cse-154-neural-network | In this assignment , we will be
kandi X-RAY | cse-154-neural-network Summary
kandi X-RAY | cse-154-neural-network Summary
cse-154-neural-network is a Python library. cse-154-neural-network has no bugs, it has no vulnerabilities and it has low support. However cse-154-neural-network build file is not available. You can download it from GitHub.
In this assignment, we will be implementing a configurable, Multi-Layer Perceptron neural network using NumPy. The starter code neuralnet_starter.py contains the abstractions of various componenets of a neural network, including layers and activation functions. You will be implementing the forward and backward propagation passes and combining the components to create a complete neural network. You will also be implementing the training and evaluation procedures to classify the MNIST dataset. Complete the code in the neuralnet_starter.py, which we will be autograding. Feel free to create multiple copies of this starter code to run different experiments, however. We are providing a checker.py so that you may check the correctness of the functions you implement, though we strongly encourage that you also write your own test cases. You may run python checker.py at any point to generate an evaluation report about the correctness of your implementation.
In this assignment, we will be implementing a configurable, Multi-Layer Perceptron neural network using NumPy. The starter code neuralnet_starter.py contains the abstractions of various componenets of a neural network, including layers and activation functions. You will be implementing the forward and backward propagation passes and combining the components to create a complete neural network. You will also be implementing the training and evaluation procedures to classify the MNIST dataset. Complete the code in the neuralnet_starter.py, which we will be autograding. Feel free to create multiple copies of this starter code to run different experiments, however. We are providing a checker.py so that you may check the correctness of the functions you implement, though we strongly encourage that you also write your own test cases. You may run python checker.py at any point to generate an evaluation report about the correctness of your implementation.
Support
Quality
Security
License
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Support
cse-154-neural-network has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
cse-154-neural-network has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of cse-154-neural-network is current.
Quality
cse-154-neural-network has 0 bugs and 0 code smells.
Security
cse-154-neural-network has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
cse-154-neural-network code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
cse-154-neural-network 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.
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cse-154-neural-network releases are not available. You will need to build from source code and install.
cse-154-neural-network has no build file. You will be need to create the build yourself to build the component from source.
It has 160 lines of code, 22 functions and 2 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed cse-154-neural-network and discovered the below as its top functions. This is intended to give you an instant insight into cse-154-neural-network implemented functionality, and help decide if they suit your requirements.
- Run the test .
- Initialize the model .
- Forward forward pass .
- Forward backward through the forward pass .
- Load images and labels .
- ReLU function .
- Apply tanh .
- Train the model .
- Softmax function .
- Evaluate the model .
Get all kandi verified functions for this library.
cse-154-neural-network Key Features
No Key Features are available at this moment for cse-154-neural-network.
cse-154-neural-network Examples and Code Snippets
No Code Snippets are available at this moment for cse-154-neural-network.
Community Discussions
No Community Discussions are available at this moment for cse-154-neural-network.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install cse-154-neural-network
You can download it from GitHub.
You can use cse-154-neural-network 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.
You can use cse-154-neural-network 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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