def main():
# A simple description
description = 'spock Advanced Tutorial'
# Build out the parser by passing in Spock config objects as *args after description
config = ConfigArgBuilder(ModelConfig, DataConfig, SGDConfig, desc=description).generate()
# Instantiate our neural net using
basic_nn = BasicNet(model_config=config.ModelConfig)
# Make some random data (BxH): H has dim of features in
x_data = torch.rand(config.DataConfig.n_samples, config.ModelConfig.n_features)
y_data = torch.randint(0, 3, (config.DataConfig.n_samples,))
# Run some training
train(x_data, y_data, basic_nn, config.ModelConfig, config.DataConfig, config.SGDConfig)
def main():
# A simple description
description = 'spock Tutorial'
# Build out the parser by passing in Spock config objects as *args after description
config = ConfigArgBuilder(
ModelConfig, desc=description,
create_save_path=True).save(file_extension='.toml').generate()
# Instantiate our neural net using
basic_nn = BasicNet(model_config=config.ModelConfig)
# Make some random data (BxH): H has dim of features in
test_data = torch.rand(10, config.ModelConfig.n_features)
result = basic_nn(test_data)
print(result)