def get_dnn_cfg(dnn_fname):
""" Construct a minimum required NetworkConfig given a model file """
model_data = io.json_load(dnn_fname)
cfg = NetworkConfig()
cfg.hidden_layers_sizes = []
i = 0
while 'W{}'.format(i) in model_data:
W_shape = string_2_array(model_data['W{}'.format(i)]).shape
# Currently factored layer can only be the first hidden layer
# TODO: change this!
if i == 0:
if 'side_W{}_0'.format(i) in model_data:
factored_cfg = FactoredConfig()
j = 0
while 'side_b{}_{}'.format(i, j) in model_data:
assert 'side_W{}_{}'.format(i, j) in model_data
side_W_shape = string_2_array(model_data['side_W{}_{}'.format(i, j)]).shape
if j == 0:
factored_cfg.n_in_main = W_shape[0]
factored_cfg.n_in_side = side_W_shape[0]
# NOTE: this assumes that main and secondary features
# are disjoint, but this is not required by the model.
# TODO: find a way to relax this assumption.
cfg.n_ins = W_shape[0] + side_W_shape[0]
factored_cfg.side_layers.append(side_W_shape[1])
j += 1
cfg.factored_cfg = factored_cfg
else:
cfg.n_ins = W_shape[0]
if 'W{}'.format(i + 1) in model_data:
cfg.hidden_layers_sizes.append(W_shape[1])
else:
cfg.n_outs = W_shape[1]
i += 1
return cfg
