tensorflow-mnist | MNIST For ML Beginners Deep MNIST for Experts | Machine Learning library

by sugyan Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | tensorflow-mnist Summary

tensorflow-mnist is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-mnist has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

tensorflow-mnist

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tensorflow-mnist has a medium active ecosystem.

It has 869 star(s) with 390 fork(s). There are 38 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 9 have been closed. On average issues are closed in 17 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-mnist is current.

Quality

tensorflow-mnist has 0 bugs and 0 code smells.

Security

tensorflow-mnist has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-mnist code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-mnist is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

tensorflow-mnist saves you 79 person hours of effort in developing the same functionality from scratch.

It has 205 lines of code, 10 functions and 8 files.

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

Top functions reviewed by kandi - BETA

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

Convolutional layer
Convenience wrapper around MNIST
Construct a tensorflow regression

Get all kandi verified functions for this library.

tensorflow-mnist Key Features

No Key Features are available at this moment for tensorflow-mnist.

tensorflow-mnist Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow-mnist.

Community Discussions

Trending Discussions on tensorflow-mnist

How to pass input data to an existing tensorflow 2.x model in Java?

Tensorflow.keras: RNN to classify Mnist

QUESTION

How to pass input data to an existing tensorflow 2.x model in Java?

Asked 2020-Oct-13 at 17:44

I'm doing my first steps with tensorflow. After having created a simple model for MNIST data in Python, I now want to import this model into Java and use it for classification. However, I don't manage to pass the input data to the model.

Here is the Python code for model creation:

...

ANSWER

Answered 2020-Oct-03 at 18:58

I finally managed to find a solution. To get all the tensor names in the graph, I used the following code:

Source https://stackoverflow.com/questions/64184509

QUESTION

Tensorflow.keras: RNN to classify Mnist

Asked 2020-Aug-04 at 07:26

I am trying to understand the tensorflow.keras.layers.SimpleRNN by building a simple digits classifier. The digits of Mnist dataset are of size 28X28. So the main idea is to present each line of the image in a time t. I have seem this idea in some blogs, for instance, this one, where it presents this image:

So my RNN is like this:

...

ANSWER

Answered 2020-Aug-04 at 07:26

Units is the number of neurons, which is the dimensionality of the output for that layer. This information can be found at the documentation.
The number of parameters are dependent on the layer input and the number of units. For the SimpleRNN layer this is 128 * 128 + 128 * 28 + 128 = 20096 (see this answer). For the dense layer this is 128 * 10 + 10 = 1290. These 10 and 128 that are added are because of the bias weights in the layer, which is turned on by default.
input_shape = (28, 28) means that your network will handle inputs of size 28x28 data points. Since the first dimension is the batch dimension, it will handle 28 vectors of length 28 (as depicted in your image). Inputs of a variable length are usually split up to fit in the given input_shape. If an input is smaller than the input_shape, padding can be applied to make it fit.

Source https://stackoverflow.com/questions/63055498

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-mnist

You can download it from GitHub.
You can use tensorflow-mnist 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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