def main():
options = parse_options()
print(options)
window = T.iscalar('theta')
inputs1 = T.tensor3('inputs1', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
shape = [int(i) for i in options['shape'].split(',')]
nonlinearities = [select_nonlinearity(s) for s in options['nonlinearities'].split(',')]
layer_names = options['layer_names'].split(',')
network = deltanet_majority_vote.load_saved_model(options['input'],
(shape, nonlinearities),
(None, None, options['input_dim']), inputs1, (None, None), mask,
options['lstm_size'], window, options['output_classes'],
use_blstm=options['use_blstm'])
d = deltanet_majority_vote.extract_lstm_weights(network, layer_names, ['f_lstm', 'b_lstm'])
expected_keys = ['f_lstm_w_hid_to_cell', 'f_lstm_w_hid_to_forgetgate', 'f_lstm_w_hid_to_ingate',
'f_lstm_w_hid_to_outgate', 'f_lstm_w_in_to_cell', 'f_lstm_w_in_to_forgetgate',
'f_lstm_w_in_to_ingate', 'f_lstm_w_in_to_outgate', 'f_lstm_b_cell', 'f_lstm_b_forgetgate',
'f_lstm_b_ingate', 'f_lstm_b_outgate',
'b_lstm_w_hid_to_cell', 'b_lstm_w_hid_to_forgetgate',
'b_lstm_w_hid_to_ingate', 'b_lstm_w_hid_to_outgate', 'b_lstm_w_in_to_cell', 'b_lstm_w_in_to_forgetgate',
'b_lstm_w_in_to_ingate', 'b_lstm_w_in_to_outgate', 'b_lstm_b_cell', 'b_lstm_b_forgetgate',
'b_lstm_b_ingate', 'b_lstm_b_outgate']
keys = d.keys()
for k in keys:
assert k in expected_keys
assert type(d[k]) == np.ndarray
if 'output' in options:
print('save extracted weights to {}'.format(options['output']))
save_mat(d, options['output'])
def load_decoder(path, shapes, nonlinearities):
nn = sio.loadmat(path)
weights = []
biases = []
shapes = [int(s) for s in shapes.split(',')]
nonlinearities = [select_nonlinearity(nonlinearity) for nonlinearity in nonlinearities.split(',')]
for i in range(len(shapes)):
weights.append(nn['w{}'.format(i+1)].astype('float32'))
biases.append(nn['b{}'.format(i+1)][0].astype('float32'))
return weights, biases, shapes, nonlinearities
def load_decoder(path, shapes, nonlinearities):
nn = sio.loadmat(path)
weights = []
biases = []
shapes = [int(s) for s in shapes.split(',')]
nonlinearities = [select_nonlinearity(nonlinearity) for nonlinearity in nonlinearities.split(',')]
for i in range(len(shapes)):
weights.append(nn['w{}'.format(i+1)].astype('float32'))
biases.append(nn['b{}'.format(i+1)][0].astype('float32'))
return weights, biases, shapes, nonlinearities
def main():
options = parse_options()
print(options)
window = T.iscalar('theta')
inputs1 = T.tensor3('inputs1', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
shape = [int(i) for i in options['shape'].split(',')]
nonlinearities = [
select_nonlinearity(s) for s in options['nonlinearities'].split(',')
]
layer_names = options['layer_names'].split(',')
network = deltanet_majority_vote.load_saved_model(
options['input'], (shape, nonlinearities),
(None, None, options['input_dim']),
inputs1, (None, None),
mask,
options['lstm_size'],
window,
options['output_classes'],
use_blstm=options['use_blstm'])
d = deltanet_majority_vote.extract_lstm_weights(network, layer_names,
['f_lstm', 'b_lstm'])
expected_keys = [
'f_lstm_w_hid_to_cell', 'f_lstm_w_hid_to_forgetgate',
'f_lstm_w_hid_to_ingate', 'f_lstm_w_hid_to_outgate',
'f_lstm_w_in_to_cell', 'f_lstm_w_in_to_forgetgate',
'f_lstm_w_in_to_ingate', 'f_lstm_w_in_to_outgate', 'f_lstm_b_cell',
'f_lstm_b_forgetgate', 'f_lstm_b_ingate', 'f_lstm_b_outgate',
'b_lstm_w_hid_to_cell', 'b_lstm_w_hid_to_forgetgate',
'b_lstm_w_hid_to_ingate', 'b_lstm_w_hid_to_outgate',
'b_lstm_w_in_to_cell', 'b_lstm_w_in_to_forgetgate',
'b_lstm_w_in_to_ingate', 'b_lstm_w_in_to_outgate', 'b_lstm_b_cell',
'b_lstm_b_forgetgate', 'b_lstm_b_ingate', 'b_lstm_b_outgate'
]
keys = d.keys()
for k in keys:
assert k in expected_keys
assert type(d[k]) == np.ndarray
if 'output' in options:
print('save extracted weights to {}'.format(options['output']))
save_mat(d, options['output'])
def main():
options = parse_options()
print(options)
window = T.iscalar('theta')
inputs1 = T.tensor3('inputs1', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
shape = [int(i) for i in options['shape'].split(',')]
nonlinearities = [select_nonlinearity(s) for s in options['nonlinearities'].split(',')]
network = deltanet_majority_vote.load_saved_model(options['input'],
(shape, nonlinearities),
(None, None, options['input_dim']), inputs1, (None, None), mask,
options['lstm_size'], window, options['output_classes'],
use_blstm=options['use_blstm'])
d = deltanet_majority_vote.extract_encoder_weights(network, ['fc1', 'fc2', 'fc3', 'bottleneck'],
[('w1', 'b1'), ('w2', 'b2'), ('w3', 'b3'), ('w4', 'b4')])
expected_keys = ['w1', 'w2', 'w3', 'w4', 'b1', 'b2', 'b3', 'b4']
keys = d.keys()
for k in keys:
assert k in expected_keys
assert type(d[k]) == np.ndarray
if 'output' in options:
print('save extracted weights to {}'.format(options['output']))
save_mat(d, options['output'])
