python neural network library

Python uses numpy to implement the BP neural network, numpybp This article uses numpy to implement a simple BP neural network. Because it is used for regression rather than classification, the incentive function selected at the output layer is f (x) = x. The principle of BP

Learning means finding a set of weights on the training data to minimize the loss function; Learning process: Calculates the gradient value of the loss function corresponding to the weight coefficient in the small batch data, then the weight coefficient moves along the gradient in the opposite direction; The probability of the learning process is based on the neural network is a series of

Title: "Python realizes BP neural network recognition mnist data Set"date:2018-06-18t14:01:49+08:00Tags: [""]Categories: ["Python"] ObjectiveThe training set read in the. MAT format when testing the correct rate with a PNG-formatted pictureCode#!/usr/bin/env Python3# Coding=utf-8ImportMathImportSysImportOsImportN

Python-based radial basis function (RBF) neural network example, pythonrbf This article describes the radial basis function (RBF) neural network implemented by Python. We will share this with you for your reference. The details ar

Wunda Depth Learning lesson five programming question one Import Module Import NumPy as NP from rnn_utils Import * Circular Neural Network small unit forward propagation # graded Function:rnn_cell_forward def rnn_cell_forward (XT, A_prev, parameters): "" "Implements a single forward Step of the Rnn-cell as described into Figure (2) arguments:xt--Your input data at Timestep "T", numpy array of Shape (

fromSklearn.metricsImportConfusion_matrix, Classification_report fromSklearn.preprocessingImportLabelbinarizer#From neuralnetwork import neuralnetwork fromSklearn.cross_validationImporttrain_test_splitdigits=load_digits () X=Digits.datay=Digits.targetx-= X.min ()#normalize the values to bring them into the range 0-1X/=X.max () nn= Neuralnetwork ([64,100,10],'Logistic') X_train, X_test, Y_train, Y_test=Train_test_split (X, y) labels_train=Labelbinarizer (). Fit_transform (y_train) labels_test=La

Python implementation of multilayer neural networks. The code is pasted first, the programming thing is not explained. Basic theory reference Next: Deep Learning Learning Notes (iii): Derivation of neural network reverse propagation algorithm Supervisedlearningmodel, Nnlayer, and softmaxregression that appear in your c

. v_state) **2) **0.5 the Self . Errors.append (RMSE) theSelf.epoch + = 1 the Print("Epoch%s:rmse =%s; | | w| |:%6.1f; Sum Update:%f"%(Self.epoch, RMSE, Numpy.sum (Numpy.abs (self). W)), Total_change)) - return Self in the defLearning_curve (self): the plt.ion () About #plt.figure () the plt.show () theE =Numpy.array (self. Errors) thePlt.plot (Pandas.rolling_mean (E, 50) [50:]) + - defActivate (self, X): the ifX.SHAPE[1]! =Self . W.sha

1. Write data to the CSV file, you should be able to directly implement the Python code to write the dataset, but I read this piece of file is not very skilled, and so I succeeded, plus, here I write the dataset directly into Excel2. Then change the suffix to. csv and use Pandas to readImport Matplotlib.pyplot as Pltfile = ' bp_test.csv ' import pandas as Pddf = pd.read_csv (file, header=none) x = df.iloc[:,].v Aluesprint (x)Read results[ -1. -0.9

ImportNumPy as NPImportMatplotlib.pyplot as PltX=np.array ([[1,3,3], [1,4,3], [1,1,1]]) Y=np.array ([1,1,-1]) W= (Np.random.random (3)-0.5) * *Print(W) LR=0.11N=0O=0defupdate ():Globalx,y,w,lr,n N+=1O=np.sign (Np.dot (x,w.t)) W_c=lr* (Y-o. T). dot (X))/Int (x.shape[0]) W=w+W_c for_inchRange (100): Update ()Print(W)Print(n) O=np.sign (Np.dot (x,w.t))if(o==y.t). All ():Print("Complete") BreakX1=[3,4]y1=[3,3]x2=[1]y2=[1]k=-w[1]/w[2]d=-w[0]/w[2]xdata=np.linspace (0,10) P

() ... dx0. 104993585404:d elta_w:[-0.0092478 -0.01849561 -0.02774341] Weight before [3,-2,1]delta_w:[-0.0092478 -0.01849561 -0.02774341] weight after [2.9907522 -2.01849561 0.97225659]dx0. 00664805667079:d elta_w:[-0.00198107 -0.00066036 0.00132071] Weight before [0,3,-1]delta_w:[-0.00198107 -0.00066036 0.00132071] weight after [-1.98106867e-03 2.99933964e+00 -9.98679288e-01]dx0. 196791859198:d elta_w:[-0.02875794 -0.01437897 -0.02875794] Weight before [-1.98106867e-03 2.99933964e+0

Building your Deep neural network:step by step Welcome to your Week 4 assignment (Part 1 of 2)! You are have previously trained a 2-layer neural network (with a single hidden layer). This week is a deep neural network with as many layers In this notebook, you'll implement t

Circular neural Network Tutorial-the first part RNN introduction Cyclic neural Network (RNN) is a very popular model, which shows great potential in many NLP tasks. Although it is popular, there are few articles detailing rnn and how to implement RNN. This tutorial is designed to address the above issues, and the tutor

ideas. In short, the neural network contains several major elements: neuronal nodes, layers (layer), weights (weights) and biases (bias). The two computational processes of neural networks are forward propagation and reverse propagation respectively. The forward propagation of each layer contains the weighted sum (convolution) respectively. The linear operation

Deep Learning Notes (i): Logistic classificationDeep learning Notes (ii): Simple neural network, back propagation algorithm and implementationDeep Learning Notes (iii): activating functions and loss functionsDeep Learning Notes: A Summary of optimization methods (Bgd,sgd,momentum,adagrad,rmsprop,adam)Deep Learning Notes (iv): The concept, structure and code annotation of cyclic

First, the main method of neural network performance tuning the technique of data augmented image preprocessing network initialization training The selection of activation function different regularization methods from the perspective of data integration of multiple depth networks 1. Data augmentation The generalization ability of the model can be improved by inc

http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://www.csdn.net/article/2015-11-25/2826323 Cyclic neural networks (recurrent neural networks,rnns) have been successful and widely used in many natural language processing (Natural Language processing, NLP). However, there are few learning materials related to Rnns online, so this series is to introduce the principle of rnns and how to achieve i

computer programs to learn and recognize handwritten numbers. Although this program is only 74 lines, and does not use any special neural network library, but it can be without any human intervention, to achieve more than 96 of the handwritten digit recognition accuracy rate. In the following chapters, we will further refine our approach to achieve an accuracy r

, 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

network, to understand DBN and SAE is necessary, I also have to take time to learn CNN, there are times to finish this article, add point description text. In addition, do not start directly with Keras these packaged libraries, but first to understand the RNN bottom of the principle and calculation formula, so that RNN can grasp more thoroughly. And these packaging libraries are not omnipotent, when the model is more complex, some functions through t