Fixed spaCy+Keras example (#2763)

* bug fixes in keras example

* created contributor agreement

.github/contributors/free-variation.md

@@ -0,0 +1,106 @@
+# spaCy contributor agreement
+
+This spaCy Contributor Agreement (**"SCA"**) is based on the
+[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
+The SCA applies to any contribution that you make to any product or project
+managed by us (the **"project"**), and sets out the intellectual property rights
+you grant to us in the contributed materials. The term **"us"** shall mean
+[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
+**"you"** shall mean the person or entity identified below.
+
+If you agree to be bound by these terms, fill in the information requested
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
+should be your GitHub username, with the extension `.md`. For example, the user
+example_user would create the file `.github/contributors/example_user.md`.
+
+Read this agreement carefully before signing. These terms and conditions
+constitute a binding legal agreement.
+
+## Contributor Agreement
+
+1. The term "contribution" or "contributed materials" means any source code,
+object code, patch, tool, sample, graphic, specification, manual,
+documentation, or any other material posted or submitted by you to the project.
+
+2. With respect to any worldwide copyrights, or copyright applications and
+registrations, in your contribution:
+
+    * you hereby assign to us joint ownership, and to the extent that such
+    assignment is or becomes invalid, ineffective or unenforceable, you hereby
+    grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
+    royalty-free, unrestricted license to exercise all rights under those
+    copyrights. This includes, at our option, the right to sublicense these same
+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
+    * you agree that each of us can do all things in relation to your
+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
+
+    * you agree that you will not assert any moral rights in your contribution
+    against us, our licensees or transferees;
+
+    * you agree that we may register a copyright in your contribution and
+    exercise all ownership rights associated with it; and
+
+    * you agree that neither of us has any duty to consult with, obtain the
+    consent of, pay or render an accounting to the other for any use or
+    distribution of your contribution.
+
+3. With respect to any patents you own, or that you can license without payment
+to any third party, you hereby grant to us a perpetual, irrevocable,
+non-exclusive, worldwide, no-charge, royalty-free license to:
+
+    * make, have made, use, sell, offer to sell, import, and otherwise transfer
+    your contribution in whole or in part, alone or in combination with or
+    included in any product, work or materials arising out of the project to
+    which your contribution was submitted, and
+
+    * at our option, to sublicense these same rights to third parties through
+    multiple levels of sublicensees or other licensing arrangements.
+
+4. Except as set out above, you keep all right, title, and interest in your
+contribution. The rights that you grant to us under these terms are effective
+on the date you first submitted a contribution to us, even if your submission
+took place before the date you sign these terms.
+
+5. You covenant, represent, warrant and agree that:
+
+    * Each contribution that you submit is and shall be an original work of
+    authorship and you can legally grant the rights set out in this SCA;
+
+    * to the best of your knowledge, each contribution will not violate any
+    third party's copyrights, trademarks, patents, or other intellectual
+    property rights; and
+
+    * each contribution shall be in compliance with U.S. export control laws and
+    other applicable export and import laws. You agree to notify us if you
+    become aware of any circumstance which would make any of the foregoing
+    representations inaccurate in any respect. We may publicly disclose your
+    participation in the project, including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable
+U.S. Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+    * [ ] I am signing on behalf of myself as an individual and no other person
+    or entity, including my employer, has or will have rights with respect to my
+    contributions.
+
+    * [ ] I am signing on behalf of my employer or a legal entity and I have the
+    actual authority to contractually bind that entity.
+
+## Contributor Details
+
+| Field                          | Entry                |
+|------------------------------- | -------------------- |
+| Name                           |  John Stewart        |
+| Company name (if applicable)   |  Amplify             |
+| Title or role (if applicable)  |  SVP Research        |
+| Date                           |  14/09/2018          |
+| GitHub username                |  free-variation      |
+| Website (optional)             |                      |

examples/deep_learning_keras.py

@@ -92,11 +92,13 @@ def get_features(docs, max_length):
 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_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))
     dev_docs = list(nlp.pipe(dev_texts))
@@ -107,7 +109,7 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
     train_X = get_features(train_docs, lstm_shape['max_length'])
     dev_X = get_features(dev_docs, lstm_shape['max_length'])
     model.fit(train_X, train_labels, validation_data=(dev_X, dev_labels),
-              nb_epoch=nb_epoch, batch_size=batch_size)
+              epochs=nb_epoch, batch_size=batch_size)
     return model
 
 
@@ -138,15 +140,9 @@ def get_embeddings(vocab):
 
 
 def evaluate(model_dir, texts, labels, max_length=100):
-    def create_pipeline(nlp):
+    nlp = spacy.load('en_vectors_web_lg')
-        '''
+    nlp.add_pipe(nlp.create_pipe('sentencizer'))
-        This could be a lambda, but named functions are easier to read in Python.
+    nlp.add_pipe(SentimentAnalyser.load(model_dir, nlp, max_length=max_length))
-        '''
-        return [nlp.tagger, nlp.parser, SentimentAnalyser.load(model_dir, nlp,
-                                                               max_length=max_length)]
-
-    nlp = spacy.load('en')
-    nlp.pipeline = create_pipeline(nlp)
 
     correct = 0
     i = 0
@@ -186,7 +182,7 @@ 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
+         nb_epoch=5, batch_size=256, nr_examples=-1):  # Training params
     if model_dir is not None:
         model_dir = pathlib.Path(model_dir)
     if train_dir is None or dev_dir is None:
@@ -219,7 +215,7 @@ def main(model_dir=None, train_dir=None, dev_dir=None,
         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_:
+            with (model_dir / 'config.json').open('w') as file_:
                 file_.write(lstm.to_json())