Traffic_sign_classifier | traffic sign classifier using LeNet for Self | Machine Learning library

by AmeyaWagh Python Version: Current License: No License

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

kandi X-RAY | Traffic_sign_classifier Summary

Traffic_sign_classifier is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. Traffic_sign_classifier has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

A traffic sign classifier using LeNet for Self driving cars

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Support

Traffic_sign_classifier has a low active ecosystem.

It has 4 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Traffic_sign_classifier has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Traffic_sign_classifier is current.

Quality

Traffic_sign_classifier has no bugs reported.

Security

Traffic_sign_classifier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Traffic_sign_classifier 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

Traffic_sign_classifier 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.

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Traffic_sign_classifier Key Features

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Traffic_sign_classifier Examples and Code Snippets

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Community Discussions

Trending Discussions on Traffic_sign_classifier

TensorFlow - How to Get My Loss Value from tf.Estimaor

Tensorflow Conv2d function - Type error

QUESTION

TensorFlow - How to Get My Loss Value from tf.Estimaor

Asked 2019-Mar-12 at 18:10

I am trying to train an alexnet model using tf.estimator in TensorFlow. The training process works smoothly, and I can see the logs well displayed.

...

ANSWER

Answered 2017-Dec-12 at 02:50

You can download the loss values from tensorboard when finishing your training.

Source https://stackoverflow.com/questions/47757772

QUESTION

Tensorflow Conv2d function - Type error

Asked 2018-Mar-15 at 09:37

I'm working on a neural net problem, and in the conv2d function, i'm getting a Type mismatch issue.

Here's the code snippet conv_layer1 = tf.nn.conv2d(inputs, w_layer1, strides=strides, padding='VALID') + b_layer1

I'm getting this error

TypeError: Expected binary or unicode string, got >

I tried having [1,1,1,1] inline aswell as a variable, but no luck

The complete error trace is here(search for "In [46]:" https://github.com/mymachinelearnings/CarND-Traffic-Sign-Classifier-Project/blob/attempt1/Traffic_Sign_Classifier.ipynb

...

ANSWER

Answered 2018-Mar-15 at 09:37

Looks like a typo. In your notebook, you're feeding input into your network which is a built-in python method to get input from e.g. a keyboard. Obviously this doesn't make much sense as input to a convolutional network. Chances are you meant to type inputs as in your question?

Note that the syntax highlighting in the notebook shows this quite clearly -- input is displayed in green (at least in my browser) signifying that it has a special meaning.

Source https://stackoverflow.com/questions/49293263

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

Vulnerabilities

No vulnerabilities reported

Install Traffic_sign_classifier

You can download it from GitHub.
You can use Traffic_sign_classifier 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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