Added ability to change sampling settings during a chat

Author: pender

README.md

@@ -4,6 +4,10 @@ A toy chatbot powered by deep learning and trained on data from Reddit.
 Here is a sample chat transcript (not cherry-picked). Lines that start with ">" are me; the lines without ">" are the chatbot's responses.
 
 ```
+$ python chatbot.py
+Creating model...
+Restoring weights...
+
 > Hi, how are you?
  It's all good, I'm going to bed now but I'll see if I can get home tomorrow. I'll get back to you.
 > Don't go to bed just yet
@@ -46,6 +50,29 @@ Try playing around with the arguments to `chatbot.py` to obtain better samples:
 
 - **relevance**: Two models are run in parallel: the primary model and the mask model. The mask model is scaled by the relevance value, and then the probabilities of the primary model are multiplied by the complement of the mask model before sampling. The state of the mask model is reset upon each newline character. The net effect is that the model is encouraged to choose a line of dialogue that is most relevant to the prior line of dialogue, even if a more generic response (e.g. "I don't know anything about that") may be more absolutely probable. Lower relevance values put more pressure on the model to produce relevant responses, at the cost of the coherence of the responses. Going much below 1.5 compromises the quality of the responses; 2-3 is the recommended range. Setting it to a negative value disables relevance, and this is the default, because I'm not confident that it qualitatively improves the outputs and it halves the speed of sampling.
 
+These values can also be manipulated during a chat, and the model state can be reset, without restarting the chatbot:
+
+```
+$ python chatbot.py
+Creating model...
+Restoring weights...
+
+> --temperature 1.3
+[Temperature set to 1.3]
+
+> --relevance 2 
+[Relevance set to 2.0]
+
+> --relevance -1
+[Relevance disabled]
+
+> --beam_width 5
+[Beam width set to 5]
+
+> --reset
+[Model state reset]
+```
+
 ### Get training data
 
 If you'd like to train your own model, you'll need training data. There are a few options here.

chatbot.py

@@ -120,41 +120,58 @@ def beam_sample(net, sess, chars, vocab, max_length=200, prime='The ',
 def sanitize_text(vocab, text):
     return ''.join(i for i in text if i in vocab)
 
+def initial_state_with_relevance_masking(net, sess, relevance):
+    if relevance <= 0.: return initial_state(net, sess)
+    else: return [initial_state(net, sess), initial_state(net, sess)]
+
 def chatbot(net, sess, chars, vocab, max_length, beam_width, relevance, temperature):
-    if relevance < 0.:
-        states = initial_state(net, sess)
-    else:
-        states = [initial_state(net, sess), initial_state(net, sess)]
+    states = initial_state_with_relevance_masking(net, sess, relevance)
     while True:
         user_input = sanitize_text(vocab, raw_input('\n> '))
-        user_command_entered, relevance, temperature = process_user_command(
-            user_input, relevance, temperature)
+        user_command_entered, reset, states, relevance, temperature, beam_width = process_user_command(
+            user_input, states, relevance, temperature, beam_width)
+        if reset: states = initial_state_with_relevance_masking(net, sess, relevance)
         if user_command_entered: continue
         states = forward_text(net, sess, states, vocab, '> ' + user_input + "\n>")
-        computer_response_generator = beam_search_generator(sess, net, copy.deepcopy(states),
-            vocab[' '], vocab['\n'], beam_width, forward_with_mask,
-            (relevance, vocab['\n']), temperature)
+        computer_response_generator = beam_search_generator(sess=sess, net=net,
+            initial_state=copy.deepcopy(states), initial_sample=vocab[' '],
+            early_term_token=vocab['\n'], beam_width=beam_width, forward_model_fn=forward_with_mask,
+            forward_args=(relevance, vocab['\n']), temperature=temperature)
         for i, char_token in enumerate(computer_response_generator):
             print(chars[char_token], end='')
             states = forward_text(net, sess, states, vocab, chars[char_token])
             sys.stdout.flush()
             if i >= max_length: break
         states = forward_text(net, sess, states, vocab, '\n> ')
 
-def process_user_command(user_input, relevance, temperature):
-    if user_input.startswith('--temperature '):
-        temperature = float(user_input[len('--temperature '):])
-        print("[Temperature set to {}]".format(temperature))
-        return True, relevance, temperature
-    elif user_input.startswith('--relevance '):
-        if relevance < 0:
-            print("[Relevance is disabled; restart program with relevance > 0 to enable.]")
-            return True, relevance, temperature
-        else:
-            relevance = float(user_input[len('--relevance '):])
-            print("[Relevance set to {}]".format(relevance))
-            return True, relevance, temperature
-    return False, relevance, temperature
+def process_user_command(user_input, states, relevance, temperature, beam_width):
+    user_command_entered = False
+    reset = False
+    try:
+        if user_input.startswith('--temperature '):
+            user_command_entered = True
+            temperature = max(0.001, float(user_input[len('--temperature '):]))
+            print("[Temperature set to {}]".format(temperature))
+        elif user_input.startswith('--relevance '):
+            user_command_entered = True
+            new_relevance = float(user_input[len('--relevance '):])
+            if relevance <= 0. and new_relevance > 0.:
+                states = [states, copy.deepcopy(states)]
+            elif relevance > 0. and new_relevance < 0.:
+                states = states[0]
+            relevance = new_relevance
+            print("[Relevance disabled]" if relevance < 0. else "[Relevance set to {}]".format(relevance))
+        elif user_input.startswith('--beam_width '):
+            user_command_entered = True
+            beam_width = max(1, int(user_input[len('--beam_width '):]))
+            print("[Beam width set to {}]".format(beam_width))
+        elif user_input.startswith('--reset'):
+            user_command_entered = True
+            reset = True
+            print("[Model state reset]")
+    except ValueError:
+        print("[Value error with provided argument.]")
+    return user_command_entered, reset, states, relevance, temperature, beam_width
 
 def consensus_length(beam_outputs, early_term_token):
     for l in xrange(len(beam_outputs[0])):