TensorFlow-Tutorials | 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다 | Machine Learning library

by golbin Python Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | TensorFlow-Tutorials Summary

TensorFlow-Tutorials is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras, Neural Network applications. TensorFlow-Tutorials has no bugs, it has no vulnerabilities and it has medium support. However TensorFlow-Tutorials build file is not available. You can download it from GitHub.

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다. 다만, 이론에 대한 깊은 이해와 정확한 구현보다는, 다양한 기법과 모델에 대한 기초적인 개념과 텐서플로우의 기본적인 사용법 학습에 촛점을 두었으므로, 구현이 미흡하게 되어 있는 부분이 많음을 고려해 주세요. 또한, 아름다운 코드를 만들기 보다는 순차적인 흐름으로 이해할 수 있도록 코드와 주석과 만들었음을 참고 해 주시면 감사하겠습니다.

Support

Quality

Security

License

Reuse

Support

TensorFlow-Tutorials has a medium active ecosystem.

It has 2070 star(s) with 1052 fork(s). There are 247 watchers for this library.

It had no major release in the last 6 months.

There are 23 open issues and 25 have been closed. On average issues are closed in 19 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of TensorFlow-Tutorials is current.

Quality

TensorFlow-Tutorials has no bugs reported.

Security

TensorFlow-Tutorials has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

TensorFlow-Tutorials does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

TensorFlow-Tutorials releases are not available. You will need to build from source code and install.

TensorFlow-Tutorials has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed TensorFlow-Tutorials and discovered the below as its top functions. This is intended to give you an instant insight into TensorFlow-Tutorials implemented functionality, and help decide if they suit your requirements.

Train the game
Decode enc_input and dec_input
Run the replay
Get the replay of a message
Generate next batch
Retrieves a set of random bottlenecks
Ensure directory exists
Get the full path to the image
Get or create a bottleneck
Translate a word
Make a batch from input data
Generate a random batch
Make a batch from seq_data
A generator that generates noise tensors
A discriminator
Determine whether the image should distort
Build the graph
Creates the bottleneck graph
Calculate the evaluation step
Adds input_distortions
Download and extract the model
Test the model
Play the game
Generate the next batch of examples
Cache bottleneck images
Generate bottleneck_truths for images
Adds the final training tensors
Creates a list of images in the given image directory

Get all kandi verified functions for this library.

TensorFlow-Tutorials Key Features

No Key Features are available at this moment for TensorFlow-Tutorials.

TensorFlow-Tutorials Examples and Code Snippets

Part 0.,Intro,Basic FFNN example

Jupyter Notebook

Lines of Code : 83

License : Permissive (MIT)

Copy

import tensorflow as tf
import numpy as np

x_data = np.array(
    [[0, 0], [1, 0], [1, 1], [0, 0], [0, 0], [0, 1]])

y_data = np.array([
    [1, 0, 0], 
    [0, 1, 0],  
    [0, 0, 1],  
    [1, 0, 0],
    [1, 0, 0],
    [0, 0, 1]
])

global_step =

word2vec4everything,Dependencies

Python

Lines of Code : 14

License : No License

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# Creating the environment through a conda command
$ conda create -n tensorflow python=2.7

# Activate the environment. This causes the terminal prompt to change.
$ source activate tensorflow
(tensorflow)$ # The new prompt for the conda environment

Install

C++

Lines of Code : 9

License : Permissive (Apache-2.0)

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$ pip install tensorflow

$ pip install tensorflow-cpu

$ python

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'

Community Discussions

Trending Discussions on TensorFlow-Tutorials

Unable to clone repository while connected to VPN. SSL: certificate subject name does not match target host name 'github.com'

QUESTION

Unable to clone repository while connected to VPN. SSL: certificate subject name does not match target host name 'github.com'

Asked 2020-Apr-12 at 13:44

I am trying to clone a git repository on a remote system connected via ssh. I need to connect to the VPN in order to ssh to the local machine of my organization.

I am trying to clone this git repository but I am getting SSL error,

...

ANSWER

Answered 2020-Apr-12 at 13:44

You can disable certificate verification;

Source https://stackoverflow.com/questions/61170411

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TensorFlow-Tutorials

You can download it from GitHub.
You can use TensorFlow-Tutorials like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.