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:
- Input Layer
- Input shape: (None, 32, 32, 1)
- Convolutional Layer
- Filters: 32
- Kernel size: (3, 3)
- Activation function: ReLU
- Batch normalization
- Max pooling: pool size (2, 2), strides (2, 2)
- Dropout Layer
- Convolutional Layer
- Filters: 64
- Kernel size: (3, 3)
- Activation function: ReLU
- Batch normalization
- Max pooling: pool size (2, 2), strides (2, 2)
- Dropout Layer
- Dropout rate: 0.25
- Flatten Layer
- Dense Layer
- Units: 128
- Activation function: ReLU
- Batch normalization
- Dropout Layer
- 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!
