def main(path, is_training, is_predicting, model_weights_file,
submission_file):
print('Starting train_statefarm.py')
print('* using path: {0}'.format(path))
print('* training: {0}, predicting: {1}'.format(is_training,
is_predicting))
batch_size = 64
data_provider = DataProvider(os.path.join(path, 'partition'), batch_size)
feature_provider = FeatureProvider(data_provider)
training_data_provider = TrainingDataProvider(data_provider,
feature_provider)
builder = ModelBuilder(training_data_provider,
dropout=0.6,
batch_size=batch_size)
if is_training:
print('Train last layer of dense model with batch normalization.')
builder.train_last_layer()
if is_training:
print('Train dense layers of model with batch normalization.')
builder.train_dense_layers()
model = builder.build(data_provider)
if not is_training:
print('Loading model weights from {0}'.format(model_weights_file))
model.load_weights(
data_provider.get_weight_filepath(model_weights_file))
else:
model.train()
print('Writing model weights to {0}'.format(model_weights_file))
model.save_weights(
data_provider.get_weight_filepath(model_weights_file))
if is_predicting:
print('Writing predictions to {0}'.format(submission_file))
batch_size = 2
data_provider = DataProvider(path, batch_size)
predict_states(model, data_provider, batch_size, submission_file)
