reshape drops

Http://blog.csdn.net/pipisorry/article/details/39088003 Numpy is a matrix computing package in Python, which is similar to MATLAB matrix computing. For more information, see http://www.numpy.org /. The numpy package and its dependent package are included when pythonxy is installed. (1) Definition Matrix >>> From numpy import * >>> A = array ([[1, 2.2, 3], [, 6]) >>> A. Ndim 2 >>> A. Shape (2, 3) >>> A. Size 6 >>> Type () >>> A. dtype Dtype ('float64 ') (2) matrix size conversion (

. Python image library is used for reading and writing images: 1234567891011121314151617181920 #!/usr/bin/python from scipy.cluster.vq import kmeans, vqfrom numpy import array, reshape, zerosfrom mltk import image vqclst = [2, 10, 100, 256] data = image.read('example.jpg')(height, width, channel) = data.shape data = reshape(data, (height*width, channel))for k in vqclst: print 'Generating vq-%

)...{Glclear (gl_color_buffer_bit | gl_depth_buffer_bit );Glusolidsphere (1.0, 40, 50 );Glflush ();}/** // * Defines the reshape function of GLUT. W and H indicate the width and height of the current window */Void reshape (int w, int H)...{Glviewport (0, 0, (glsizei) W, (glsizei) H );Glmatrixmode (gl_projection );Glloadidentity ();If (W Glortho (-1.5, 1.5,-1.5 * (glfloat) h/(glfloat) W, 1.5 * (glfloat) h/(g

(Click here for the original article link) Author: Johannes Kopf (Microsoft Research Institute) and Dani lischinski (Hebrew University) Summary We propose a novel algorithm to export a smooth, resolution-Independent Vector image from a pixel image, which can be scaled up without distortion. Our algorithm completely retains the scaling feature information of the original input pixel image, and generates a shadow with smooth variation through segmented smooth contour curves, and clearly splits the

# Include # Include # Include Void myinit (void );Void callback display (void );Void callback reshape (glsizei W, glsizei H ); Void myinit (void){Auxinitdisplaymode (aux_double | aux_rgba );Auxinitposition (0, 0, 500,500 );Auxinitwindow ("sample1 ");Glclearcolor (0.0, 0.0, 0.0, 0.0 );Glclear (gl_color_buffer_bit );// Glshademodel (gl_flat ); // First define a material. The definition method is similar to the light source definition mentioned above.G

representing both real and imaginary parts The NumPy type is converted as follows: >>> float64 (42.0) >>> int8 (42.0)---------- True >>> bool ( 42.0) True >>> Float (True) 1.0 The parameters of many functions can be specified in the parameter type, of course, this type parameter is optional. As follows: >>> Arange (7, dtype=uint16) Array ([0, 1, 2, 3, 4, 5, 6], dtype=uint16) Output array When an array is output, the NumPy is displayed in a specific layout in a similar nested

libraries, specific instructions look at the back, python2.7#coding =utf-8import numpy as Npimport granddescentimport costfunctionimport loaddataimport predictimport Write2fileimport rmseimport osimport randomimport matplotlib.pyplot as pltfrom pylab import plot,showfrom scipy import STA TsGenerates a dataset with a horizontal axis of x and an ordinate of YDef handle_data_self (): x_train = Np.array ([0,1,2,3,4,5,6,7,8,9]). Reshape (1) y_train

) [1] # calculation error rate [1] 0.06 we find that the error rate is 6%################################# #MATLAB ####################################% Main functionClear all;Close all;C = 10;Kertype = ' linear ';% Training Samplesn = 50;RANDN (' state ', 6);x1 = RANDN (2,n); %2 row n column Matrixy1 = ones (1,n); %1*n a 1x2 = 5+randn (2,n); %2*n Matrixy2 =-ones (1,n); %1*n A-1FigurePlot (x1 (1,:), X1 (2,:), ' bx ', x2 (1,:), X2 (2,:), ' K. ');Axis ([-3 8-3 8]);Hold on;X = [X1,X2]; % Training s

*32 image is convolution with 6 5*5 size patches (i.e. weights, training, random initialization, adjusting during training), and 6 feature graphs are obtained.(2) S2-C3C3 that 16 10*10 size of the feature map is how to come?The S2 feature map with 1 input layer 150 (=5*5*6, not 5*5) nodes, the output layer is 16 nodes of the network for convolution.How is the value of the 3rd feature map (assumed to be H3) obtained ?First we put the network 150-16 (in this case, the surface Input layer node is 1

-byte string, I represents a 32-bit integer, and f represents a 32-bit floating point. If the last parameter is True, memory alignment is required. The field uses the NumPy character encoding to represent the data type. For more detailed data types, see the table below. Data type Character encoding Integer I Unsigned integer U Single-precision floating point number F Double-precision floating point number D Boolean value B

of the H3 feature chart (assumed to be How did you get it? The process is as follows:First we put the network 150-16 (in this case, the surface Input layer node is 150, the hidden layer node is 16) in the input of 150 nodes into 6 parts, each part is a continuous 25 nodes. Take the 3rd part of the last node (25), and at the same time with the hidden layer 16 nodes of the 4th (because the corresponding is 3rd, counting from 0) connected that 25 values, resha