keras datasets

about the Keras 2.0 version of the Run demo error problem Because it is the neural network small white, when running the demo does not understand Keras version problem, appeared a warning: C:\ProgramData\Anaconda2\python.exe "F:/program Files (x86)/jetbrains/pycharmprojects/untitled1/cnn4.py" Using Theano backend. F:/program Files (x86)/jetbrains/pycharmprojects/untitled1/cnn4.py:27:userwarning:update your

Keras a pre-trained model with multiple networks that can be easily used.Installation and use main references official tutorial: https://keras.io/zh/applications/https://keras-cn.readthedocs.io/en/latest/other/application/An example of using RESNET50 for ImageNet classification is given on the official website. fromKeras.applications.resnet50ImportResNet50 fromKeras.preprocessingImportImage fromKeras.applic

Logs/000/trained_weights_final.h5 placement after training weightKeras-yolo3-masterKeras/tensorflow + Python + yolo3 train your own datasetCode: https://github.com/qqwweee/keras-yolo3Modify the yolov3.cfg file: 79695109Use yolo3 to train your own dataset for Target DetectionVocdevkit/voc2007/Annotations XML fileVocdevkit/voc2007/javasimages jpgimageFour files under vocdevkit/voc2007/imagesets/Main, create the file test. py under voc2007,Run voc_annota

Deep learning Keras Frame Notes Autoencoder class use notes　　This is a very common auto-coding model for building. If the parameter is Output_reconstruction=true, then Dim (input) =dim (output), otherwise dim (output) =dim (hidden).Inputshape: Depends on the definition of encoderOutputshape: Depends on the definition of decoderParameters: Encoder: Encoder, which is a layer type or layer container type. Decoder: Decoder, which is a layer t

,output_dim=300 Back to the original question: the embedded layer converts a positive integer (subscript) to a vector with a fixed size, such as [[4],[20]]->[[0.25,0.1],[0.6,-0.2]] Give me a chestnut: if the Word table size is 1000, the word vector dimension is 2, after the word frequency statistics, Tom corresponds to the id=4, and Jerry corresponding to the id=20, after the conversion, we will get a m1000x2 matrix, and Tom corresponds to the matrix of the 4th line, The data to remove the row i

Today, the GPU is used to speed up computing, that feeling is soaring, close to graduation season, we are doing experiments, the server is already overwhelmed, our house server A pile of people to use, card to the explosion, training a model of a rough calculation of the iteration 100 times will take 3, 4 days of time, not worth the candle, Just next door there is an idle GPU depth learning server, decided to get started. Deep learning I was also preliminary contact, decisive choice of the simp

Recently paid attention to a burst of keras, feeling this thing quite convenient, today tried to find it really quite convenient. Not only provide the commonly used algorithms such as layers, normalization, regularation, activation, but also include several commonly used databases such as cifar-10 and mnist, etc. The following code is Keras HelloWorld bar. Mnist handwritten digit recognition with MLP implem

The model saved with H5py has very little space to take up. Before you can use H5py to save Keras trained models, you need to install h5py, and the specific installation process will refer to my blog post about H5py installation: http://blog.csdn.net/linmingan/article/details/50736300 the code to save and read the Keras model using H5py is as follows: Import h5py from keras.models import model_from_json

According to the description of the kaggle:invasive species monitoring problem, we need to judge whether the image contains invasive species, that is, to classify the images (0: No invasive species in the image; 1: The images contain invasive species), According to the data given (2295 graphs and categories of the training set, 1531 graphs of the test set), it is clear that this kind of image classification task is very suitable to be solved by CNN, KERA Application Module application provides

Deeplearning library is quite a lot of, now GitHub on the most hot should be caffe. However, I personally think that the Caffe package is too dead, many things are packaged into a library, to learn the principle, or to see the Theano version.My personal use of the library is recommended by Friends Keras, is based on Theano, the advantage is easy to use, can be developed quickly.Network frameworkThe network framework references Caffe's CIFAR-10 framew

The Keras Python Library makes creating deep learning models fast and easy. The sequential API allows you to create models Layer-by-layer for most problems. It is limited the it does not allow the to create models that share layers or have multiple inputs or outputs. The functional API in Keras is a alternate way of creating models, offers a lot flexibility more complex models. In this tutorial, you'll disc

This article mainly introduces the question and answer section of Keras, in fact, very simple, may not be in detail behind, cooling a bit ahead, easy to look over. Keras Introduction: Keras is an extremely simplified and highly modular neural network Third-party library. Based on Python+theano development, the GPU and CPU operation are fully played. The purpose o

The content of a simple experiment lesson. First, the size of the given sample material is 32*32, which can be done in Python batch and OpenCV function resize (), where I do not list the code. List some of the pictures that are well-shrunk. Then in the use of Keras CV convolutional neural network model, before doing this experiment, the computer should be configured Python+theano+keras environment. #生成一个

You could ref:http://www.cvpapers.com/datasets.html I'll paste that contents in the followings: Participate in reproducible Detection PASCAL VOC, DataSet classification/detection competitions, segmentation competition, person Layout taster Competition Datasets LabelMe DataSet LabelMe is a web-based image annotation tool This allows researchers to label images and share T He annotations with the rest of the community. If you are using the database, we

Install keras (tensorflow is the background) and kerastensorflow in Ubuntu 0 System Version Ubuntu16.04 1. system update (the speed is very slow. You can skip this step to see if it will affect subsequent installation) sudo apt updatesudo apt upgrade 2. Install python Basic Development Kit sudo apt install -y python-dev python-pip python-nose gcc g++ git gfortran vim 3. Download Anaconda and install it on the terminal. ./Anaconda.sh 4. Modify termina

Conda create-n Keras python=3.5 IpykernelActivate KerasPython-m ipykernel Install--user--name kerasJupyter NotebookKeras installed using this method can be called by Jupyter Notebook.I found the answer at http://ipython.readthedocs.io/en/stable/install/kernel_install.html# Kernels-for-different-environmentsIpykernel have to is linked to the environment, and then jupyter can use it.The following installation procedure works:conda create -n

(LambdaX:X * * 2))#add a layer that returns the concatenation# of the positive part of the the input and#The opposite of the negative partdefantirectifier (x): x-= K.mean (x, Axis=1, keepdims=True) x= K.l2_normalize (x, Axis=1) Pos=k.relu (x) Neg= K.relu (-x)returnK.concatenate ([Pos, neg], Axis=1)defAntirectifier_output_shape (input_shape): Shape=list (input_shape)assertLen (shape) = = 2#Only valid for 2D tensorsShape[-1] *= 2returntuple (Shape) model.add (Lambda (antirectifier, Output_shape=a

Tags: Environment configuration EPO Directory decompression profile logs Ros Nvidia initializationThis article is a personal summary of the Keras deep Learning framework configuration, the shortcomings please point out, thank you! 1. First, we need to install the Ubuntu operating system (under Windows) , which uses the Ubuntu16.04 version: 2. After installing the Ubuntu16.04, the system needs to be initialized and updated:Open Terminal input:System U

Keras Installation:It is best to build in the Anaconda virtual environment:Conda create-n Environment Name python=3.6Enter the environment:Source Activate Environment nameInstall Keras:Pip Install KerasPip Install TheanoPip Install tensorflow-gpu==1.2.0If you use Theano as backend, you need to Conda install PYGPU to support parallel and gou operations. If Modulenotfounderror:no module named ' Mkl ' appearsTo demote the MKL in the current environment

Let's spit it out. This is based on the Theano Keras how difficult to install, anyway, I am under Windows toss to not, so I installed a dual system. This just feel the powerful Linux system at the beginning, no wonder big companies are using this to do development, sister, who knows ah ....Let's start by introducing the framework: We all know the depth of the neural network, Python started with Theano this framework to write the neural network, but la