def main(unused_args):
require_flag('billboard_path')
feature_type = FLAGS.feature_type
cache_filename = '../billboard-datasets-%s.cpkl' % feature_type
if os.path.exists(cache_filename):
datasets = load_datasets(cache_filename)
else:
datasets = billboard.read_billboard_datasets(FLAGS.billboard_path,
feature_type=FLAGS.feature_type)
dump_datasets(datasets, cache_filename)
# grid = {
# 'n_hidden': [300, 800, 1300, 1800, 2300, 2800, 3300],
# 'n_steps': [200],
# 'spectral_radius': [1.],
# 'connectivity': [.01, .0001],
# 'max_leakage': [.99],
# 'min_leakage': [.3],
# 'ridge_beta': [0, .5],
# 'input_scaling': [.2],
# 'input_shift': [0],
# }
grid = {
'n_hidden': [2000],
'n_steps': [200],
'spectral_radius': [1.],
'connectivity': [.01],
'max_leakage': [.99],
'min_leakage': [.3],
'ridge_beta': [.5],
'input_scaling': [.2],
'input_shift': [0],
}
grid_product = list(dict_product(grid))
#random.shuffle(grid_product)
for config in grid_product:
config['n_inputs'] = datasets.train.feature_reader.num_features
config['n_outputs'] = datasets.train.label_reader.num_labels
print '\n%s' % config
if FLAGS.backend == 'numpy':
numpy_eval(datasets, config)
elif FLAGS.backend == 'tensorflow':
tensorflow_eval(datasets, config)
else:
print 'unknown backend'