Welcome to the exciting journey of building a neural network classifier from the ground up! In this article, we’ll walk through the process of creating a multi-layer fully-connected neural network to classify seeds from a well-known dataset. So, buckle up and get ready to dive into the world of machine learning!

Overview

We will train a neural network to classify the seeds dataset found at this link. The neural network will employ an L2 loss function, utilize a sigmoid activation, and for simplicity, we won’t be using bias terms. Weight optimization will be handled through gradient descent via the delta rule.

Usage

To get started with your neural network, you’ll need to run the following command:

python3 NN_scratch.py

This will trigger the training process and you can observe the output that gives you valuable insights into the performance of the model.

Expected Output

Upon successful execution, your console should display results similar to the following:

Reading dataset seeds_dataset.csv... - X.shape = (210, 7), y.shape = (210,), n_classes = 3
Neural network model: input_dim = 7 hidden_layers = [5] output_dim = 3 eta = 0.1 n_epochs = 400 n_folds = 4 seed_crossval = 1 seed_weights = 1
Cross-validating with 4 folds...
Fold 1: acc_train = 98.10%, acc_valid = 94.23% (n_train = 158, n_valid = 52)
Fold 2: acc_train = 98.10%, acc_valid = 98.08% (n_train = 158, n_valid = 52)
Fold 3: acc_train = 98.73%, acc_valid = 96.15% (n_train = 158, n_valid = 52)
Fold 4: acc_train = 98.73%, acc_valid = 94.23% (n_train = 158, n_valid = 52)
- acc_train_avg = 98.42%, acc_valid_avg = 95.67%

This output gives a clear indication of how well the neural network is performing with different folds of cross-validation.

Understanding the Code with an Analogy

Think of your neural network as a factory producing a particular type of seed. The raw materials (input data) enter the factory through different assembly lines (neurons). As they progress through various processing stages (layers), workers (weights) refine them with a combination of different techniques (activations) until the final product (classification) emerges. Specifically:

• The **input_dim** is akin to the number of machines used for processing raw materials.
• **hidden_layers** represent the stages of refinement where different operations are performed to enhance quality.
• The **output_dim** indicates the types of final products that can be generated at the end of the factory line.
• **eta** serves as the speed at which workers are making adjustments, crucial for smooth operations.
• **n_epochs** is essentially the number of times the factory goes through the entire process to ensure the products are consistently high-quality.

Troubleshooting

If you encounter any issues while executing your neural network, here are some common troubleshooting tips:

• File Not Found Error: Ensure that the dataset seeds_dataset.csv is correctly placed in the working directory.
• Library Import Issues: Check that you have the required libraries installed: numpy and pandas. You can install them using pip install numpy pandas.
• Shape Misalignment Errors: Verify the shape of your input data and make sure they match the expected dimensions as specified in your code.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following the steps and understanding the code, you have laid down a strong foundation to build upon in your machine learning journey. Remember that iterating on your model and experimenting with different configurations will lead to more robust and accurate results. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.