import numpy as np
from decoder_utils import load_data, load_nnet, decode, int_to_char,\
collapse_seq, load_char_map
if __name__ == '__main__':
print 'Loading data'
fnum = 1
data_dict, alis, keys = load_data(fnum)
print 'Loading neural net'
rnn = load_nnet()
data, labels = data_dict[keys[0]], np.array(alis[keys[0]], dtype=np.int32)
probs = rnn.costAndGrad(data, labels)
probs = np.log(probs.astype(np.float64) + 1e-30)
hyp, hypScore, refScore = decode(probs,
alpha=1.0,
beta=0.0,
beam=200,
method='bg')
char_map = load_char_map()
if labels is not None:
print ' labels:', collapse_seq(int_to_char(labels, char_map))
print ' top hyp:', collapse_seq(int_to_char(hyp, char_map))
print 'score:', hypScore
#print 'ref score:', refScore
import numpy as np
from decoder_utils import load_data, load_nnet, int_to_char,\
collapse_seq, decode, load_char_map
if __name__ == '__main__':
print 'Loading data'
fnum = 1
data_dict, alis, keys = load_data(fnum)
print 'Loading neural net'
rnn = load_nnet()
for k in range(0, 10):
data, labels = data_dict[keys[k]], np.array(alis[keys[k]],
dtype=np.int32)
probs = rnn.costAndGrad(data, labels)
probs = np.log(probs.astype(np.float64) + 1e-30)
hyp, hypScore, refscore = decode(probs,
alpha=0.0, beta=0.0, beam=40, method='clm2')
hyp_pmax, _, _ = decode(probs,
alpha=1.0, beta=0.0, method='pmax')
char_map = load_char_map()
if labels is not None:
print ' labels:', collapse_seq(int_to_char(labels, char_map))
print ' top hyp:', collapse_seq(int_to_char(hyp, char_map))
print 'pmax hyp:', collapse_seq(int_to_char(hyp_pmax, char_map))
print 'score:', hypScore
#print 'ref score:', refScore
import numpy as np
from decoder_utils import load_data, load_nnet, decode, int_to_char, collapse_seq, load_char_map
if __name__ == "__main__":
print "Loading data"
fnum = 1
data_dict, alis, keys = load_data(fnum)
print "Loading neural net"
rnn = load_nnet()
data, labels = data_dict[keys[0]], np.array(alis[keys[0]], dtype=np.int32)
probs = rnn.costAndGrad(data, labels)
probs = np.log(probs.astype(np.float64) + 1e-30)
hyp, hypScore, refScore = decode(probs, alpha=1.0, beta=0.0, beam=200, method="bg")
char_map = load_char_map()
if labels is not None:
print " labels:", collapse_seq(int_to_char(labels, char_map))
print " top hyp:", collapse_seq(int_to_char(hyp, char_map))
print "score:", hypScore
# print 'ref score:', refScore
collapse_seq, decode, load_char_map
if __name__ == '__main__':
print 'Loading data'
fnum = 1
data_dict, alis, keys = load_data(fnum)
print 'Loading neural net'
rnn = load_nnet()
for k in range(0, 10):
data, labels = data_dict[keys[k]], np.array(alis[keys[k]],
dtype=np.int32)
probs = rnn.costAndGrad(data, labels)
probs = np.log(probs.astype(np.float64) + 1e-30)
hyp, hypScore, refscore = decode(probs,
alpha=0.0,
beta=0.0,
beam=40,
method='clm2')
hyp_pmax, _, _ = decode(probs, alpha=1.0, beta=0.0, method='pmax')
char_map = load_char_map()
if labels is not None:
print ' labels:', collapse_seq(int_to_char(labels, char_map))
print ' top hyp:', collapse_seq(int_to_char(hyp, char_map))
print 'pmax hyp:', collapse_seq(int_to_char(hyp_pmax, char_map))
print 'score:', hypScore
#print 'ref score:', refScore
