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
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
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
. 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
This article is void
My next installment is the TensorFlow and Keras truth.
Environment:
Anaconda4.2;python3.5;windows10,64,cuda
Previous hard cuda9.1 useless, we want to use the GPU must choose cuda8.0, I thought the official will be corresponding update, naive. First TensorFlow don't recognize, moreover cudnn own all do not recognize, only 8.0.
Keras and TensorFlow are both Pip,pytorch and OpenCV are go
InstallationBefore installing Keras, install one of its backend engines:tensorflow, Theano, or CNTK. We recommend the TensorFlow backend.
TensorFlow installation instructions. (installed)
Theano installation instructions.
CNTK installation instructions.
Also consider installing the following optional dependencies:
CuDNN (Recommended if you plan on the running Keras on GPU). (i
This article mainly wants to introduce how to use the Scikit-learn grid search function, and gives a set of code examples. You can copy and paste the code into your own project as the start of the project.
List of topics covered below: How to use Keras in the Scikit-learn model. How to use Grid search in the Scikit-learn model. How to tune batch size and training epochs. How to tune the optimization algorithm. How to tune the learning rate and momentu
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 is a Theano and TensorFlow-compatible neural network Premium package that uses him to component a neural network more quickly, and several statements are done. and a wide range of compatibility allows Keras to run unhindered on Windows and MacOS or Linux.Today to compare learning to use Keras to build the following common neural network:
Regression
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,
Reprint Please specify:Look at Daniel's small freshness : http://www.cnblogs.com/luruiyuan/This article original website : http://www.cnblogs.com/luruiyuan/p/6660142.htmlThe Ubuntu version I used was 16.04, and using Gnome as the desktop (which doesn't matter) has gone through a lot of twists and turns and finally completed the installation of Keras with TensorFlow as the back end.Installation of the TENSORFLOW-GPU version:1. Download CUDA 8.0Address:
Keras If you are using the Theano back end, you should automatically do not use the GPU only CPU, start the GPU using Theano internal command.For the TensorFlow back end Keras and TensorFlow will automatically use the visible GPU, and I need it to run only on the CPU. Three methods were found on the web, and the last one was useful to me, but the following records were also made for three: using TensorFlow
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.