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Deep learning is a prominent topic in the AI field. it has been around for a long time. It has received much attention because it has made breakthroughs beyond human capabilities in computer vision (ComputerVision) and AlphaGO. Since the last investigation, attention to deep learning has increased significantly. Deep learning is a prominent topic in the AI field. it has been around for a long time. It has received much attention because it has made breakthroughs beyond human capabilities in Comp
Oaching to me and hides the screen.Specifically, Keras is used to implement neural network for learning his face, a Web camera was used to recognize that he I s approaching, and switching the screen.MissionThe mission is-to-switch the screen automatically when my boss was approaching to me.The situation is as follows:It is on 6 or 7 meters from the seat to my seat. He reaches my seat in 4 or 5 seconds after he leaves his seat. Therefore, it's necessa
neural network implemented by JavaScript and its common modules, and includes a large number of browser-based instances. These documents and instances are numerous and complete. Don't let JavaScript and neural networks combine to scare you away, which is a very popular and useful project. 4. Keras Keras is also a library of Python deep learning programs, but it leverages TensorFlow and Theano, which means
methods of the Ndarray class, which need to be invoked using an instance of the Ndarray class. >>> a= Np.random.random ((2,3)) >>> a array ([[0.65806048, 0.58216761, 0.59986935], [ 0.6004008, 0.41965453, 0.71487337]] >>> a.sum () 3.5750261436902333 >>> a.min () 0.41965453489104032 >>> A.max () These operations treat an array as a one-dimensional linear list. However, you can perform the appropriate operation on the specified axis by specifying the axis paramet
, in_data): Return 1/(1 + np.exp (in_data)) def forward (self, in_data): Return self._sigmoid (Np.dot (SELF.W, in_data) + self. b There's not much to see in the code, and notice that we randomly initialize the W in the parameter, and sometimes we let God randomly give us a neural network, and we can also look at random the great. For the convenience of visualization, this is done with data input of 2 and output of 1. Okay, let's see number 1th first: x = Np.linspace ( -10,1
One, variable batch renaming: For example, to change the number of variables a_2 b_2 c_2 d_2 e_2 suffix to W ren (*_2) (*W) Second, check the duplicate data commonly used commands: Duplicates report X//Reports x variable has no duplicates Duplicates list x//list duplicate records Bys X:gen Cn=_n Browse if Cn>1 Drop CN//Browse specific duplicate values for next step analysis and processing Duplicates drop x//delete duplicate value, keep first record of duplicate value Third, the data transverse l
Iris Flower Classification is the representative of classical logistic regression, but its code contains a large number of Python library core processing patterns, this article is to dissect the Python code article.1 #The width and length of the flowers were taken by using the two feture labeled 2,3.2 #The first dimension takes ":" To represent all rows, the second dimension represents the column range, and this parameter pattern actually looks like reshape
= [63 75 78 50 56 65 70 71 80];Sum ((Dim1-mean (DIM1)). * (Dim2-mean (DIM2))/(9-1)% 0Sum ((Dim1-mean (DIM1)). * (Dim3-mean (DIM3))/(9-1)% 0.625Sum ((Dim2-mean (DIM2)). * (Dim3-mean (DIM3))/(9-1)% 5STD (DIM1) ^2% 0.75STD (dim2) ^2% 0.75STD (dim3) ^2% 100.7778P274X = [2 2; 2 3; 3 4; 4 3; 5 4; 5 5];[Coeff,score,latent,tsquare] = Princomp (X);P275-1X0=x-repmat (Mean (X), 6, 1);Score_1 = X0*coeff;P275-2X = [2 2; 2 3; 3 4; 4 3; 5 4; 5 5];V = CoV (X);[Coeff,latent] = Pcacov (V)P277I = Imread (' baboon
How Matlab draws the envelopes of complex curvesHttp://jingyan.baidu.com/article/aa6a2c14d36c710d4c19c4a8.htmlIf a curve, such as a sound waveform, fluctuates greatly and is complex, it can be smoother and clearer by drawing envelopes. This experience helps newcomers to new MATLAB to complete this process.Tools/Materials Matlab Software Sample Data Method/Step Before and after the treatment of the effect of the comparison, illustrated by a sound wave Fourier transform (FFT)
function [H, array] = display_network(A, Opt_normalize, Opt_graycolor, cols, Opt_ Colmajor) % This function visualizes filters in matrix A. Each column of a is a% filter. We'll reshape each column into a square image and visualizes% on each cell of the visualization panel.% All other parameters is optional, usually you does not need to worry% about it.% Opt_normalize:whether We need to normalize , the filter so, all of% them can have similar contrast.
ndarray, you can perform dimension transformation and element type conversion.Dimension transformation of the ndarray Method Description . Reshape (shape) Returns a shape array without changing the array element. The original array remains unchanged. . Resize (shape) The function is consistent with. reshape (), but the original array is modified. . Swapaxes (ax1
size of hidden layers, and train a new network. We can observe the influence of parameters on the learning results. The algorithm code is as follows: #! Usr/bin/env python3 #-*-coding: UTF-8-*-import numpy as npimport math # definition of sigmoid funtion # numpy. exp work for arrays. def sigmoid (x): return 1/(1 + np. exp (-x) # definition of sigmoid derivative funtion # input must be sigmoid function's resultdef sigmoid_output_to_derivative (result): return result * (1-result) # init training
", then you can omit to write.When Python uses slice syntax, it produces slice objects. Extended slice syntax allows for different index tile operations to include step slices, multidimensional slices, and omitted slices. The syntax for a multidimensional slice is sequence[start1:end1,start2:end2], or use the ellipsis, Sequence[...,start1:end1]. The slice object can also be slice () by the built-in function.Selection of two-dimensional arrays:First we said that the syntax for multidimensional ar
Logistic Regression with a neural Network mindset V4Simply using the logistic to realize the cat's recognition, the logistic can be regarded as a simple neural network structure, the following is the main code:1.Import NumPy as Npimport Matplotlib.pyplot as Pltimport h5pyimport scipyfrom PIL import imagefrom scipy import Ndimagefrom Lr_utils Import Load_dataset%matplotlib Inline2.# # # START CODE here # # # (≈3 lines of code) M_train = Train_set_x_orig.shape[0]m_test = TEST_SET_X_ORIG.SHAPE[0]NU
1. TopicsIn Matlab, there is a very useful function reshape that can reshape a matrix to another new matrix of different sizes, but retains its original data.gives a matrix represented by a two-dimensional array, and two positive integers r c , respectively, representing the number of rows and columns of the matrix you want to refactor.The reconstructed matrix requires that all elements of the original matr
({' Group_val ': [3.5, 7]}, index=[' A ', ' B ']) LEFT1 # in[29]: right1 # In[31]: Pd.merge (Left1,right1,lef t_on= ' key ', right_index=true) # In[32]: Pd.merge (LEFT1, right1, left_on= ' key ', right_index=true, how= ' outer ') # in[34 ]: #2 import NumPy asNP lefth = Dataframe ({' Key1 ': [' Ohio ', ' Ohio ', ' Ohio ', ' Nevada ', ' Nevada '], ' Key2 ': [2000, 2001, 2002, 2001, 2002], ' Data ': Np.arange (5.)}) righth = Dataframe (Np.arange) reshape
, visiblesize); [Cost, Grad] = Orthonormalicacost (Weightmatrix, Visiblesize, NUMFEAtures, patches, epsilon); Numgrad = Computenumericalgradient (@ (x) orthonormalicacost (x, Visiblesize, numfeatures, Patches, epsilon), Weightmatrix (:)); % uncomment to display the numeric and analytic gradients side-by-side% disp ([Numgrad grad]); diff = Norm (numgrad-grad)/norm (Numgrad+grad); fprintf (' orthonormal ICA difference:%g\n ', diff); Assert (diff % function [Cost, Grad] = Orthonormalicacost (t
. We should use a multi-parameter and not less-than-fit network model. The tradeoff between too much capacity and too little capacity.Unfortunately, there is no effective rule or method to determine the size of the model parameters. You must constantly try to find the optimal parameter size on the validation set. a general approach to determining the size of a model: start with a relatively simple model, gradually increase or decrease the number of neurons or the number of network layers until t
Summary In order to follow the in-depth study of the introductory, usually see the relevant sites and videos, here back up. A comparison of 5 depth learning frameworks Share a comparison video about the most popular 5 depth learning frameworks (Scikit Learn,tensorflow,theano,keras, and Caffe): http://weibo.com/p/ 23044464933dbb5463a1b0cef9ebcb4207b869. Iterate through each of the pros and cons, as well as some sample code, to make a definitive concl
); %subplot (2, 5, a), Imshow (I0); End I0 = ((double (back)/255). ^ backdepth); %figure, Imshow (I0); I0 = uint8 (I0 * 255); Imwrite (I0, ' new_texture.jpg ');% begins calculation of P. ^β= J in β-matrix theta = 0.2; P = im2double (Imread (' new_texture.jpg ')); J = im2double (Imread (' tone.jpg '));% initialized to vector convenient calculation p = imresize (P, [H, W]); p = Reshape (P, h*w, 1); Logp = log (P); Logp = SPDia
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