Update Keras sentiment analysis example

examples/deep_learning_keras.py

@@ -9,7 +9,9 @@ from keras.models import Sequential, model_from_json
 from keras.layers import LSTM, Dense, Embedding, Dropout, Bidirectional
 from keras.layers import TimeDistributed
 from keras.optimizers import Adam
-import cPickle as pickle
+from spacy.compat import pickle
+
+import thinc.extra.datasets
 
 import spacy
 
@@ -70,11 +72,14 @@ def get_features(docs, max_length):
     for i, doc in enumerate(docs):
         j = 0
         for token in doc:
-            if token.has_vector and not token.is_punct and not token.is_space:
-                Xs[i, j] = token.rank + 1
-                j += 1
-                if j >= max_length:
-                    break
+            vector_id = token.vocab.vectors.find(key=token.orth)
+            if vector_id >= 0:
+                Xs[i, j] = vector_id
+            else:
+                Xs[i, j] = 0
+            j += 1
+            if j >= max_length:
+                break
     return Xs
 
 
@@ -82,12 +87,13 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
         lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5,
         by_sentence=True):
     print("Loading spaCy")
-    nlp = spacy.load('en', entity=False)
+    nlp = spacy.load('en_vectors_web_lg')
+    nlp.add_pipe(nlp.create_pipe('sentencizer'))
     embeddings = get_embeddings(nlp.vocab)
     model = compile_lstm(embeddings, lstm_shape, lstm_settings)
     print("Parsing texts...")
-    train_docs = list(nlp.pipe(train_texts, batch_size=5000, n_threads=3))
-    dev_docs = list(nlp.pipe(dev_texts, batch_size=5000, n_threads=3))
+    train_docs = list(nlp.pipe(train_texts))
+    dev_docs = list(nlp.pipe(dev_texts))
     if by_sentence:
         train_docs, train_labels = get_labelled_sentences(train_docs, train_labels)
         dev_docs, dev_labels = get_labelled_sentences(dev_docs, dev_labels)
@@ -111,9 +117,10 @@ def compile_lstm(embeddings, shape, settings):
             mask_zero=True
         )
     )
-    model.add(TimeDistributed(Dense(shape['nr_hidden'], bias=False)))
-    model.add(Bidirectional(LSTM(shape['nr_hidden'], dropout_U=settings['dropout'],
-                                 dropout_W=settings['dropout'])))
+    model.add(TimeDistributed(Dense(shape['nr_hidden'], use_bias=False)))
+    model.add(Bidirectional(LSTM(shape['nr_hidden'],
+                                 recurrent_dropout=settings['dropout'],
+                                 dropout=settings['dropout'])))
     model.add(Dense(shape['nr_class'], activation='sigmoid'))
     model.compile(optimizer=Adam(lr=settings['lr']), loss='binary_crossentropy',
 		  metrics=['accuracy'])
@@ -121,12 +128,7 @@ def compile_lstm(embeddings, shape, settings):
 
 
 def get_embeddings(vocab):
-    max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector)
-    vectors = numpy.ndarray((max_rank+1, vocab.vectors_length), dtype='float32')
-    for lex in vocab:
-        if lex.has_vector:
-            vectors[lex.rank + 1] = lex.vector
-    return vectors
+    return vocab.vectors.data
 
 
 def evaluate(model_dir, texts, labels, max_length=100):
@@ -174,22 +176,32 @@ def read_data(data_dir, limit=0):
     batch_size=("Size of minibatches for training LSTM", "option", "b", int),
     nr_examples=("Limit to N examples", "option", "n", int)
 )
-def main(model_dir, train_dir, dev_dir,
+def main(model_dir=None, train_dir=None, dev_dir=None,
          is_runtime=False,
          nr_hidden=64, max_length=100, # Shape
          dropout=0.5, learn_rate=0.001, # General NN config
          nb_epoch=5, batch_size=100, nr_examples=-1):  # Training params
-    model_dir = pathlib.Path(model_dir)
-    train_dir = pathlib.Path(train_dir)
-    dev_dir = pathlib.Path(dev_dir)
+    if model_dir is not None:
+        model_dir = pathlib.Path(model_dir)
+    if train_dir is None or dev_dir is None:
+        imdb_data = thinc.extra.datasets.imdb()
     if is_runtime:
-        dev_texts, dev_labels = read_data(dev_dir)
+        if dev_dir is None:
+            dev_texts, dev_labels = zip(*imdb_data[1])
+        else:
+            dev_texts, dev_labels = read_data(dev_dir)
         acc = evaluate(model_dir, dev_texts, dev_labels, max_length=max_length)
         print(acc)
     else:
-        print("Read data")
-        train_texts, train_labels = read_data(train_dir, limit=nr_examples)
-        dev_texts, dev_labels = read_data(dev_dir, limit=nr_examples)
+        if train_dir is None:
+            train_texts, train_labels = zip(*imdb_data[0])
+        else:
+            print("Read data")
+            train_texts, train_labels = read_data(train_dir, limit=nr_examples)
+        if dev_dir is None:
+            dev_texts, dev_labels = zip(*imdb_data[1])
+        else:
+            dev_texts, dev_labels = read_data(dev_dir, imdb_data, limit=nr_examples)
         train_labels = numpy.asarray(train_labels, dtype='int32')
         dev_labels = numpy.asarray(dev_labels, dtype='int32')
         lstm = train(train_texts, train_labels, dev_texts, dev_labels,
@@ -198,10 +210,11 @@ def main(model_dir, train_dir, dev_dir,
                      {},
                      nb_epoch=nb_epoch, batch_size=batch_size)
         weights = lstm.get_weights()
-        with (model_dir / 'model').open('wb') as file_:
-            pickle.dump(weights[1:], file_)
-        with (model_dir / 'config.json').open('wb') as file_:
-            file_.write(lstm.to_json())
+        if model_dir is not None:
+            with (model_dir / 'model').open('wb') as file_:
+                pickle.dump(weights[1:], file_)
+            with (model_dir / 'config.json').open('wb') as file_:
+                file_.write(lstm.to_json())
 
 
 if __name__ == '__main__':