embedding (such as Word2vec or glove), In practice, the use of static word vectors and fine-tunning-word vectors as different channel methods are also used.One dimensional convolution (conv-1d): The image is a two-dimensional data, the word vector expression of the text is one-dimensional data, so in tex

. We use the cublas. lib and curand. Lib libraries. One is matrix calculation and the other is random number generation. I applied for all the memory I needed at one time. After the program started running, there was no data exchange between the CPU and GPU. This proved to be very effective. The program performance is about dozens of times faster than the original C language version (if the network is relatively large, it can reach a speed-up ratio of about one hundred times ). Each EPOS uses 16

== output Dimension Model.add (Activation (' Relu ')) # Activation function Model.add (dropout (0.5)) #dropout#第二层model. Add (Dense (nb_classes)) Model.add (Activation (' Softmax '))#损失函数设置, optimization function, metric model.compile (loss= ' categorical_crossentropy ', optimizer= ' Adam ', metrics=[' Accu Racy '])#训练, cross-validation history = Model.fit (X_train, Y_train, Nb_epoch=nb_epoch, Batch_size=batch_size, Verbose=1, validation_split=0.1) score = Model.evaluate (X_test, Y_test, Batch_s

weight update, is by a lot of weight multiplied, the smaller, a bit like the gradient disappears meaning (this sentence is I added) 8: If training rnn or LSTM, It is important to ensure that the norm of the gradient is constrained to 15 or 5 (provided that the gradient is first normalized), which is significant in RNN and lstm. 9: Check the gradient below, if it is your own calculation. 10: If you use LSTM to solve the problem of long-time dependencies, remember to initialize bias 12: As far as