neural_network_in_python

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Neural Networks: in C Script

Documentation for Neural Network Project

Overview

This documentation provides a comprehensive guide for using two Python scripts: neural_network.py for training a neural network model, and run_inference.py for running inference with a trained model.

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Download Commands

git clone https://github.com/mdriyadkhan585/neural_network_python

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Installing Dependencies

1. requirements.txt

This file lists all the Python packages your project depends on. You can create it with the following content:

numpy

After cloning the repository, navigate into the project directory and install the dependencies listed in requirements.txt:

cd neural_network_python
pip install -r requirements.txt

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neural_network.py - Model Trainer

Description

neural_network.py is responsible for defining, training, and saving a neural network model. This script includes:

• Model architecture and activation functions
• Weight initialization
• Forward pass and backpropagation algorithms
• Training loop with loss calculation and early stopping
• Model saving

Key Functions

1. initialize_weights(size)

   • Purpose: Initializes weights for the neural network layers.
   • Input: size (int) - Number of weights to initialize.
   • Output: np.ndarray - Array of initialized weights.

2. relu(x)

   • Purpose: Applies the ReLU activation function.
   • Input: x (float) - Input value.
   • Output: float - Activated value.

3. elu(x)

   • Purpose: Applies the ELU activation function.
   • Input: x (float) - Input value.
   • Output: float - Activated value.

4. sigmoid(x)

   • Purpose: Applies the sigmoid activation function.
   • Input: x (float) - Input value.
   • Output: float - Activated value.

5. swish(x)

   • Purpose: Applies the Swish activation function.
   • Input: x (float) - Input value.
   • Output: float - Activated value.

6. softmax(output)

   • Purpose: Applies the softmax function to the output layer.
   • Input: output (np.ndarray) - Array of logits.
   • Output: np.ndarray - Softmax probabilities.

forward_pass(input, weights_hidden1, weights_hidden2, weights_hidden3, weights_output, biases_hidden1, biases_hidden2, biases_hidden3, biases_output)

• Purpose: Performs the forward pass of the network.
• Inputs:
  • input (np.ndarray) - Input data.
  • weights_hidden1, weights_hidden2, weights_hidden3, weights_output (np.ndarray) - Weights for each layer.
  • biases_hidden1, biases_hidden2, biases_hidden3, biases_output (np.ndarray) - Biases for each layer.
• Outputs:
  • hidden1_output, hidden2_output, hidden3_output, final_output (np.ndarray) - Output of each layer.

backpropagate(input, hidden1_output, hidden2_output, hidden3_output, final_output, actual_output, weights_hidden1, weights_hidden2, weights_hidden3, weights_output, biases_hidden1, biases_hidden2, biases_hidden3, biases_output, learning_rate)

• Purpose: Updates weights and biases through backpropagation.
• Inputs:
  • input, hidden1_output, hidden2_output, hidden3_output, final_output (np.ndarray) - Layer outputs.
  • actual_output (np.ndarray) - True output values.
  • weights_hidden1, weights_hidden2, weights_hidden3, weights_output (np.ndarray) - Weights to be updated.
  • biases_hidden1, biases_hidden2, biases_hidden3, biases_output (np.ndarray) - Biases to be updated.
  • learning_rate (float) - Learning rate for updates.
• Output: None.

    train(inputs, targets, num_samples)

• Purpose: Trains the neural network model.
• Inputs:
  • inputs (np.ndarray) - Input data.
  • targets (np.ndarray) - Target output data.
  • num_samples (int) - Number of training samples.
• Output: None (Trains and saves the model).

How to Use

1. Prepare Data: Organize your input data and target labels.
2. Configure Parameters: Adjust the hyperparameters and model architecture if needed.
3. Run Training:

   python neural_network.py

   This will train the model on the provided data and save it to trained_model.pkl.

Example

Here's an example of the data preparation and training:

import numpy as np

# Sample data
inputs = np.random.rand(10, INPUT_SIZE)  # Replace with actual data
targets = np.eye(OUTPUT_SIZE)[np.random.randint(0, OUTPUT_SIZE, 10)]  # Example one-hot encoded targets

# Train the model
train(inputs, targets, len(inputs))

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run_inference.py - Run Inference

Description

run_inference.py is used to load a pre-trained model and perform inference on new data. This script includes:

• Model loading from a file
• Forward pass for inference

Key Functions

load_model(weights_hidden1, weights_hidden2, weights_hidden3, weights_output, biases_hidden1, biases_hidden2, biases_hidden3, biases_output)

• Purpose: Loads model weights and biases from a file.
• Inputs:
  • weights_hidden1, weights_hidden2, weights_hidden3, weights_output (np.ndarray) - Arrays to store weights.
  • biases_hidden1, biases_hidden2, biases_hidden3, biases_output (np.ndarray) - Arrays to store biases.
• Output: None.

forward_pass(input, weights_hidden1, weights_hidden2, weights_hidden3, weights_output, biases_hidden1, biases_hidden2, biases_hidden3, biases_output, hidden1_output, hidden2_output, hidden3_output, final_output)

• Purpose: Performs inference by running a forward pass with the provided input.
• Inputs:
  • input (np.ndarray) - Input data for inference.
  • weights_hidden1, weights_hidden2, weights_hidden3, weights_output (np.ndarray) - Loaded weights.
  • biases_hidden1, biases_hidden2, biases_hidden3, biases_output (np.ndarray) - Loaded biases.
• Outputs:
  • hidden1_output, hidden2_output, hidden3_output, final_output (np.ndarray) - Output of each layer.

How to Use

1. Load the Model: Ensure that trained_model.pkl is available in the same directory.

2. Run Inference:

   python run_inference.py

Example

Here’s how to use the run_inference.py script to perform inference:

import numpy as np

# Load the trained model and perform inference
test_input = np.random.rand(INPUT_SIZE)  # Replace with actual test data
forward_pass(test_input, weights_hidden1, weights_hidden2, weights_hidden3, weights_output,
             biases_hidden1, biases_hidden2, biases_hidden3, biases_output,
             hidden1_output, hidden2_output, hidden3_output, final_output)

print("Inference result:")
for i in range(OUTPUT_SIZE):
    print(f"Class {i}: {final_output[i]:.4f}")

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Summary

• neural_network.py: For training the model and saving it.
• run_inference.py: For loading the trained model and running inference.

Ensure that all dependencies (e.g., NumPy) are installed and the model file (trained_model.pkl) is available when running inference.

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