(self, model)
return sents, targets
Given a model, generate sentences from this dataset.

        Args:
            model (Model): Image captioning model.

        Returns:
            list, list containing predicted sentences and target sentences
Given a model, generate sentences from this dataset.
Given a model generate sentences from this dataset .
def predict(self, model):
        """
        Given a model, generate sentences from this dataset.

        Args:
            model (Model): Image captioning model.

        Returns:
            list, list containing predicted sentences and target sentences
        """
        sents = []
        targets = []
        y = self.be.zeros(self.dev_X.shape)
        for mb_idx, (x, t) in enumerate(self):
            y.fill(0)
            # Repeatedly generate next word in sentence and choose max prob word each time.
            for step in range(1, self.max_sentence_length + 1):
                prob = model.fprop((x[0], y), inference=True).get()[:, :-self.be.bsz].copy()
                pred = np.argmax(prob, axis=0)
                prob.fill(0)
                for i in range(step * self.be.bsz):
                    prob[pred[i], i] = 1
                y[:] = prob
            sents += self.prob_to_word(y)
            # Test set, keep list of targets
            if isinstance(self, ImageCaptionTest):
                targets += t[0]
            # Train set, only 1 target
            else:
                targets.append(t[0])

        return sents, targets
def predict ( self , model ) : sents = [ ] targets = [ ] y = self . be . zeros ( self . dev_X . shape ) for mb_idx , ( x , t ) in enumerate ( self ) : y . fill ( 0 ) # Repeatedly generate next word in sentence and choose max prob word each time. for step in range ( 1 , self . max_sentence_length + 1 ) : prob = model . fprop ( ( x [ 0 ] , y ) , inference = True ) . get ( ) [ : , : - self . be . bsz ] . copy ( ) pred = np . argmax ( prob , axis = 0 ) prob . fill ( 0 ) for i in range ( step * self . be . bsz ) : prob [ pred [ i ] , i ] = 1 y [ : ] = prob sents += self . prob_to_word ( y ) # Test set, keep list of targets if isinstance ( self , ImageCaptionTest ) : targets += t [ 0 ] # Train set, only 1 target else : targets . append ( t [ 0 ] ) return sents , targets