This repository contains the code for the Cats vs Dogs Kaggle Challenge for the programming assignment in Week 4 of Deeplearning.ai's "Convolutional Neural Networks" course.
In this project, we use the full Cats vs Dogs dataset of 25k images to build a classifier using convolutional neural networks. The objective is to build a classifier that can distinguish between cats and dogs in images with high accuracy.
Clone this repository:
git clone https://github.com/nadinejackson1/build-classifier-using-cnn.git
The main file for this project is cats_vs_dogs.ipynb, which can be run in Jupyter Notebook or Google Colab. The following functions should be implemented and tested:
create_train_val_dirs: This function creates directories for the training and validation sets.
split_data: This function splits the data into training and validation sets.
train_val_generators: This function creates the data generators for the training and validation sets.
create_model: This function creates the convolutional neural network.
The notebook also includes code for training the model, making predictions, and evaluating the model's performance.
The Cats vs Dogs dataset can be downloaded from the following Kaggle competition page: https://www.kaggle.com/c/dogs-vs-cats/data.
After downloading the dataset, create a directory called data and extract the dataset files to it.
The accuracy of the model will be evaluated using the training history file. The file should be saved as history.pkl in the root directory of the project.
This project is based on the Cats vs Dogs Kaggle Challenge and was developed as a programming assignment for Deeplearning.ai's "Convolutional Neural Networks" course.