Review comments updated

tutorials/nnvm/nlp/keras_s2s_translate.py

@@ -64,7 +64,7 @@
 download(data_url, model_file)
 
 latent_dim = 256  # Latent dimensionality of the encoding space.
-num_samples = 10000  # Number of samples used for training.
+test_samples = 10000  # Number of samples used for testing.
 
 ######################################################################
 # Process the data file
@@ -78,10 +78,10 @@
 target_characters = set()
 with open(data_file, 'r', encoding='utf-8') as f:
     lines = f.read().split('\n')
-num_samples = min(num_samples, len(lines))
+test_samples = min(test_samples, len(lines))
 max_encoder_seq_length = 0
 max_decoder_seq_length = 0
-for line in lines[:num_samples]:
+for line in lines[:test_samples]:
     input_text, target_text = line.split('\t')
     # We use "tab" as the "start sequence" character
     # for the targets, and "\n" as "end sequence" character.
@@ -228,10 +228,10 @@ def generate_input_seq(input_text):
 ######################################################################
 # Run the model
 # -------------
-# Randonly take some text and translate
+# Randonly take some text from test samples and translate
 for seq_index in range(100):
     # Take one sentence randomly and try to decode.
-    index = random.randint(1, num_samples)
+    index = random.randint(1, test_samples)
     input_text, _ = lines[index].split('\t')
     input_seq = generate_input_seq(input_text)
     decoded_sentence = decode_sequence(input_seq)