update

generator/ctrl/ctrl_generator.py

@@ -158,7 +158,7 @@ class CTRLGenerator():
         if prompt[-1] != " ":
             prompt = prompt + " "
 
-        #prompt = second_to_first_person(prompt)
+        prompt = second_to_first_person(prompt)
 
         prompt = self.control_code + prompt
         # print("\n\nAFTER PROMPT_REPLACE")
@@ -172,7 +172,7 @@ class CTRLGenerator():
         first_letter_capitalized = result[0].isupper()
         result = result.replace("#", "")
         result = result.replace("*", "")
-        #result = first_to_second_person(result)
+        result = first_to_second_person(result)
         result = remove_profanity(result)
 
         if not first_letter_capitalized:

generator/ctrl/training_utils/make_forest_tf_records.py

@@ -11,6 +11,7 @@ import argparse
 import fastBPE
 import platform
 import json
+from story.utils import *
 
 def make_samples_helper(context, story_block, action_results, path, tree_id):
 
@@ -45,7 +46,13 @@ def build_tokenized_samples(bpe, tree):
 
         sample[2] = sample[2][0].lower() + sample[2][1:]
         sample[2] = "You " + sample[2]
-        string_samples.append(" ".join(sample))
+
+        new_sample = []
+
+        for item in sample:
+            new_sample.append(second_to_first_person(item))
+
+        string_samples.append(" ".join(new_sample))
 
     tokenized_samples = [bpe.apply([sample.encode('ascii', errors='ignore') if not use_py3 else sample])[0] for sample in
                          string_samples]  # will NOT work for non-English texts
@@ -65,7 +72,7 @@ use_py3 = platform.python_version()[0] == '3'
 
 paths_to_train_files = ["apoc_seed1.json","apoc_seed2.json","apoc_seed3.json","apoc_seed4.json"]
 seq_length = 256
-domain = ["Writing", "Text@@", ":"]
+domain = ["Apocalypse"]
 
 
 # Build sequences from JSON

generator/ctrl/training_utils/training.py

@@ -142,7 +142,7 @@ def loss(labels, logits):
 # the optimizer is not used since this code only supports inference
 # however, to compile the model, we still define it
 optimizer = tf.contrib.estimator.clip_gradients_by_norm(
-    tf.train.AdagradOptimizer(learning_rate=1e-4), 0.25)
+    tf.train.AdagradOptimizer(learning_rate=3e-3), 0.25)
 
 # compile the model with the optimizer and loss
 model.compile(optimizer=optimizer, loss=loss)