forked from aayu24/Xor_lstm
/
train.py
50 lines (44 loc) · 1.29 KB
/
train.py
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import argparse
import numpy as np
from utils import build_model, generate_samples, model_plot
np.random.seed(42)
def main(length=40, num_epochs=20):
'''
Build and train LSTM network to solve XOR problem
'''
X_train, y_train, X_test, y_test = generate_samples(length=length)
model = build_model()
history = model.fit(
X_train,
y_train,
epochs=num_epochs,
batch_size=32,
validation_split=0.10,
shuffle=False)
# Evaluate model on test set
preds = model.predict(X_test)
preds = np.round(preds[:, 0]).astype('float32')
acc = (np.sum(preds == y_test) / len(y_test)) * 100
print('Accuracy: {:.2f}%'.format(acc))
# Plotting loss and accuracy
model_plot(history)
return
if __name__ == '__main__':
'''
Execute main program
'''
# Grab user arguments
parser = argparse.ArgumentParser()
parser.add_argument(
'-l',
'--length',
help='define binary string length (40 or -1)')
args = parser.parse_args()
if args.length == '50':
print("Generating binary strings of length 40")
main(length=50)
elif args.length == '-1':
print("Generating binary strings of length b/w 1 and 40")
main(length=-1)
else:
print('Invalid entry')