Cat Detection ML Project

This repository contains a simple machine learning project to classify images as either cat or non-cat. The project is implemented using a two-layer neural network.

Dataset

The dataset consists of two files:

• train_catvnoncat.h5: Contains training set images and labels.
• test_catvnoncat.h5: Contains test set images and labels.

You can load the dataset using the provided load_data function. To load the dataset, run the following code:

train_x_orig, train_y, test_x_orig, test_y, classes = load_data(r'C:\Users\Furkan\Desktop\Assignment-5\dataset\dataset')
# Example of a picture
index = 11
plt.imshow(train_x_orig[index])
print("y = " + str(train_y[0, index]) + ". It's a " + classes[train_y[0, index]].decode("utf-8") + " picture.")

# Explore your dataset
m_train = train_x_orig.shape[0]
num_px = train_x_orig.shape[1]
m_test = test_x_orig.shape[0]

print("Number of training examples: " + str(m_train))
print("Number of testing examples: " + str(m_test))
print("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)")
print("train_x_orig shape: " + str(train_x_orig.shape))
print("train_y shape: " + str(train_y.shape))
print("test_x_orig shape: " + str(test_x_orig.shape))
print("test_y shape: " + str(test_y.shape))

Data Preprocessing

The training and test examples are reshaped and standardized to have feature values between 0 and 1:

# Reshape the training and test examples
train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T
test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T

# Standardize data to have feature values between 0 and 1
train_x = train_x_flatten / 255.
test_x = test_x_flatten / 255.

print("train_x's shape: " + str(train_x.shape))
print("test_x's shape: " + str(test_x.shape))

Model Architecture

The neural network has two layers: one hidden layer with ReLU activation, an output layer with sigmoid activation. The model's parameters are initialized using the initialize_parameters_deep function.

n_x = 12288  # num_px * num_px * 3
n_h = 7
n_y = 1
layers_dims = (n_x, n_h, n_y)

Training the Model

The model is trained using the two_layer_model function, which implements gradient descent. The training process and cost evolution are visualized using the plot_loss function.

parameters, costs = two_layer_model(train_x, train_y, layers_dims, learning_rate=0.0075, num_iterations=3000)

Evaluation

The model's accuracy is evaluated on the test set using the predict function:

accuracy = predict(test_x, test_y, parameters)
print(f"\nAccuracy is {accuracy}")

Results

The training process will display the cost at regular intervals.
After 3000 iterations, the model achieves a certain accuracy on the test set.
Feel free to experiment with hyperparameters and dataset to further improve the model's performance.

License

This project is licensed under the MIT License. Feel free to use and modify the code as needed.