Multiclass-Classification-using-Softmax 💻

Overview

This project utilizes a neural network for multiclass classification, specifically recognizing hand-written digits (0-9). The model is implemented using TensorFlow and includes the ReLU activation function and the Softmax function for improved accuracy.

Features

• Packages: Numpy, Matplotlib, TensorFlow.
• Activation Functions: ReLU, Softmax.
• Model Architecture: Three-layer neural network with ReLU activation in hidden layers and linear activation in the output layer.

Dataset

• Size: 5000 training examples.
• Input: 20x20 grayscale images unrolled into a 400-dimensional vector.
• Labels: 5000x1 vector indicating the digit (0-9) for each image.

Model

• Architecture: Input layer (400 units), Hidden layers (25, 15 units with ReLU activation), Output layer (10 units with linear activation).
• Training: Softmax grouped with loss function, SparseCategoricalCrossentropy loss, Adam optimizer, 40 epochs.

Usage

1. Import required packages.
2. Load dataset using load_data().
3. Build the model using Keras Sequential model.
4. Compile the model with specified loss function and optimizer.
5. Train the model using model.fit(X, y, epochs=40).
6. Make predictions using model.predict(image).
7. Evaluate accuracy and visualize results.

Conclusion

This project demonstrates the successful implementation of a neural network for digit recognition. The model achieves accurate predictions and provides a useful template for similar multiclass classification tasks.