In order to learn Keras, first have to install good keras, but under Windows, Keras installation really will have a lot of problems. These two days go a lot of detours, finally installed Keras, is based on Theano, now record the installation process, perhaps to their own help.
1. Install Python
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Symbolic Calculation
The underlying library of Keras uses Theano or TensorFlow, both of which are also known as Keras's back end, whether Theano or TensorFlow, a symbolic library.As for symbolism, it can be generalized as follows: the calculation of symbolism begins with the definition of various variables and then establishes a "calculation chart", which specifies the computational relationship between the variables. The building of a good calculati
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Using Keras to explore the filter for convolutional networks
Visualize All Filters
Deep Dream (Nightmare)
Fool the Neural network
The revolution has not been successful, comrades still need to work hard
Source informationThis address: http://blog.keras.io/how-convolutional-neural-networks-see-the-world.htmlThis article Francois CholletThe translation of this article was first published by
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
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
Part I: InstallationSince my computer was already configured with Caffe, all the related packages for Python have been installed. Therefore, even without Anaconda installation is still very simple.sudo pip install TensorFlowsudo pip install KerasTest:Pythonfrom keras.models import SequentialThe second part: How to use Keras to read pictures from the local, and do a two classification of the neural network, directly posted code:#Coding=utf-8##ImportOs#
Tag:tensor Construction pipflowinstall aptsciras environment construction
Install Theano (Environment parameter: Ubuntu 16.04.2 Python 2.7)
Installing NumPy and SciPy
1.sudo apt-get Install Python-numpy python-scipy
2.sudo pip Install Theano
If PIP is not installed, install PIP first
Installing Pyyaml
sudo pip install Pyyaml
It is recommended to install HDF5 and H5PY,CUDNN according to your own situation
sudo apt-get insta
.
Validation_split: Verifies the proportion of data used. Validation_data: (X, y) tuples used as validation data.
will replace the validation data divided by Validation_split. Shuffle: Type Boolean or str (' batch '). Do you want to shuffle the sample for each iteration (see Bowen Theano Learning Notes 01--dimshuffle () function).
' Batch ' is a special option for handling data in HDF5 (Keras data format for storing weights).
Show_accuracy: Whether th
TensorFlow and Theano and Keras are deep learning frameworks, TensorFlow and Theano are more flexible and difficult to learn, they are actually a differentiator.
Keras is actually TensorFlow and Keras interface (Keras as the front end, TensorFlow or Theano as the back end), it is also very flexible, and relatively eas
about the Keras 2.0 version of the Run demo error problem
Because it is the neural network small white, when running the demo does not understand Keras version problem, appeared a warning:
C:\ProgramData\Anaconda2\python.exe "F:/program Files (x86)/jetbrains/pycharmprojects/untitled1/cnn4.py"
Using Theano backend.
F:/program Files (x86)/jetbrains/pycharmprojects/untitled1/cnn4.py:27:userwarning:update your
. 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
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
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
The content of a simple experiment lesson.
First, the size of the given sample material is 32*32, which can be done in Python batch and OpenCV function resize (), where I do not list the code.
List some of the pictures that are well-shrunk.
Then in the use of Keras CV convolutional neural network model, before doing this experiment, the computer should be configured Python+theano+keras environment.
#生成一个
I. Background and purposeBackground: Configure the Theano, get the GPU, to learn the Dnn method.Objective: This study Keras basic usage, learn how to write MLP with Keras, learn keras the basic points of text.Second, prepareToolkit: Theano, NumPy, Keras and other toolkitsData set: If you can't get down, you can use the
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