pytorch keras

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

Python provides two libraries for fast numerical computations, Theano and TensorFlow, which are very powerful libraries, but it's hard to use them directly to create deep learning models, so Keras came into being, Keras provides a fast and efficient way to create deep learning models based on Theano or TensorFlow.About the installation of Keras, you can see my ot

find MinGW.4, restart the computerV. Installation of TheanoIt is easiest to install directly using the command line:1. Open cmd2, input pip install Theano, after the return is pleasing to download the progress bar, this is very small, so the installation is relatively fast.3, in cmd, input python into the Python environment, and then enter import Theano carriage return, need to wait for some time.Vi. installation of KerasKeras This library on the basis of Theano continue to encapsulate, modular

Migration learning, with off-the-shelf network, run their own data: to retain the network in addition to the output layer of the weight of other layers, change the existing network output layer output class number. Train your network based on existing network weights,Take Keras 2.1.5/vgg16net as an example. Import the necessary libraries From keras.preprocessing.image import Imagedatagenerator to keras impo

This script is a training Keras mnist digital Recognition program, previously sent, today to achieve the forecast, # larger CNN for the mnist Dataset # 2.Negative dimension size caused by subtracting 5 from 1 for ' conv2d_4/convolution ' ( OP: ' conv2d ') with input shapes # 3.userwarning:update your ' conv2d ' call to the Keras 2 Api:http://blog.csdn.net/johini eli/article/details/69222956 # 4.Error check

The laboratory installed new Keras, found Keras default back end is TensorFlow, want to change back to Theano, see the official document also didn't understand, finally buttoned up, very simple.Description of Chinese document: Keras Chinese document, switch back end In fact, in C:\Users\75538 (75538 is my windos user name, to find your corresponding user name on

' This script goes along the blog post "Building powerful image classification models using very little data" from BLOG.K Eras.io. It uses data that can is downloaded at:https://www.kaggle.com/c/dogs-vs-cats/data in our setup, we:-Created a data/folder-created Train/and validation/subfolders inside data/created-Cats/and dogs/subfolders inside train/a nd validation/-Put the "Cat pictures index 0-999 in data/train/cats-put" Cat pictures index 1000-1400 in Data/valida Tion/cats-put The Dogs Picture

Reprint: http://blog.csdn.net/mmc2015/article/details/50976776 Install first and say: sudo pipinstall Keras or manually installed: Download: Git clone git://github.com/fchollet/keras.git Upload it to the appropriate machine. Install: CD to the Keras folder and run the Install command: sudo python setup.py install Keras in Theano, before learning

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