def batch_sgd_step_disk(self, lines, lrate): # Get nn input and output from line batch token_list, relation_list = utils.parse_processed_lines(lines) inp = [] for tokens in token_list: inp.append(np.asarray([self.word_to_ind.get(token, self.default_index) for token in tokens])) inp = np.asarray(inp) rel = [self.rel_to_ind[relation] for relation in relation_list] out = np.zeros((len(lines), self.odim)) out[range(len(lines)), rel] = 1 out = np.asarray(out) print 'Example input', type(inp), inp.size, type(inp[0]), inp[0].size #print 'Example output', rel hist = self.model.fit(inp, out, batch_size = len(lines), nb_epoch = 1, verbose = 0) return hist['loss'][0]*len(lines)
def batch_sgd(self, lines): # Get nn input and output from line batch token_list, relation_list = utils.parse_processed_lines(lines) inp = [] for tokens in token_list: inp.append(np.asarray([self.word_to_ind.get(token, self.default_index) for token in tokens])) inp = np.asarray(inp) inp = sequence.pad_sequences(inp) print 'Example input', type(inp), inp.size, type(inp[0]), inp[0].size rel = [self.rel_to_ind[relation] for relation in relation_list] #print 'Example output', rel out = np.zeros((len(lines), self.odim)) out[range(len(lines)), rel] = 1 out = np.asarray(out) hist = self.model.fit(inp, out, batch_size = self.batch_size, nb_epoch = self.nepochs, verbose = 1)
def batch_sgd_step_disk(self, lines, lrate): # Get nn input and output from line batch token_list, relation_list = utils.parse_processed_lines(lines) inp = [] for tokens in token_list: inp.append( np.asarray([ self.word_to_ind.get(token, self.default_index) for token in tokens ])) inp = np.asarray(inp) rel = [self.rel_to_ind[relation] for relation in relation_list] out = np.zeros((len(lines), self.odim)) out[range(len(lines)), rel] = 1 out = np.asarray(out) print 'Example input', type(inp), inp.size, type(inp[0]), inp[0].size #print 'Example output', rel hist = self.model.fit(inp, out, batch_size=len(lines), nb_epoch=1, verbose=0) return hist['loss'][0] * len(lines)
def batch_sgd(self, lines): # Get nn input and output from line batch token_list, relation_list = utils.parse_processed_lines(lines) inp = [] for tokens in token_list: inp.append( np.asarray([ self.word_to_ind.get(token, self.default_index) for token in tokens ])) inp = np.asarray(inp) inp = sequence.pad_sequences(inp) print 'Example input', type(inp), inp.size, type(inp[0]), inp[0].size rel = [self.rel_to_ind[relation] for relation in relation_list] #print 'Example output', rel out = np.zeros((len(lines), self.odim)) out[range(len(lines)), rel] = 1 out = np.asarray(out) hist = self.model.fit(inp, out, batch_size=self.batch_size, nb_epoch=self.nepochs, verbose=1)