from hare import Hare, Conversation
from hare.tensorflowbrain import BiGruBrain
mockhare = Hare()
mockhare.brain = BiGruBrain()
for i in range(10000):
convo = Conversation()
convo.add_utterance(speaker='a', content='c c c c c')
convo.add_utterance(speaker='b', content='c c c c c')
convo.add_utterance(speaker='b', content='c c c c b')
convo.add_utterance(speaker='a', content='c c c c a')
convo.label_speaker('b', 1)
mockhare.add_conversation(convo)
mockhare.train()
mockhare.visualize_history_for_conversation()
elif line[0] == '#':
try:
current_conversation.label_speaker(line.split()[1], 1)
except IndexError:
continue
conversations.append(current_conversation)
current_conversation = Conversation()
if len(conversations) % 100 == 0:
print(len(conversations))
if len(conversations) == NR_OF_CONVERSATIONS:
break
continue
speaker, content = line.split('\t')
current_conversation.add_utterance(speaker, content)
#Add to a hare object
moba_hare = Hare()
for conversation in conversations:
moba_hare.add_conversation(conversation)
moba_hare.brain = BiGruBrain()
moba_hare.brain.downsampling = True
moba_hare.brain._max_sequence_length = 500
moba_hare.train()
moba_hare.save('moba')
from hare import Hare, Conversation
from hare.brain import BiGruBrain
brain: BiGruBrain = BiGruBrain()
brain.embedding_location = '/vol/bigdata/word_embeddings/glove/glove.6B.50d.txt'
brain.verbose = True
hare = Hare()
hare.brain = brain
convo = Conversation()
convo.add_utterance(speaker='a', content='hate you')
convo.add_utterance(speaker='b', content='i love you')
convo.label_speaker('a', 1)
hare.add_conversation(convo)
hare.train()
hare.save('/vol/tensusers2/wstoop/HaRe/hare/pretrained/simple')
hare.update_status_history_for_conversation()
hare.visualize_history_for_conversation()
current_conversation = Conversation()
if len(conversations) % 100 == 0:
print(len(conversations))
if len(conversations) == NR_OF_CONVERSATIONS:
break
continue
speaker, content = line.split('\t')
current_conversation.add_utterance(speaker, content)
#Add to a hare object
for downsample_ratio in DOWNSAMPLE_RATIOS:
for training_size in TRAINING_SIZES:
print('===', 'training', downsample_ratio, training_size, '===')
exp_hare = Hare()
for conversation in conversations[:training_size]:
exp_hare.add_conversation(conversation)
exp_hare.brain = BiGruBrain()
exp_hare.brain.downsampling = True
exp_hare.brain.downsampling_ratio = downsample_ratio
exp_hare.brain._max_sequence_length = 500
exp_hare.train()
exp_hare.save('moba_' + str(downsample_ratio) + '_' +
str(training_size))