Add example for visualizing word vectors with TensorBoard Projector

Use:

```bash
python vectors_tensorboard.py en_core_web_lg ./output_folder spaCy_large
```

examples/vectors_tensorboard.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+# coding: utf8
+"""Visualize spaCy word vectors in Tensorboard.
+
+Adapted from: https://gist.github.com/BrikerMan/7bd4e4bd0a00ac9076986148afc06507
+"""
+from __future__ import unicode_literals
+
+from os import path
+
+import math
+import numpy
+import plac
+import spacy
+import tensorflow as tf
+import tqdm
+from tensorflow.contrib.tensorboard.plugins.projector import visualize_embeddings, ProjectorConfig
+
+
+@plac.annotations(
+    vectors_loc=("Path to spaCy model that contains vectors", "positional", None, str),
+    out_loc=("Path to output folder for tensorboard session data", "positional", None, str),
+    name=("Human readable name for tsv file and vectors tensor", "positional", None, str),
+)
+def main(vectors_loc, out_loc, name="spaCy_vectors"):
+    meta_file = "{}.tsv".format(name)
+    out_meta_file = path.join(out_loc, meta_file)
+
+    print('Loading spaCy vectors model: {}'.format(vectors_loc))
+    model = spacy.load(vectors_loc)
+    print('Finding lexemes with vectors attached: {}'.format(vectors_loc))
+    strings_stream = tqdm.tqdm(model.vocab.strings, total=len(model.vocab.strings), leave=False)
+    queries = [w for w in strings_stream if model.vocab.has_vector(w)]
+    vector_count = len(queries)
+
+    print('Building Tensorboard Projector metadata for ({}) vectors: {}'.format(vector_count, out_meta_file))
+
+    # Store vector data in a tensorflow variable
+    tf_vectors_variable = numpy.zeros((vector_count, model.vocab.vectors.shape[1]))
+
+    # Write a tab-separated file that contains information about the vectors for visualization
+    #
+    # Reference: https://www.tensorflow.org/programmers_guide/embedding#metadata
+    with open(out_meta_file, 'wb') as file_metadata:
+        # Define columns in the first row
+        file_metadata.write("Text\tFrequency\n".encode('utf-8'))
+        # Write out a row for each vector that we add to the tensorflow variable we created
+        vec_index = 0
+        for text in tqdm.tqdm(queries, total=len(queries), leave=False):
+            # https://github.com/tensorflow/tensorflow/issues/9094
+            text = '<Space>' if text.lstrip() == '' else text
+            lex = model.vocab[text]
+
+            # Store vector data and metadata
+            tf_vectors_variable[vec_index] = model.vocab.get_vector(text)
+            file_metadata.write("{}\t{}\n".format(text, math.exp(lex.prob) * vector_count).encode('utf-8'))
+            vec_index += 1
+
+    print('Running Tensorflow Session...')
+    sess = tf.InteractiveSession()
+    tf.Variable(tf_vectors_variable, trainable=False, name=name)
+    tf.global_variables_initializer().run()
+    saver = tf.train.Saver()
+    writer = tf.summary.FileWriter(out_loc, sess.graph)
+
+    # Link the embeddings into the config
+    config = ProjectorConfig()
+    embed = config.embeddings.add()
+    embed.tensor_name = name
+    embed.metadata_path = meta_file
+
+    # Tell the projector about the configured embeddings and metadata file
+    visualize_embeddings(writer, config)
+
+    # Save session and print run command to the output
+    print('Saving Tensorboard Session...')
+    saver.save(sess, path.join(out_loc, '{}.ckpt'.format(name)))
+    print('Done. Run `tensorboard --logdir={0}` to view in Tensorboard'.format(out_loc))
+
+
+if __name__ == '__main__':
+    plac.call(main)