def test_batch_train_sequence_model():
data_dir = './data/'
data = load_data.load_amazon_reviews_sentiment_analysis_dataset(data_dir)
acc, loss = batch_train_sequence_model.batch_train_sequence_model(data)
assert acc == pytest.approx(0.61, 0.02)
assert loss == pytest.approx(0.89, 0.02)
# Train and validate model.
history = model.fit_generator(
generator=training_generator,
steps_per_epoch=steps_per_epoch,
validation_data=validation_generator,
validation_steps=validation_steps,
callbacks=callbacks,
epochs=epochs,
verbose=2) # Logs once per epoch.
# Print results.
history = history.history
print('Validation accuracy: {acc}, loss: {loss}'.format(
acc=history['val_acc'][-1], loss=history['val_loss'][-1]))
# Save model.
model.save('amazon_reviews_sepcnn_model.h5')
return history['val_acc'][-1], history['val_loss'][-1]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='./data',
help='input data directory')
FLAGS, unparsed = parser.parse_known_args()
# Using the Amazon reviews dataset to demonstrate training of
# sequence model with batches of data.
data = load_data.load_amazon_reviews_sentiment_analysis_dataset(
FLAGS.data_dir)
batch_train_sequence_model(data)