gluon-tutorials | This is staging repo for https : //github

by mli Python Version: Current License: No License

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kandi X-RAY | gluon-tutorials Summary

gluon-tutorials is a Python library. gluon-tutorials has no bugs, it has no vulnerabilities and it has low support. However gluon-tutorials build file is not available. You can download it from GitHub.

This is staging repo for The reason for creating another repo is. We made the following changes. Prebuild doc is at

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gluon-tutorials has a low active ecosystem.

It has 31 star(s) with 12 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

gluon-tutorials has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gluon-tutorials is current.

Quality

gluon-tutorials has 0 bugs and 0 code smells.

Security

gluon-tutorials has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gluon-tutorials code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gluon-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

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

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

gluon-tutorials saves you 54 person hours of effort in developing the same functionality from scratch.

It has 141 lines of code, 7 functions and 5 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Load MNIST dataset .
Iterate over the dataset .
Try to load a GPU .
Initialize dataset .
Setup the extension .
Returns the number of samples .

Get all kandi verified functions for this library.

gluon-tutorials Key Features

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gluon-tutorials Examples and Code Snippets

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Community Discussions

Trending Discussions on gluon-tutorials

Is there any side effect by using ‘Sequential()’ or ‘HybridSequential()’ as a container only?

QUESTION

Is there any side effect by using ‘Sequential()’ or ‘HybridSequential()’ as a container only?

Asked 2018-May-22 at 23:21

I am reading a tutorial about MxNet. The writers use ‘mxnet.gluon.nn.Sequential()’ as a container to store some blocks (see code 1); then, they rewrite the connection of blocks in ‘def forward(self, x)’ (see codes 2 and 3). Is there any side effect by doing this? By the way, what is the difference between ‘Sequential()’ and ‘HybridSequential()’. I try a list to replace the ‘Sequential’, and I get following warnings doing the initialization process.

“ToySSD.downsamplers” is a container with Blocks. Note that Blocks inside the list, tuple or dict will not be registered automatically. Make sure to register them using register_child() or switching to nn.Sequential/nn.HybridSequential instead.’

As far as I know, if you put some blocks in ‘mxnet.gluon.nn.Sequential()’ or ‘mxnet.gluon.nn.HybridSequential()’, this action is telling the computer that these blocks are connected. However, if you design the relationship of blocks in the ‘forward’ function, you are telling the computer to connect these blocks in another way. Will it lead to confusion? If I only design some block connections in ‘forward’, what are the relationships of the other blocks in ‘Sequential()’ that are not designed in ‘forward’ function?

The entire tutorial can be found in here.

code 1：

...

ANSWER

Answered 2018-May-22 at 23:21

In Gluon, networks are build using Blocks. If something is not a Block, it cannot be part of a Gluon network. Dense layer is a Block, Convolution is a Block, Pooling layer is a Block, etc.

Sometimes you might want a Block that is not a pre-defined block in Gluon but is a sequence of predefined Gluon blocks. For example,

Conv2D -> MaxPool2D -> Conv2D -> MaxPool2D -> Flatten -> Dense -> Dense

Gluon doesn't have a pre-defined block that does the above sequence of operation. But Gluon does have Blocks that does each of the individual operation. So, you can create your own block that does the above sequence of operation by stringing together predefined Gluon blocks. Example:

Source https://stackoverflow.com/questions/50458412

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

Vulnerabilities

No vulnerabilities reported

Install gluon-tutorials

You can download it from GitHub.
You can use gluon-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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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