def test_experiment_model_resume(csv_filename):
# Single sequence input, single category output
# Tests saving a model file, loading it to rerun training and predict
input_features = [sequence_feature(encoder='rnn', reduce_output='sum')]
output_features = [category_feature(vocab_size=2, reduce_input='sum')]
# Generate test data
rel_path = generate_data(input_features, output_features, csv_filename)
config = {
'input_features': input_features,
'output_features': output_features,
'combiner': {
'type': 'concat',
'fc_size': 14
},
'training': {
'epochs': 2
}
}
_, _, _, _, output_dir = experiment_cli(config, dataset=rel_path)
logger.info('Experiment Directory: {0}'.format(output_dir))
experiment_cli(config, dataset=rel_path, model_resume_path=output_dir)
predict_cli(os.path.join(output_dir, 'model'), dataset=rel_path)
shutil.rmtree(output_dir, ignore_errors=True)
def test_experiment_model_resume(tmpdir):
# Single sequence input, single category output
# Tests saving a model file, loading it to rerun training and predict
input_features = [sequence_feature(encoder="rnn", reduce_output="sum")]
output_features = [category_feature(vocab_size=2, reduce_input="sum")]
# Generate test data
rel_path = generate_data(input_features, output_features,
os.path.join(tmpdir, "dataset.csv"))
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {
"type": "concat",
"output_size": 14
},
TRAINER: {
"epochs": 2
},
}
_, _, _, _, output_dir = experiment_cli(config,
dataset=rel_path,
output_directory=tmpdir)
experiment_cli(config, dataset=rel_path, model_resume_path=output_dir)
predict_cli(os.path.join(output_dir, "model"), dataset=rel_path)
shutil.rmtree(output_dir, ignore_errors=True)