first, the basic environment$PIP Install flask gevent Requests Pillowwhere flask no need to explainThe gevent is used to automatically switch processes;Pillow is used for image processing under python;The requests is used for Python under request processing. Second, the Core code interpretation# Import the necessary packages fromKeras.applicationsImportResNet50 fromKeras.preprocessing.imageImportImg_to_array fromKeras.applicationsImportImagenet_utils fromPILImportImageImportNumPy asNpImportFlask
Keras.js
Suggest a demo on the Webhttps://transcranial.github.io/keras-js/#/
The load is slow, but it's very fast to recognize.
Run Keras models (trained using TensorFlow backend) in your browser, with GPU support. Models are created directly from the Keras json-format configuration file, using weights serialized directly from the Corr esponding HDF5 file. Als
. Then this version should be a driver that matches CUDA8 with each other. )
Install cudnn5.1 (HTTPS://DEVELOPER.NVIDIA.COM/CUDNN) unzip the installation package just down, copy the files under these three folders to the Cuda folder below.
After the Anaconda installation is complete, you should be able to see whether the version is 3.5 by tapping Python directly in the Windows Command window.
Create a TensorFlow virtual environment c:> Conda create-n TensorFlow python=3.5, everything in th
The Keras framework is concise and elegant, and its design is a model. Tensorflow is bloated and complicated, and it is confusing. Of course, the peripheral components of Keras, such as callbacks, datasets, and preprocessing, have a lot of over-designed feelings, but the core of Keras is good, the perfect core of this design makes the system highly scalable and t
integrated Numpy, making it one of the most commonly used libraries in the General deep learning field from the very beginning. Today, Theano still works well, but because it does not support multi-GPU and horizontal scaling, in the TensorFlow craze (they target the same field), Theano is already forgotten.
Learning Materials Link: http://outlace.com/Beginner-Tutorial-Theano/
about Keras Keras is a very hi
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
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
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
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
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
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 data elements that are linearly distributed on the logical structure are also adjacent to each other in the actual physical storage structure, which is called the sequential storage structure of the linear table.
In other words, the logically linear relationship of data is stored in a whole block of contiguous memory space in the order of the pre-and post-order, and there is no gap between them, so the storage structure is called
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
,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
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