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assignment2.py
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assignment2.py
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import argparse
import numpy as np
from utils import load_dataset
from model.model_utils import bag_of_words_matrix, labels_matrix
from model.ffnn import NeuralNetwork
from helper import batch_train, minibatch_train
DATA_PATH = './data/dataset.csv'
def main():
parser = argparse.ArgumentParser(
description='Train feedforward neural network'
)
parser.add_argument(
'--minibatch', dest='minibatch',
help='Train feedforward neural network with mini-batch gradient descent/SGD',
action='store_true'
)
parser.add_argument(
'--train', dest='train',
help='Turn on this flag when you are ready to train the model with backpropagation.',
action='store_true'
)
args = parser.parse_args()
sentences, intent, unique_intent = load_dataset(DATA_PATH)
X = bag_of_words_matrix(sentences=sentences,COUNT_THRESHOLD=2)
Y = labels_matrix(data=(intent,unique_intent))
print(f"Input Matrix Shape: {X.shape}")
print(f"Target Matrix Shape: {Y.shape}")
model = NeuralNetwork(input_size = X.shape[0]+1,
hidden_size = 150,
num_classes = Y.shape[0],
learning_rate = 0.005)
if not args.minibatch:
print("Training FFNN using batch gradient descent...")
model = batch_train(X, Y, model, train_flag = args.train)
else:
print("Training FFNN using mini-batch gradient descent...")
model = minibatch_train(X, Y, model,BATCH_SIZE=64, train_flag=args.train)
if __name__ == "__main__":
main()