def test_add_talk_to_speaker(self):
s = Speaker(name = 'Jimi Hendrix',
slug = 'jimi-hendrix',
url = 'http://jimi.hendrix.com')
s.save()
t = Talk(title = 'First Talk',
start_time = '15:00')
t.save()
s.talk_set.add(t)
self.assertQuerysetEqual(s.talk_set.all(),
['<Talk: First Talk>'])
self.assertQuerysetEqual(t.speakers.all(),
['<Speaker: Jimi Hendrix>'])
def test_create_speaker(self):
s = Speaker(name = 'Bob Marley',
slug = 'bob-marley',
url = 'http://bobmarley.com')
s.save()
ce = Contact(kind='E', value='*****@*****.**')
cf = Contact(kind='F', value='11-5555-9999')
cp = Contact(kind='P', value='11-5555-8888')
s.contact_set.add(ce)
s.contact_set.add(cf)
s.contact_set.add(cp)
self.assertQuerysetEqual(s.contact_set.all().order_by(),
['<Contact: E, [email protected]>',
'<Contact: F, 11-5555-9999>',
'<Contact: P, 11-5555-8888>'])
def post(self, id = None): request = self.wsgi_request response = self.wsgi_response c = self.context param_dict = dict( first_name = request.params.first_name, last_name = request.params.last_name, desc = request.params.desc, twitter_handle = request.params.twitter_handle) content = param_dict speaker = Speaker(**param_dict) speaker = speaker.save() self.set_body(speaker.to_json()) response.headers['content-type'] = 'application/json' return self.render()
class Game_Master():
def __init__(self,
target_size,
vocab_size,
bias,
test_data,
seed,
episodes=1000):
self.data = test_data
self.vocab_size = vocab_size
self.target_size = target_size
self.speaker_bias = 1.0 - bias
self.listener_bias = bias
self.speaker = Speaker(512, self.target_size, self.vocab_size,
self.speaker_bias, seed)
self.listener = Listener(512, self.vocab_size, self.target_size,
self.listener_bias, seed)
self.episode_rewards = []
self.accuracy_history = deque(maxlen=100)
self.symbols_used = {}
self.synonym_array = np.zeros((target_size, vocab_size))
self.accuracy_record = []
self.episodes = 1000
def create_labels(self, num_labels, num_repeats):
arr = np.array([])
for i in range(num_labels):
n = np.repeat(i, num_repeats)
arr = np.concatenate((arr, n))
np.random.shuffle(arr)
return arr
def sample(self, label, test_or_train="X_train"):
train = self.data[test_or_train]
size = train[0].shape[0]
target_one_hot = to_categorical(label, num_classes=self.target_size)
target_image = 0
image_list = []
for i in range(self.target_size):
sampled_image = np.expand_dims(train[i][np.random.choice(size), :],
0)
if i == label:
target_image = sampled_image
image_list.append(sampled_image)
return target_image, image_list, target_one_hot
def cross_entropy(self, predictions, targets, epsilon=1e-12):
predictions = np.clip(predictions, epsilon, 1. - epsilon)
N = predictions.shape[0]
ce = -np.sum(np.sum(targets * np.log(predictions + 1e-9))) / N
return ce
def test(self):
y = self.create_labels(10, 1)
one_hot = to_categorical(y)
it = 0
while it < y.shape[0]:
label = y[it]
target_image, image_list, target_one_hot = self.sample(
label, "X_test")
speaker_action, speaker_probs = self.speaker.act(target_image)
speaker_action_class = np.expand_dims(
to_categorical(speaker_action, num_classes=self.vocab_size), 0)
image_list.append(speaker_action_class)
listener_action, listener_probs = self.listener.act(image_list)
if listener_action == label:
speaker_reward = 1.0
listener_reward = 1.0
else:
speaker_reward = 0.0
listener_reward = 0.0
self.synonym_array[int(label)][int(speaker_action)] += 1
it += 1
def play(self):
y = self.create_labels(10, 100)
one_hot = to_categorical(y)
bar = tqdm(np.arange(self.episodes))
for i in bar:
it = 0
acc = np.zeros(self.target_size)
right = 0
while it < y.shape[0]:
label = y[it]
target_image, image_list, target_one_hot = self.sample(label)
speaker_action, speaker_probs = self.speaker.act(target_image)
speaker_action_class = np.expand_dims(
to_categorical(speaker_action,
num_classes=self.vocab_size), 0)
image_list.append(speaker_action_class)
listener_action, listener_probs = self.listener.act(image_list)
if listener_action == label:
speaker_reward = 1.0
listener_reward = 1.0
right += 1
else:
speaker_reward = 0.0
listener_reward = 0.0
acc = np.vstack((acc, listener_probs))
self.speaker.save(target_image, speaker_action, speaker_probs,
speaker_reward)
self.listener.save(image_list, speaker_action_class,
listener_action, listener_probs,
listener_reward)
it += 1
accuracy = right / y.shape[0]
total = np.sum(self.speaker.params["rewards"])
cross = self.cross_entropy(acc[1:, :], one_hot)
self.episode_rewards.append(total)
self.accuracy_history.append(accuracy)
self.accuracy_record.append(accuracy)
bar.set_description("Cross Entropy: " + str(cross) +
", Rolling Acc 100: " +
str(np.mean(self.accuracy_history)) +
", Accuracy: " + str(accuracy))
self.speaker.train()
self.listener.train()
self.test()
class Game_Master():
def __init__(self,target_size,vocab_size,bias,test_data,seed,episodes=1000):
self.data = test_data
self.vocab_size = vocab_size
self.target_size = target_size
self.speaker_bias = 1.0 - bias
self.listener_bias = bias
self.speaker = Speaker(512,self.target_size,self.vocab_size,self.speaker_bias,seed)
self.listener = Listener(512,self.vocab_size,self.target_size,self.listener_bias,seed)
self.episode_rewards = []
self.accuracy_history = deque(maxlen=100)
self.symbols_used = {}
self.synonym_array = np.zeros((target_size,vocab_size))
self.accuracy_record = []
self.episodes = 1000
def create_labels(self,num_labels,num_repeats):
arr = np.array([])
for i in range(num_labels):
n = np.repeat(i,num_repeats)
arr = np.concatenate((arr,n))
np.random.shuffle(arr)
return arr
def sample(self,label,test_or_train="X_train"):
train = self.data[test_or_train]
size = train[0].shape[0]
target_one_hot = to_categorical(label,num_classes=self.target_size)
target_image = 0
image_list = []
for i in range(self.target_size):
sampled_image = np.expand_dims(train[i][np.random.choice(size),:],0)
if i == label:
target_image = sampled_image
image_list.append(sampled_image)
return target_image, image_list, target_one_hot
def cross_entropy(self,predictions, targets, epsilon=1e-12):
predictions = np.clip(predictions, epsilon, 1. - epsilon)
N = predictions.shape[0]
ce = -np.sum(np.sum(targets*np.log(predictions+1e-9)))/N
return ce
def test(self):
y = self.create_labels(10,1)
one_hot = to_categorical(y)
it = 0
while it < y.shape[0]:
label = y[it]
target_image, image_list, target_one_hot = self.sample(label,"X_test")
speaker_action, speaker_probs = self.speaker.act(target_image)
speaker_action_class = np.expand_dims(to_categorical(speaker_action,num_classes=self.vocab_size),0)
image_list.append(speaker_action_class)
listener_action, listener_probs = self.listener.act(image_list)
if listener_action == label:
speaker_reward = 1.0
listener_reward = 1.0
else:
speaker_reward = 0.0
listener_reward = 0.0
self.synonym_array[int(label)][int(speaker_action)] += 1
it += 1
def play(self):
y = self.create_labels(10,100)
one_hot = to_categorical(y)
bar = tqdm(np.arange(self.episodes))
for i in bar:
it = 0
acc = np.zeros(self.target_size)
right = 0
while it < y.shape[0]:
label = y[it]
target_image, image_list, target_one_hot = self.sample(label)
speaker_action, speaker_probs = self.speaker.act(target_image)
speaker_action_class = np.expand_dims(to_categorical(speaker_action,num_classes=self.vocab_size),0)
image_list.append(speaker_action_class)
listener_action, listener_probs = self.listener.act(image_list)
if listener_action == label:
speaker_reward = 1.0
listener_reward = 1.0
right += 1
else:
speaker_reward = 0.0
listener_reward = 0.0
acc = np.vstack((acc,listener_probs))
self.speaker.save(target_image,speaker_action,speaker_probs,speaker_reward)
self.listener.save(image_list,speaker_action_class,listener_action,listener_probs,listener_reward)
it += 1
accuracy = right/y.shape[0]
total = np.sum(self.speaker.params["rewards"])
cross = self.cross_entropy(acc[1:,:],one_hot)
self.episode_rewards.append(total)
self.accuracy_history.append(accuracy)
self.accuracy_record.append(accuracy)
bar.set_description("Cross Entropy: " + str(cross) + ", Rolling Acc 100: " + str(np.mean(self.accuracy_history)) + ", Accuracy: " + str(accuracy))
self.speaker.train()
self.listener.train()
self.test()
