alexnet in keras

http://blog.csdn.net/jerr__y/article/details/53695567 Introduction: This article mainly describes how to configure the GPU version of the TensorFlow environment in Ubuntu system. Mainly include:-Cuda Installation-CUDNN Installation-TensorFlow Installation-Keras InstallationAmong them, Cuda installs this part is the most important, Cuda installs after, whether is tensorflow or other deep learning framework can be easy to configure.My environment: Ubunt

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

Keras-anomaly-detection Anomaly Detection implemented in Keras The source codes of the recurrent, convolutional and feedforward networks auto-encoders for anomaly detection can be found in keras_anomaly_detection/library/convolutional. py and keras_anomaly_detection/library/recurrent. py and keras_anomaly_detection/library/feedforward. PY The anomaly detection is implemented using auto-Encoder with convolut

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

Preface body RNN from Scratch RNN using Theano RNN using Keras PostScript "From simplicity to complexity, and then to Jane." "Foreword Skip the nonsense and look directly at the text After a period of study, I have a preliminary understanding of the basic principles of RNN and implementation methods, here are listed in three different RNN implementation methods for reference. RNN principle in the Internet can find a lot, I do not say here, say it wil

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

Win10 + python3.6 + VSCode + tensorflow-gpu + keras + cuda8 + cuDN6N environment configuration, win10cudn6n Preface: Before getting started, I knew almost nothing about python or tensorflow, so I took a lot of detours When configuring this environment, it took a whole week to complete the environment... However, the most annoying thing is that it is difficult to set up the environment. Because my laptop is low in configuration, the program provided by

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

. 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

"Install Anaconda3"Download: https://www.continuum.io/downloads, prompts during installation failed to create Anacoda menue refer to Http://www.cnblogs.com/chuckle/p/7429624.html when the error occurs. "Install TensorFlow"(Requires network link, offline installation reference: HTTP://WWW.JIANSHU.COM/P/C245D46D43F0)Open Anaconda Prompt, enter:Pip Install TensorFlow"Install Keras"(need network link, reference: http://www.jianshu.com/p/c245d46d43f0)Open

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

I see that Keras is good, based on Python, the background is based on Theano or TensorFlow. Installation Environment: ubuntu14.04First, install the Python environment, Theano, and Keras sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ the git sudo pip libopenblas-dev All Theano sudo pip install KerasData and Code Preparation According to the blog, download Mnist.zip data

Keras mixed with TensorFlow Keras and TensorFlow using tensorfow Fly Keras Recently, TensorFlow has updated its new version to 1.4. Many updates have been made, and it is of course important to add Tf.keras. After all, Keras for the convenience of the model building everyone is obvious to all. Likes the

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