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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

Stats:

CreatedNov 9, 2023
Warriors Trained9
Total Skips0
Total Code Submissions67
Total Times Completed3
Python Completions3
Total Stars1
% of votes with a positive feedback rating100% of 2
Total "Very Satisfied" Votes2
Total "Somewhat Satisfied" Votes0
Total "Not Satisfied" Votes0
Total Rank Assessments1
Average Assessed Rank
6 kyu
Highest Assessed Rank
6 kyu
Lowest Assessed Rank
6 kyu
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