convolutional neural network source code

LeNet5 convolution neural network forward propagation # TensorFlow actual combat Google Depth Learning Framework 06 image recognition and convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # filename:LeNet5_infernece.py # LeNet5 forward propagate import TensorFlow

the design of BP Neural network should pay attention to the following several questions: 1. Number of layers of the network. The general three-layer network structure can approximate any rational function. Although the increase of network layer can improve the precision of c

This article from Csdn Blog, reproduced please indicate the source: http://blog.csdn.net/a635661820/article/details/44730507references: A neural probabilistic Language Modelreferring to my other NNLM study introduced the blog, this is a brief implementation of NNLM, I simplified some, the input layer to the output layer is not connected (plus the straight edge of the original paper did not significantly imp

activation functions of neural networks (Activation function) This blog is only for the author to record the use of notes, there are many details of the wrong place. Also hope that you crossing can forgive, welcome criticism correct. More related blog please poke: http://blog.csdn.net/cyh_24 If you want to reprint, please attach this article link: http://blog.csdn.net/cyh_24/article/details/50593400 In daily coding, we will naturally use some activat

Optimization algorithm is an important part of machine learning, BP Neural network is the foundation of deep Learning, BP neural network principle is very simple, almost can be understood as a logistic regression of a set way, in the previous blog post, I use r language to achieve several optimization algorithms, Based

Recurrent neural Network Language Modeling Toolkit tool use Click to open linkFollow the training schedule to learn the code:Structure in Trainnet ():Step1.learnvocabfromtrainfile () Statistics all the word information in the training file, and organize the statistic good informationThe data structures involved:Vocab_wordOcab_hash *intThe functions involved:Addwordtovocab ()For a word w, the information is

Neural networks:learning Last week's course learned the neural network forward propagation algorithm, this week's course mainly lies in the neural network reverse renewal process. 1.1 Cost function Let's recall the value function of logistic regression.J (θ) =1m[∑mi=1y (i)

)) Y.append (1) return x,yif __name__ = ' __main__ ': x1 , Y1=load_adfa_training_files ("adfa-ld/training_data_master/") x2,y2=load_adfa_java_files ("ADFA-LD/Attack_Data_ master/") x=x1+x2 y=y1+y2 #print x vectorizer = Countvectorizer (min_df=1) x=vectorizer.fit_transform (x) X=x.toarray () MLP = Mlpclassifier (hidden_layer_sizes= (150,50), max_iter=10, alpha=1e-4, solver= ' SGD ', verbose=10, tol=1e-4, random_ State=1, learning_rate_init=.1) Score=cross_validation.cross_val_score (MLP, x, Y, N_

The Network format is defined by reading a file. The file format is as follows: Input Image length input image width hidden layer neuron count output neuron countNumber of different network structures[Number of hidden layer neurons connected at different locations][Position table of input neurons connected by hidden layer neurons] The following is an example: 24 28 52 1316 321 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3

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