feat(lstm): Capitalize, vary tokenization

KoozDawa/dawa/loader.py

@@ -28,8 +28,8 @@ def clean_text(lines):
     In dataset preparation step, we will first perform text cleaning of the data
     which includes removal of punctuations and lower casing all the words.
     """
-    lines = "".join(v for v in lines if v not in string.punctuation).lower()
-    lines = lines.encode("utf8").decode("ascii", 'ignore')
+    lines = "".join(v for v in lines if v not in string.punctuation)
+    # lines = lines.encode("utf8").decode("ascii", 'ignore')
     return lines
 
 

KoozDawa/dawa/lstm.py

@@ -2,7 +2,6 @@ import warnings
 
 import numpy as np
 from keras import Sequential
-from keras.engine.saving import load_model
 from keras.layers import Embedding, LSTM, Dropout, Dense
 from keras.utils import to_categorical
 from keras_preprocessing.sequence import pad_sequences
@@ -57,14 +56,14 @@ def generate_text(model, tokenizer, seed_text="", nb_words=5, max_sequence_len=0
                 output_word = word
                 break
         seed_text += " " + output_word
-    return seed_text.title()
+    return seed_text.capitalize()
 
 
 def main():
     should_train = True
     nb_epoch = 100
     max_sequence_len = 61  # TODO: Test different default
-    model_file = "../models/dawa_lstm_%i.hd5" % nb_epoch
+    # model_file = "../models/dawa_lstm_%i.hd5" % nb_epoch
     tokenizer = Tokenizer()
 
     if should_train:
@@ -79,21 +78,24 @@ def main():
         model.summary()
 
         model.fit(predictors, label, epochs=nb_epoch, verbose=5)
-        model.save(model_file)
-    else:
-        model = load_model(model_file)
+        # model.save(model_file)
+    # else: # FIXME: Load and predict
+    #     model = load_model(model_file)
 
     for sample in ["",
                    "L'étoile du sol",
                    "Elle me l'a toujours dit",
                    "Les punchlines sont pour ceux"]:
-        nb_words = 50
+        nb_words = 200
         print(generate_text(model, tokenizer, sample, nb_words, max_sequence_len))
 
+    with open("../output/lstm.txt", "a") as f:
         while True:
             input_text = input("> ")
-        print(generate_text(model, tokenizer, input_text, nb_words, max_sequence_len))
-        print(generate_text(model, tokenizer, input_text, nb_words, max_sequence_len))
+            text = generate_text(model, tokenizer, input_text, nb_words, max_sequence_len)
+
+            print(text)
+            f.writelines(text)
 
 
 if __name__ == '__main__':