lstm keras

When you install Keras,import Keras with Pip after the normal installation completes Python 2.7, you will be prompted not toTensorFlow initially does not support Windows environments and is now compatible with Windows, but requires Python 3. The installation steps are as follows:Install the Anaconda link first: https://www.anaconda.com/download/download the Windows 2.7 version and install it directly after

Installation Full Name reference https://keras-cn.readthedocs.io/en/latest/for_beginners/keras_linux/cuda8.0.cudnn5.0,ubuntu16.04 configured in the environmentInstalled version of TENSORFLOW-GPUTest after the installation is complete, import TensorFlowIssue: ImportError:libcublas.so. 9. 0:cannot Open Shared object file:no such file or directory Cause: The TensorFlow version does not correspond to the CUDNN and Cuda versions, ref: 79415787So

The title describes the operating environment Win7 2016-07-24Look at the online a lot of keras identification minist but generally because of the version of the problem, can not be directly used,, here also special thanks to the three-headed SCP. The tutorial is very good to the whole. There is the best you install Anaconda before the original installed py uninstall, or install MinGW when the problem,, installation is not detailed introduction of the

Label:System configuration: Ubuntu 14 (other systems are also similar to the following operation) 1. Install Python via Anaconda Address: Https://www.continuum.io/downloads#linux 2. Installing Theano [Email protected]:~/downloads$ pip Install Theano 3. Installing Keras [Email protected]:~/downloads$ pip Install Keras 4. Installing Spearmint [Email protected]:~/tools$ pip install-e ~/tools/spearmint/ [Ema

Learning Data Augmentation Based on keras, augmentationkeras In deep learning, when the data size is not large enough, the following 4 methods are often used: 1. Manually increase the size of the training set. A batch of "new" Data is created from existing Data by means of translation, flip, and Noise addition. That is, Data Augmentation.2. regularization. A small amount of data may lead to over-fitting of the model, making the training error small a

Keras error ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'tensor ("embedding_1/random_uniform: 0", shape = (5001,128), dtype = float32 )', Train and save the model on the server. After the model is copied to the local machine, the load_model () error is returned: ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'tensor ("embedding_1/random_uniform: 0", shape = (5001,128), dtyp

under the successful installation Anaconda, First, install MinGW: Open prompt-- Input:Conda config--add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/--in input: Conda config--set show_cha Nnel_urls yes-- last input: Conda install MinGW Libpython (so the purpose of the installation is to download more quickly) Second, Open Prompt , you will see a path inside the window, depending on your path, locate the corresponding directory, and create a new text document in the dir

The curve fitting is realized, that is, the regression problem. The model was created with single input output, and two hidden layers were 100 and 50 neurons. In the official document of Keras, the examples given are mostly about classification. As a result, some problems were encountered in testing regression. In conclusion, attention should be paid to the following aspects: 1 training data should be matrix type, where the input and output is 1000*1,

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

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