keras reshape

(foo), im = double (IM),% (256*256)/(4*4)% (256*256) divided into (4*4) image block n = 4;m = 4;block_n = N*ones (1,256/n) ; % Block_n = [4,4....4] 64 4block_m = M*ones (1,256/m); im_block = Mat2cell (im,block_n,block_m);%im_block = Reshape (im_bloc k,1,4096); X = ones (16,4096); for i =1:4096 tmp = Cell2mat (Im_block (i)); %x (:, i) = reshape (tmp,16,1); For a = 1:4 for b = 1:4 X (:, i) = TMP

1 ImportNumPy as NP2 #Base Properties3Array = Np.array ([[[+], [0,0,1]], [[+], [0,0,1]]],4Dtype =Np.int64)5 6 Print(Array)7 Print(Array.ndim)#Number of Dim8 Print(Array.shape)#Shape9 Print(array.size)#Number of elementsTen Print(Array.dtype) One A #Create an array -A = Np.array ([1,2,3,4])#1 Dim - theb = Np.array ([[1,2,3,4]])#row vector, 2 Dim -c = Np.array ([[1], [2], [3], [4]])#column vector, 2 Dim - Print(A.shape, B.shape, C.shape) - +A = Np.zeros ((2,3), Dtype =np.float) -A = Np.ones ((2

This article explains some of the other common layers, including: Softmax-loss layer, Inner product layer, accuracy layer, reshape layer and dropout layer, and their parameter configuration.1, Softmax-lossThe Softmax-loss layer and the Softmax layer are calculated roughly the same. Softmax is a classifier that calculates the probability of a class (likelihood) and is a generalization of the logistic regression.The Logistic regression can only be used

Gl. h and Glu. H, you only need to include this file */# Include # Include /* Initialize the material attributes, light source attributes, and illumination model, and open the depth buffer */Void Init (void){Glfloat mat _ ecular [] = {1.0, 1.0, 1.0, 1.0 };Glfloat mat_shinine [] ={ 50.0 };Glfloat light_position [] = {1.0, 1.0, 1.0, 0.0 }; Glclearcolor (0.0, 0.0, 0.0, 0.0 );Glshademodel (gl_smooth ); Glmaterialfv (gl_front, GL _ ecular, mat _ ecular );Glmaterialfv (gl_front, gl_shinine, mat_shin

) followed by an array of the same size containing phase data (in radians) for each pixel. the data is stored row-wise. The mstar files available from the CIS Web site have had the ASCII header removed. the following snippet of Matlab code will read a single image provided the file has been opened resulting in a file pointer, FP: Mstar_size = n_rows * n_cols; [Tmp_data, num] = Fread (FP, mstar_size * 2, 'float '); % Reshape works by column, but mstar

") plt.title ("Ch-2 Wavedata ") Plt.grid (' on ') #标尺, on: There, off: none. Plt.subplot (5,1,5) Plt.plot (time,wavedata[:,2]) Plt.xlabel ("Time (s)") Plt.ylabel ("AmpliTude ") plt.title (" Ch-3 wavedata ") Plt.grid (' on ') #标尺, on: There, off: none. Plt.show ()：Single channel is a special case of multichannel, so the multi-channel read mode for any channel WAV files are applicable. It is important to note that Wavedata is different from the previous data structure after

Reproduced in: http://blog.csdn.net/miangangzhen/article/details/51281989#!usr/bin/env Python3#-*-coding:utf-8-*-ImportNumPy as NPImportMath#definition of sigmoid funtion#numpy.exp work for arrays. defsigmoid (x):return1/(1 + np.exp (-x))#definition of sigmoid derivative funtion#input must be sigmoid function ' s resultdefsigmoid_output_to_derivative (Result):returnresult* (1-result)#Init Training SetdefGettrainingset (nameofset): Setdict= { "Sin": Getsinset (),}returnSetdict[nameofset]

, Figsize=( the, the)) forIinch Range(n): Figs[i].imshow (Images[i].asnumpy ()) Figs[i].axes.get_xaxis (). Set_visible (False) Figs[i].axes.get_yaxis (). Set_visible (False) plt.show () data, label=cifar_100_train[1:9]Print(data.shape, label) show_images (data)Downloading E:/Data/MXNet/cifar100\cifar-100-binary.tar.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/cifar100/cifar-100-binary.tar.gz...C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:252: Depr

): Endpoint Indicates whether 10 is the generated elementNp.concatenate (): Concatenation of multiple arrays Dimension transformations of arrays . Reshape (SHAPE): Does not change the current array, generated by shape. Resize (Shape): Changes the current array, generated by shape. swapaxes (Ax1, AX2): Swap two dimensions. Flatten (): Descending dimension of an array, returning a collapsed one Type transformation of an array

(a) > >> bool (42.0) True >>> float (true) You can specify the type of the parameter in many parameters of a function, of course, this type parameter is optional. As follows: >>> Arange (7, dtype=uint16) Output array When outputting an array, the NumPy is displayed in a particular layout in a similar way to a nested list: The first line is output from left to right Each row is output from top to bottom Each slice is separated from the next by a blank

/32/64,complex64/128 print(np_lst.shape) # 行列数 print(np_lst.ndim) # 维数 print(np_lst.dtype) # 数据类型 print(np_lst.itemsize) # 每个数据的数据存储大小 print(np_lst.size) # 元素个数Some kinds of array#encoding=utf-8import numpy as npdef main(): print(np.zeros([2, 4])) print(np.ones([3, 5])) print("Rand:") print(np.random.rand()) # 0-1内均匀分布随机数 print(np.random.rand(2, 4)) print("RandInt:") print(np.random.randint(1, 10, 3))

programThe example comes from the Wunda machine learning programming problem. The sample is the same as the digital recognition of multiple classifications in logistic regression.1, calculate the loss function, and gradientfunction [J Grad] = nncostfunction (Nn_params, ... input_layer_size, ... Hidden_layer_size, ... num_labels, ... X, Y, lambda) Theta1 = reshape (Nn_params (1:hidden_layer_size * (input_layer_size +

First, the index The order in which the values are taken is from the perimeter to the innermost element position, which is written sequentially. 1.1. Single Value IndexImport NumPy as NPA = Np.arange (+). Reshape (2,2,4) print ("original array: \ n", a) print ("single value index: \ n", a[1][1][2]) >>> original array: [[[0] 1 2 3] [4 5 6 7] [[8 9] [12 13 14 15]]] Single value index value: 141.2. Fancy Index You can index m

A complex number, represented by two 64-bit floating-point numbers 64 Object Object O Python Object Type String String_ S Fixed-length string type (1 bytes per character) Unicode_ U string of fixed-length Unicode type Third, array modification (attributes) Shape modification:. Reshape (),. T Dimen

typeNp.full (Shape, Val): All Val-generatedNp.eye (n): Generating the Unit matrix Np.ones_like (a): Generates an array of all 1 by the shape of array aNp.zeros_like (a): similarlyNp.full_like (A, Val): similarly Np.linspace (1,10,4): Generate arrays based on spacing between start and start dataNp.linspace (1,10,4, endpoint = False): Endpoint Indicates whether 10 is a generated elementNp.concatenate (): Dimension transformation of an array . Reshape (

About the transpose of the array, NumPy provides the transpose function and. T property two implementations, General transpose is more convenient to use, in addition to the conversion of the two axes can also be used swapreaxes , the following examples to do the introduction. #一维数组转置 >>> arr = np.arange (6) >>> print arr [0, 1, 2, 3, 4, 5] >>> PR int Np.transpose (arr) [0, 1, 2, 3, 4, 5] #一维还是一维 ... #二维数组转置 >>> arr = np.arange (6). Reshape ((2

Common LINALG functions function Description Diag Returns the diagonal (or non-diagonal) elements of a matrix in the form of a one-dimensional array, or converts a one-dimensional array to a matrix (non-diagonal element 0) Dot Standard matrix multiplication Trace Calculates the and of the diagonal elements Det Determinant of a computed matrix Eigvals Calculate the eigenvalues of a matrix

as this:1 (3,4,5)); // Initializes an array of 3*4*5 with 1 to 60 digits B = Randn (345// Initializes an array of 3*4*5 with a random number Other initialization functions are linspace (), logspace (), ones (), zeros (), eyes (), and so on. These functions can also be used with reshape (), such as:c = Linspace (02). Reshape (345);In all of these initializations, tuples are an important component.Three, ra

Dtype The data type object of the array element Ndim Dimensions of an array Shape The shape of an array Data A python buffer object that holds the array data Flat Returns a one-dimensional iterator of an array Imag Returns the imaginary part of an array Real Returns the real part of an array Nbytes The byte length of all elements in the array Instance: >>> A

boundary expansion of two-dimensional transpose convolutionIt is important to note that the padding,stride is still the value specified by the convolution process and will not change. Example Because the above is only a theoretical explanation of the purpose of transpose the convolution, and does not explain how to rebuild the input by the output after the convolution. Here's an example of how to feel. For example, with input data: After 3x3,reshape