Meow Meow Detector

Description:

• Implementing the Logistic Regression Classifier with Neural Networks mindset to detect cats from images.
• Librabries used: Matplotlib, h5py, numpy, PIL and scipy.
• We are given a dataset of cats images as h5 file and the task is to tell if the image is a cat or not.
• The user can clone the repo and test the model by giving it his images.
• The Model was trained on small dataset and its test accuracy is 70% while its training accuracy is approximatly 100%.

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The Process of The Learning Algorithm:

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Mathematical expression of the algorithm:

For one example $x^{(i)}$: $$z^{(i)} = w^T x^{(i)} + b \tag{1}$$

$$\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\tag{2}$$

$$ \mathcal{L}(a^{(i)}, y^{(i)}) = - y^{(i)} \log(a^{(i)}) - (1-y^{(i)} ) \log(1-a^{(i)})\tag{3}$$

The cost is then computed by summing over all training examples:

$$ J = \frac{1}{m} \sum_{i=1}^m \mathcal{L}(a^{(i)}, y^{(i)})\tag{4}$$

Optimization

The goal is to learn $w$ and $b$ by minimizing the cost function $J$. For a parameter $\theta$, the update rule is $\theta$ = $\theta$ - $\alpha$ $\text{ } d\theta$, where $\alpha$ is the learning rate.

Predict

We are able now to use w and b to predict the labels for a dataset X. Implement the predict() function. There are two steps to computing predictions:

1. Calculate $\hat{Y} = A = \sigma(w^T X + b)$

2. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector Y_prediction. If you wish, you can use an if/else statement in a for loop (though there is also a way to vectorize this).

Installation

To get started with Meow Meow Detector, follow these installation steps:

Prerequisets:

1. Python3
2. Install libraries mentioned above using the following command:

pip3 install <library name>

Run The Model

1. Clone this repository to your local machine:

git clone https://github.com/shehabomar/Meow-Detector.git

2. Navigate to the project directory:

cd Meow-Detector

3. Run the model:

python3 logistic-regression-cats.py