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
This is Keras tutorial introduces you to deep learning Python:learn into preprocess to your data, model, evaluate and optimize Neural networks. ▲21▲21
Deep Learning
By now, your might already know machine learning, a branch in computer science that studies the "design of Algorithms" C An learn. Today, your ' re going to focus on deep learning, a subfield of machine learning This is a set of algorithms this is inspired By the structure and function of
1. Introduction Keras is a Theano based framework for deep learning, designed to refer to torch, written in Python, and is a highly modular neural network library that supports GPU and CPU. Keras Official document Address 2. Process First, use CNN for training, use the Theano function to remove the full link of the CNN, and train the SVM 3. Results Example Because this is just a demo
Install keras (tensorflow is the background) and kerastensorflow in Ubuntu
0 System Version Ubuntu16.04
1. system update (the speed is very slow. You can skip this step to see if it will affect subsequent installation)
sudo apt updatesudo apt upgrade
2. Install python Basic Development Kit
sudo apt install -y python-dev python-pip python-nose gcc g++ git gfortran vim
3. Download Anaconda and install it on the terminal.
./Anaconda.sh
4. Modify termina
Conda create-n Keras python=3.5 IpykernelActivate KerasPython-m ipykernel Install--user--name kerasJupyter NotebookKeras installed using this method can be called by Jupyter Notebook.I found the answer at http://ipython.readthedocs.io/en/stable/install/kernel_install.html# Kernels-for-different-environmentsIpykernel have to is linked to the environment, and then jupyter can use it.The following installation procedure works:conda create -n
(LambdaX:X * * 2))#add a layer that returns the concatenation# of the positive part of the the input and#The opposite of the negative partdefantirectifier (x): x-= K.mean (x, Axis=1, keepdims=True) x= K.l2_normalize (x, Axis=1) Pos=k.relu (x) Neg= K.relu (-x)returnK.concatenate ([Pos, neg], Axis=1)defAntirectifier_output_shape (input_shape): Shape=list (input_shape)assertLen (shape) = = 2#Only valid for 2D tensorsShape[-1] *= 2returntuple (Shape) model.add (Lambda (antirectifier, Output_shape=a
Tags: Environment configuration EPO Directory decompression profile logs Ros Nvidia initializationThis article is a personal summary of the Keras deep Learning framework configuration, the shortcomings please point out, thank you! 1. First, we need to install the Ubuntu operating system (under Windows) , which uses the Ubuntu16.04 version: 2. After installing the Ubuntu16.04, the system needs to be initialized and updated:Open Terminal input:System U
Keras Installation:It is best to build in the Anaconda virtual environment:Conda create-n Environment Name python=3.6Enter the environment:Source Activate Environment nameInstall Keras:Pip Install KerasPip Install TheanoPip Install tensorflow-gpu==1.2.0If you use Theano as backend, you need to Conda install PYGPU to support parallel and gou operations.
If Modulenotfounderror:no module named ' Mkl ' appearsTo demote the MKL in the current environment
Let's spit it out. This is based on the Theano Keras how difficult to install, anyway, I am under Windows toss to not, so I installed a dual system. This just feel the powerful Linux system at the beginning, no wonder big companies are using this to do development, sister, who knows ah ....Let's start by introducing the framework: We all know the depth of the neural network, Python started with Theano this framework to write the neural network, but la
Keras in the construction of neural network model and training neural network, simple and useful, summed up a few Keras API use, continuous updating. Of course, you can also learn through the Keras website. Visualization of https://keras.io/models
Save the model map as a picture.
From keras.utils import Plot_model
Plot_model (model, to_file= ' model.png ')
Plot_
Tags: arc update. So dia switch Linu HTTPS installation tutorial DevelopThe Deep learning Framework Keras is based on TensorFlow, so installing Keras requires the installation of TensorFlow:1. The installation tutorial is mainly referenced in two blog tutorials:Https://www.cnblogs.com/HSLoveZL/archive/2017/10/27/7742606.htmlHttps://www.jianshu.com/p/5b708817f5d8?from=groupmessage2. This tutorial starts with
right: Actually, the right is a left-hand image on the time series of the expansion, the last moment output is the input of this moment. It is important to note that, in fact, all neurons on the right are the same neuron, the left, which share the same weights, but accept different inputs at each moment, and then output to the next moment as input. This is the information stored in the past.Understanding the meaning of "loops" is the purpose of this chapter, and the formulas and details are des
Installing Anaconda3
A key step:conda install pip
The following to install a variety of packages you need, generally no more error.pip install tensorflow-gpu ==1.5.0rc1pip install -U keras
If you need to install Theano, you need to install its dependency package, which isconda install mingw libpythonpip install -U theano
Install OpenCV3 (Windows environment):pip install -U opencv-contrib-python
Install TensorFlow
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