lightweight_dataset_usage_example.py

# Usage Example for the Fall Prediction Dataset
# Please install dependencies before:
# pip install -r requirements.txt

# Import necessary libraries
import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout, Input
from convert_and_load_dataset import load_dataset, convert_and_load_dataset


# Example for local converting and loading (frist time usage take a while)
# X_train, X_test, y_train, y_test = convert_and_load_dataset()

# Example for local loading (first time usage may take a while)
X_train, X_test, y_train, y_test = load_dataset()

# Define a simple LSTM model
model = Sequential()
model.add(Input((X_train.shape[1], 1)))
model.add(LSTM(64))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(X_train, y_train, epochs=10, batch_size=64, validation_data=(X_test, y_test))

# Evaluate the model on the test set
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test Accuracy: {accuracy * 100:.2f}%")

# You can save the model if needed
# model.save('fall_prediction_model.h5')