fix word2vec typo and string encode as bytes.

.gitignore

@@ -11,3 +11,4 @@ dist/*
 checkpoint
 htmlcov
 mnist
+/.vs

examples/2_BasicModels/word2vec.py

@@ -48,7 +48,7 @@
 data_path = 'text8.zip'
 if not os.path.exists(data_path):
     print("Downloading the dataset... (It may take some time)")
-    filename, _ = urllib.urlretrieve(url, data_path)
+    filename, _ = urllib.request.urlretrieve(url, data_path)
     print("Done!")
 # Unzip the dataset file. Text has already been processed
 with zipfile.ZipFile(data_path) as f:
@@ -156,6 +156,7 @@ def next_batch(batch_size, num_skips, skip_window):
 embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True))
 cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True)
 
+tf.train.export_meta_graph(filename='word2vec.meta');
 # Initialize the variables (i.e. assign their default value)
 init = tf.global_variables_initializer()
 
@@ -165,16 +166,16 @@ def next_batch(batch_size, num_skips, skip_window):
     sess.run(init)
 
     # Testing data
-    x_test = np.array([word2id[w] for w in eval_words])
+    x_test = np.array([word2id.get(w.encode(), 0) for w in eval_words])
 
     average_loss = 0
-    for step in xrange(1, num_steps + 1):
+    for step in range(1, num_steps + 1):
         # Get a new batch of data
         batch_x, batch_y = next_batch(batch_size, num_skips, skip_window)
         # Run training op
         _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})
         average_loss += loss
-
+         
         if step % display_step == 0 or step == 1:
             if step > 1:
                 average_loss /= display_step
@@ -186,10 +187,10 @@ def next_batch(batch_size, num_skips, skip_window):
         if step % eval_step == 0 or step == 1:
             print("Evaluation...")
             sim = sess.run(cosine_sim_op, feed_dict={X: x_test})
-            for i in xrange(len(eval_words)):
+            for i in range(len(eval_words)):
                 top_k = 8  # number of nearest neighbors
                 nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                 log_str = '"%s" nearest neighbors:' % eval_words[i]
-                for k in xrange(top_k):
+                for k in range(top_k):
                     log_str = '%s %s,' % (log_str, id2word[nearest[k]])
                 print(log_str)