Minibatch the forward pass. THe output argmax is incorrect...

examples/spacy_dynet_lstm.py

@@ -118,6 +118,42 @@ class BiTagger(object):
             tags.append(self.vt.i2w[np.argmax(out.npvalue())])
         return tags
 
+    def predict_batch(self, words_batch):
+        dynet.renew_cg()
+        length = max(len(words) for words in words_batch)
+        word_ids = np.zeros((length, len(words_batch)), dtype='int32')
+        for j, words in enumerate(words_batch):
+            for i, word in enumerate(words):
+                word_ids[i, j] = self.vw.w2i.get(word, self.UNK)
+        wembs = [dynet.lookup_batch(self._E, word_ids[i]) for i in range(length)]
+        
+        f_state = self._fwd_lstm.initial_state()
+        b_state = self._bwd_lstm.initial_state()
+
+        fw = [x.output() for x in f_state.add_inputs(wembs)]
+        bw = [x.output() for x in b_state.add_inputs(reversed(wembs))]
+
+        H = dynet.parameter(self._pH)
+        O = dynet.parameter(self._pO)
+        
+        tags_batch = [[] for _ in range(len(words_batch))]
+        for i, (f, b) in enumerate(zip(fw, reversed(bw))):
+            r_t = O * (dynet.tanh(H * dynet.concatenate([f, b])))
+            out = dynet.softmax(r_t).npvalue()
+            for j in range(len(words_batch)):
+                tags_batch[j].append(self.vt.i2w[np.argmax(out.T[j])])
+        return tags_batch
+
+    def pipe(self, sentences):
+        batch = []
+        for words in sentences:
+            batch.append(words)
+            if len(batch) == self._minibatch_size:
+                tags_batch = self.predict_batch(batch)
+                for words, tags in zip(batch, tags_batch):
+                    yield tags
+                batch = []
+
     def update(self, words, tags):
         self._words_batch.append(words)
         self._tags_batch.append(tags)
@@ -193,10 +229,9 @@ def main(train_loc, dev_loc, model_dir):
                 tagged = 0
             if i % 10000 == 0:
                 good = bad = 0.0
-                for sent in test:
+                word_sents = [[w for w, t in sent] for sent in test]
-                    #word_ids = [vw.w2i.get(w, UNK) for w, t in sent]
+                gold_sents = [[t for w, t in sent] for sent in test]
-                    tags = tagger([w for w, t in sent])
+                for words, tags, golds in zip(words, tagger.pipe(words), gold_sents):
-                    golds = [t for w, t in sent]
                     for go, gu in zip(golds, tags):
                         if go == gu:
                             good += 1