def set_ensemble_train_func(self):
write('Building an ensemble training function...')
self.optimizer = get_optimizer(self.argv)
self.optimizer.set_params(self.model.params)
if self.argv.load_opt_param:
self.optimizer.load_params(self.argv.load_opt_param)
# 1D: batch_size * n_spans, 2D: [batch index, label id, span index]
span_true = T.imatrix('span_true')
# 1D: batch_size, 2D: n_spans, 3D: 2 * hidden_dim
h_span = self.model.feat_layer.forward(self.model.inputs, self.experts)
# 1D: batch_size, 2D: n_labels, 3D: n_spans; score
logits = self.model.feat_layer.calc_logit_scores(h=h_span)
# 1D: batch_size, 2D: n_labels; span index
span_pred = self.model.argmax_span(logits)
nll = self.model.calc_loss(logits, span_true)
l2_reg = L2Regularizer()
objective = nll + l2_reg(alpha=self.argv.reg, params=self.model.params)
grads = T.grad(cost=objective, wrt=self.model.params)
updates = self.optimizer(grads=grads, params=self.model.params)
self.train_func = theano.function(inputs=self.model.inputs +
[span_true],
outputs=[objective, span_pred],
updates=updates,
mode='FAST_RUN')
def set_train_func(self):
write('Building a training function...')
self.optimizer = get_optimizer(self.argv)
self.optimizer.set_params(self.model.params)
if self.argv.load_opt_param:
write('\tLoading optimization params...')
self.optimizer.load_params(self.argv.load_opt_param)
y_true = T.imatrix('y')
# 1D: batch_size, 2D: n_words, 3D: output_dim
emit_scores = self.model.get_emit_scores()
# 1D: batch_size, 2D: n_words; elem=label id
y_pred = self.model.label_layer.get_y_pred(emit_scores)
# 1D: batch_size; elem=log proba
y_path_proba = self.model.label_layer.get_y_path_proba(
emit_scores, y_true)
l2_reg = L2Regularizer()
cost = -T.mean(y_path_proba) + l2_reg(alpha=self.argv.reg,
params=self.model.params)
grads = T.grad(cost=cost, wrt=self.model.params)
updates = self.optimizer(grads=grads, params=self.model.params)
self.train_func = theano.function(inputs=self.model.inputs + [y_true],
outputs=[cost, y_pred],
updates=updates,
on_unused_input='warn',
mode='FAST_RUN')