Draft
Basic Neural Network Implementation
Description:
Basic Neural Network Implementation
Your challenge is to develop a SimpleNeuralNetwork
class capable of learning through training epochs. This task will test your understanding of neural networks and machine learning fundamentals.
Task 1: Simple Neural Network
Implement the SimpleNeuralNetwork
class with the following methods:
__init__(self, input_size, hidden_size, output_size):
Initializes the network with random weights and biases.train(self, inputs, labels, epochs, learning_rate):
Trains the network over a specified number of epochs and learning rate.calculate_loss(self, inputs, labels):
Computes the loss between the predictions and the actual labels.predict(self, inputs):
Predicts the output for given inputs.forward_pass(self, inputs):
Performs a forward pass through the network.
The network should demonstrate learning by reducing loss across epochs and adjusting its weights and biases accordingly.
Test Cases
Your code will be evaluated against a suite of test cases. The neural network must exhibit a reduction in loss after training and show changes in its internal parameters. It should also handle various input scenarios effectively.
Requirements
- Use the provided class structure for the neural network.
- Ensure that the network functions as expected and passes all the test cases.
Networks
Neural Networks
NumPy
Arrays
Machine Learning
Artificial Intelligence
Similar Kata:
Stats:
Created | Nov 9, 2023 |
Warriors Trained | 9 |
Total Skips | 0 |
Total Code Submissions | 67 |
Total Times Completed | 3 |
Python Completions | 3 |
Total Stars | 1 |
% of votes with a positive feedback rating | 100% of 2 |
Total "Very Satisfied" Votes | 2 |
Total "Somewhat Satisfied" Votes | 0 |
Total "Not Satisfied" Votes | 0 |
Total Rank Assessments | 1 |
Average Assessed Rank | 6 kyu |
Highest Assessed Rank | 6 kyu |
Lowest Assessed Rank | 6 kyu |