bobs24/Category_Prediction

Convolutional Neural Network (CNN) Model

This repository contains the configuration and weights for a Convolutional Neural Network (CNN) model trained on image data. The model architecture is defined using the Keras Sequential API.

Model Architecture

The model is defined as a Sequential model with the following layers:

1. Input Layer

• Input shape: (None, 32, 32, 1)

2. Convolutional Layer

• Filters: 32
• Kernel size: (3, 3)
• Activation function: ReLU
• Batch normalization
• Max pooling: pool size (2, 2), strides (2, 2)

3. Dropout Layer

• Dropout rate: 0.25

4. Convolutional Layer

• Filters: 64
• Kernel size: (3, 3)
• Activation function: ReLU
• Batch normalization
• Max pooling: pool size (2, 2), strides (2, 2)

5. Dropout Layer

• Dropout rate: 0.25
• Flatten Layer

6. Dense Layer

• Units: 128
• Activation function: ReLU
• Batch normalization

7. Dropout Layer

• Dropout rate: 0.5

8. Dense Layer

• Units: 6 (output layer)
• Activation function: Softmax

Categories to Predict

The model predicts images into the following categories:

• Accessories
• Bags
• Clothes
• Shoes
• Watches

Model Files

• model_config.json: Configuration file containing the model architecture.
• model_weights.h5: File containing the model weights.

Feel free to use this model for your category classification tasks!