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