cnn-from-scratch | A Convolutional Neural Network implemented from scratch | Machine Learning library

by vzhou842 Python Version: v1.0 License: MIT

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kandi X-RAY | cnn-from-scratch Summary

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

A Convolutional Neural Network implemented from scratch (using only numpy) in Python.

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cnn-from-scratch has a low active ecosystem.

It has 194 star(s) with 70 fork(s). There are 9 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 0 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cnn-from-scratch is v1.0

Quality

cnn-from-scratch has 0 bugs and 0 code smells.

Security

cnn-from-scratch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cnn-from-scratch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cnn-from-scratch 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

cnn-from-scratch releases are available to install and integrate.

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

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

cnn-from-scratch saves you 30 person hours of effort in developing the same functionality from scratch.

It has 81 lines of code, 8 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Train the given image
Backpropagate back - propagation
Backpropagate the gradient of the image
Backprop propagation
Forward the image
Compute the log transformation
Perform the forward computation
Generator that iterates over all regions of an image
Iterate over regions of the image
Forward image
Calculate the output of each region

Get all kandi verified functions for this library.

cnn-from-scratch Key Features

No Key Features are available at this moment for cnn-from-scratch.

cnn-from-scratch Examples and Code Snippets

No Code Snippets are available at this moment for cnn-from-scratch.

Community Discussions

Trending Discussions on cnn-from-scratch

CIFAR-10 python architicture

Strange Loss function behaviour when training CNN

QUESTION

CIFAR-10 python architicture

Asked 2020-Nov-06 at 12:48

I'm following this tutorial here.

...

ANSWER

Answered 2020-Nov-06 at 12:47

why is he using kernel_initializer='he_uniform'?

The weights in a layer of a neural network are initialized randomly. How though? Which distribution should they follow? he_uniform is a strategy for initializing the weights of that layer.

why did he choose the 128 for the dense layer?

This was chosen arbitrarily.

What will happen if we add more dense layer to the code like:
model.add(Dense(512, activation='relu', kernel_initializer='he_uniform'))

I assime you mean to add them where the other 128-neuron Dense layer is (there it won't break the model) The model will become deeper and have a much higher number of parameters (i.e. your model will become more complex) with whatever positives or negatives come along with this.

what would be a suitable dropout rate?

Usually you see rates in the range of [0.2, 0.5]. Higher rates reduce overfitting but might cause training to become more unstable.

Source https://stackoverflow.com/questions/64714658

QUESTION

Strange Loss function behaviour when training CNN

Asked 2018-Nov-18 at 11:12

I'm trying to train my network on MNIST using a self-made CNN (C++).

It gives enough good results when I use a simple model, like: Convolution (2 feature maps, 5x5) (Tanh) -> MaxPool (2x2) -> Flatten -> Fully-Connected (64) (Tanh) -> Fully-Connected (10) (Sigmoid).

After 4 epochs, it behaves like here 1.
After 16 epochs, it gives ~6,5% error on a test dataset.

But in the case of 4 feature maps in Conv, the MSE value isn't improving, sometimes even increasing 2,5 times 2.

The online training mode is used, with help of Adam optimizer (alpha: 0.01, beta_1: 0.9, beta_2: 0.999, epsilon: 1.0e-8). It is calculated as:

...

ANSWER

Answered 2018-Nov-18 at 11:11

Try to decrease the learning rate.

Source https://stackoverflow.com/questions/53351546

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

Vulnerabilities

No vulnerabilities reported

Install cnn-from-scratch

You can download it from GitHub.
You can use cnn-from-scratch 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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