anaconda keras

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

RNN model of deep learning--keras training RNN principle: (Recurrent neural Networks) cyclic neural network. It interacts with each neuron in the hidden layer and is able to handle the problems associated with the input and back. In RNN, the output from the previous moment is passed along with the input of the next moment, which is equivalent to a stream of data over time. Unlike Feedforward neural networks, RNN can receive serialized data as input,

) Reducelronplateau when the indicator becomes Reduce learning rate Reducelronplateau (monitor= ' Val_loss ', factor=0.1, patience=10, mode= ' auto ', epsilon=0.0001, CoolD Own=0, min_lr=0) modelcheckpoint Example: From keras.callbacks import modelcheckpoint model = sequential () model.add (Dense, input_dim=784, kernel_ initializer= ' uniform ')) Model.add (Activation (' Softmax ')) model.compile (loss= ' categorical_crossentropy ') , optimizer= ' R

index is to assign an integer ID to each word in turn. Traversing all the news texts, we keep only the 20,000 words we see most, and each news text retains a maximum of 1000 words. Generates a word vector matrix. Column I is a word vector that represents the word index for I. Load the word vector matrix into the Keras embedding layer, set the weight of the layer can not be trained (that is, in the course of network training, the word vector will no l

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

Installation of the Python version of Xgboost (Anaconda) Xgboost is a popular machine learning algorithm in recent years, proposed by the University of Washington Chen Tianchi, in many competitions at home and abroad to obtain a very good position, to specifically understand the model, you can go to GitHub, This article describes the installation method of the Git-based Python version under the Widows system. Three software is required: Pyth

1. Anaconda Download Anaconda official Download Link: https://www.continuum.io/downloads Download 32-bit or 64-bit according to your system selection. 2. Go to the download directory If not modified, the default download directory is/home/download/, ctrl+alt+t open the terminal, enter Cd/home, and then press Two tab, the terminal will automatically fill in the user name and the file directory under t

Article Description: This article is intended to be configured under Windows to be compatible with python2.7 and python3.6 anaconda environments. If there is something wrong with the article, please point out the positive discussion. 1. Download Anaconda (python3.6) and install (Anaconda download) 2. Click Start-anaconda3-

Ml_metrics is the Python implementation of metrics implementations a library of various supervised machine Learni NG evaluation metrics.First, open the Anaconda Prompt,Follow these steps:1, Search Ml_metrics PackageAnaconda search-t Conda ml_metrics Using anaconda-server API site Https://api.anaconda.orgRun ' Anaconda show 2, display the information of Ml_metrics

Recently, we have been using Python for Chinese natural language processing. The IDE is PyCharm. PyCharm is indeed the first choice for Python development, but it is still lacking in scientific computing. For this reason, I have tried EnthoughtCanopy, But Canopy feels complicated and it is not convenient to manage Python extensions. Until today I found Anaconda. Anaconda is a scientific computing environmen

Python Data Analysis Prerequisites:1.Anaconda operationFirst, you should set the local data directory as the working directory, so that you can load the local data set into memoryImport Osos.chdir ("d:/bigdata/workspace/testdata/"# Sets the current path to the working path OS.GETCWD () # Gets the current working path2. Installing GraphvizExcerpt from official website:What is Graphviz?Graphviz is open source graph visualization software. Graph Visual

Https://www.zhihu.com/people/alexwhu/answersIf you use Anaconda, you can refer to the following steps:1, open Anaconda Navigator, select the left side of the Environment menu environments, in the middle will list the current configuration of the various environment names, such as root, TensorFlow, etc. amp;amp;amp;amp;amp;lt;img src= "Https://pic1.zhimg.com/v2-795b6c54d393a952019b112d870d6e40_b.png"

The tutorials on the web are varied and similar. I had some problems with my installation, so I summed up a blog post.Sublime text is a lightweight, cross-platform, textual editor that expands its capabilities through the package.There are a lot of packages that build the Python environment, where I install the Anaconda package.1. Sublime Text Download: HTTP://WWW.SUBLIMETEXT.COM/3Fool-mounted, all the way to the point.1.1 Remove the title bar of the