keras reshape

described below. The first is the index:#-*-encoding:utf-8-*-import NumPy as Npimport pandas as Pdimport Matplotlib.pyplot as Pltfrom pandas import Series,dataf Rame#series has a reindex function that can rearrange the index so that the order of elements changes obj = Series ([1,2,3,4],index=[' A ', ' B ', ' C ', ' d ']) #注意这里的reindex并不改变obj的值, Get a "copy" #fill_value is obviously filled with the value of the empty index #print obj.reindex ([' A ', ' C ', ' d ', ' B ', ' e '],fill_value = 0) #

excluded from the ' # ' sort, the default is quick sort a = Np.array ([[[2,3,1,5],[2,1,0,3]]) Np.sort (A , axis=0) # vertical sort ' [ [2 1 0 3] [2 3 1 5]] ' np.sort (A,axis=1) # horizontal sort ' [[1 + 2 3 5] [0 1 2 3]] "' # Limit the size of the element Np.clip (arr1,3,6) ' [[3 3 3 3] [4 5 6 6]] '" indexes and slices of an arrayIndexing and slicing of one-dimensional arrays # Create a test array arr = Np.arange (9) # [0 1 2 3 4 5 6 7 8] arr # get arr[2] # 2 # Slice oper

Recently in doing a project, need to use the Keras, on the internet received a bit, summed up here, for small partners Reference!1. Installation EnvironmentWin7+anconda (I have two versions of 2 and 3)2. A great God said to open cmd directly, enter PIP install Keras, and then automatically installed. I tried for a moment without success. (hint that PIP version is not enough).3. Later found is to install The

its API is difficult to use. (Project address: Https://github.com/shogun-toolbox/shogun)2, KerasKeras is a high-level neural network API that provides a Python deep learning library. For any beginner, this is the best choice for machine learning because it provides a simpler way to express neural networks than other libraries. The Keras is written in pure Python and is based on the TensorFlow, Theano, and cntk back end.According to the official websi

= tf. nn. max_pool (l1a, ksize = [1, 2, 2, 1], # l1 shape = (?, 14, 14, 32)Strides = [1, 2, 2, 1], padding = 'same ')L1 = tf. nn. dropout (l1, p_keep_conv)# The second convolutional layer and pooled layer, dropout part of neuronsL2a = tf. nn. relu (tf. nn. conv2d (l1, w2, # l2a shape = (?, 14, 14, 64)Strides = [1, 1, 1, 1], padding = 'same '))L2 = tf. nn. max_pool (l2a, ksize = [1, 2, 2, 1], # l2 shape = (?, 7, 7, 64)Strides = [1, 2, 2, 1], padding = 'same ')L2 = tf. nn. dropout (l2, p_keep_con

with a radius of 1 and the center is (0, 0, 0). Then, we set glortho (-1.5, 1.5,-1.5, 1.5,-10, 10); indicates that the entire sphere is installed in a frame with a width and height of 3. If you set glortho (0.0, 1.5,-1.5, 1.5,-10, 10); indicates to use a frame with a width of 1.5 and a height of 3 to bring in the right of the entire sphere. If glortho (0.0, 1.5, 0.0, 1.5,-10, 10); indicates that the upper right corner of the sphere is installed in a frame with a width and height of 1.5. The pre

storage space in the BLOB has been requested * @pa RAM Top * The allocated but unshaped output blobs of the to is shaped by reshape * @param the top-level data, blob objects to construct but the storage empty No application, * specific space size to be based on bottom blob size and LAYER_PARAM_ common decision, specific in the reshape function reality * * Checks that number of bottom and top B LOBs is c

###### #编程环境: Anaconda3 (64-bit)->spyder (python3.5)fromKeras.modelsImportSequential #引入keras库 fromKeras.layers.coreImportDense, Activationmodel= Sequential ()#Building a modelModel.add (Dense (12,input_dim=2))#Input Layer 2 node, hide layer 12 nodes (The number of nodes can be set by itself)Model.add (Activation ('Relu'))#Use the Relu function as an activation function to provide significant accuracy Model.add (Dense (1,input_dim=12))#dense hidden la

glortho. Suppose there is a sphere with a radius of 1 and the center is (0, 0, 0). Then, we set glortho (-1.5, 1.5,-1.5, 1.5,-10, 10); indicates that the entire sphere is installed in a frame with a width and height of 3. If glortho (0.0, 1.5,-1.5, 1.5,-10, 10) is set, a frame with a width of 1.5 and a height of 3 is used to bring in the right of the entire sphere; if glortho (0.0, 1.5, 0.0, 1.5,-10, 10) is set, the upper right corner of the sphere is installed in a frame with a width and heigh

terms of height and width. For example: if we use the GLUT library to create a form: Glutinitwindowsize (500, 500); Then use Glutreshapefunc (reshape); The reshape code is as follows:void reshape (int width, int height){Glviewport (0, 0, (Glsizei) width, (glsizei) height);Glmatrixmodel (gl_projection);Glloadidentity ();Glortho (-1.5, 1.5,-1.5, 1.5,-10, 10);....}

screen in terms of height and width.For example: if we use the GLUT library to create a form: Glutinitwindowsize (500, 500); Then use Glutreshapefunc (reshape); The reshape code is as follows:void reshape (int width, int height){Glviewport (0, 0, (Glsizei) width, (glsizei) height);Glmatrixmodel (gl_projection);Glloadidentity ();Glortho (-1.5, 1.5,-1.5, 1.5,-10,

scene are displayed to the screen in terms of height and width.For example: if we use the GLUT library to create a form: Glutinitwindowsize (500, 500), then use Glutreshapefunc (reshape); The reshape code is as follows:void reshape (int width, int height){Glviewport (0, 0, (Glsizei) width, (glsizei) height);Glmatrixmodel (gl_projection);Glloadidentity ();Glortho

)Nchannels: number of channels in the matrix (number of channels in the matrix: 1, 2, 4)J: column number (which column do you want to access)C: Channel Number (the value of the channel you want to access) The above method can also be used to access the matrix of a single channel, but the nchannels value is 1 and C = 0. Reshape of the matrix The Reshape of a matrix is a conversion between the matrix channel

1. First install Python, I install the pythoh2.7 version, installation steps1) Enter in the terminal in turn TAR–JXVF python-2.7.12.tar.bz2 CD Python-2.7.12 ./configure Make Make install 2) Testing Terminal input Python jump into editor2. Install the Python Basic Development Kit # 系统升级 sudo apt update sudo apt upgradesudo apt install-y python-dev python-pip python-nose gcc g++ git gfortran vim3. Install Operation Acceleration Library sudo apt install-y libopenblas-Dev

network outage causes model weights such as Keras load Vgg16 to fail,The direct workaround is to delete the downloaded file and download it again.windows-weights Path : C:\Users\ your user name \.keras\models linux-weights Path : . keras/models/Note: Files with dots in Linux are hidden and need to be viewed hidden file to display

Setting up a deep learning machine from Scratch (software)A detailed guide-to-setting up your machine for deep learning. Includes instructions to the install drivers, tools and various deep learning frameworks. This is tested on a a-bit machine with Nvidia Titan X, running Ubuntu 14.04There is several great guides with a similar goal. Some is limited in scope, while others is not up to date. This are based on (with some portions copied verbatim from): Caffe Installation for Ubuntu R

from the last signal. Implement the LSTM model in Python There are a number of packages in Python that can be called directly to build lstm models, such as Pybrain, Kears, TensorFlow, cikit-neuralnetwork, etc. (more stamp here ). Here we choose keras. PS: If the operating system with Linux or Mac, strong push TensorFlow ... ） Because the training of LSTM neural network model can be optimized by adjusting many parameters, such as activation functio

calculate gradients and update weight coefficients; Remember to perform optimizer output. Use a predefined common loss function: Initializes using Xavier, and Tf.layer automatically sets the weighting factor (weight) and the offset (bias). C. Senior Wrapper--keras Keras can be understood as a layer at the top of the TensorFlow, which can make some work simpler (and also support Theano backend). Define

(stats04 (k, 1: COL), stats03 (k, 1: COL )); Comp04 = [comp04; t]; EndConnection struct or CELL ARRAY The operations for connecting to a struct array or cell are similar to those for operating a common matrix. For example, generate a 3x8 struct matrix S, each of which has the following fields: X, Y, and Z. Connect the second structure array S2. Generate a 3-by-8 structure matrix s: For k = 1:24 S (K) = struct ('x', 10 * k, 'y', 10 * k + 1, 'z', 10 * k + 2 ); End S =

freedom are adjustable (the default is N) A.STD (), A.var () 3.4991427521608776, 12.244 Min, max Minimum value and maximum value Argmin, Argmax Indexes with minimum and maximum elements, respectively A.argmin (), A.argmax () 0, 4 Diff Diff (A, n=1, axis=-1), the difference between the last and the previous one, the parameter n represents the N-round operation, the multidimensional array, which can be controlled by axis