class UnigramLMSpeakerLearner(Learner):
def __init__(self):
options = config.options()
self.tokenizer = options.speaker_tokenizer
self.token_counts = Counter()
self.seq_vec = SequenceVectorizer(
unk_threshold=options.speaker_unk_threshold)
self.num_tokens = 0
def train(self,
training_instances,
validation_instances='ignored',
metrics='ignored'):
tokenize = TOKENIZERS[self.tokenizer]
tokenized = [
tokenize(inst.output) + ['</s>'] for inst in training_instances
]
self.seq_vec.add_all(tokenized)
unk_replaced = self.seq_vec.unk_replace_all(tokenized)
progress.start_task('Example', len(training_instances))
for i, utt in enumerate(unk_replaced):
progress.progress(i)
self.token_counts.update(utt)
self.num_tokens += len(utt)
progress.end_task()
@property
def num_params(self):
return len(self.token_counts)
def predict_and_score(self, eval_instances):
predict = [''] * len(eval_instances)
score = []
progress.start_task('Example', len(eval_instances))
for i, inst in enumerate(eval_instances):
progress.progress(i)
score.append(self._get_log_prob(inst.output))
progress.end_task()
return predict, score
def _get_log_prob(self, output):
tokenize = TOKENIZERS[self.tokenizer]
tokenized = tokenize(output) + ['</s>']
unk_replaced = self.seq_vec.unk_replace(tokenized)
log_prob = 0.0
for token in unk_replaced:
log_prob += np.log(self.token_counts[token] * 1.0 /
self.num_tokens)
return log_prob
class UnigramLMSpeakerLearner(Learner):
def __init__(self):
options = config.options()
self.tokenizer = options.speaker_tokenizer
self.token_counts = Counter()
self.seq_vec = SequenceVectorizer(unk_threshold=options.speaker_unk_threshold)
self.num_tokens = 0
def train(self, training_instances, validation_instances='ignored', metrics='ignored'):
tokenize = TOKENIZERS[self.tokenizer]
tokenized = [tokenize(inst.output) + ['</s>'] for inst in training_instances]
self.seq_vec.add_all(tokenized)
unk_replaced = self.seq_vec.unk_replace_all(tokenized)
progress.start_task('Example', len(training_instances))
for i, utt in enumerate(unk_replaced):
progress.progress(i)
self.token_counts.update(utt)
self.num_tokens += len(utt)
progress.end_task()
@property
def num_params(self):
return len(self.token_counts)
def predict_and_score(self, eval_instances):
predict = [''] * len(eval_instances)
score = []
progress.start_task('Example', len(eval_instances))
for i, inst in enumerate(eval_instances):
progress.progress(i)
score.append(self._get_log_prob(inst.output))
progress.end_task()
return predict, score
def _get_log_prob(self, output):
tokenize = TOKENIZERS[self.tokenizer]
tokenized = tokenize(output) + ['</s>']
unk_replaced = self.seq_vec.unk_replace(tokenized)
log_prob = 0.0
for token in unk_replaced:
log_prob += np.log(self.token_counts[token] * 1.0 / self.num_tokens)
return log_prob
class LRContextListenerLearner(Learner):
def train(self,
training_instances,
validation_instances=None,
metrics=None):
X, y = self._data_to_arrays(training_instances, init_vectorizer=True)
self.mod = LogisticRegression(solver='lbfgs')
self.mod.fit(X, y)
@property
def num_params(self):
return np.prod(self.mod.coef_.shape) + np.prod(
self.mod.intercept_.shape)
def predict_and_score(self, eval_instances, random=False, verbosity=0):
X, y = self._data_to_arrays(eval_instances)
y = y.reshape((len(eval_instances), self.context_len))
all_scores = self.mod.predict_log_proba(X)[:, 1].reshape(
(len(eval_instances), self.context_len))
all_scores -= logsumexp(all_scores, axis=1)[:, np.newaxis]
preds = all_scores.argmax(axis=1)
scores = np.where(y, all_scores, 0).sum(axis=1)
return preds.tolist(), scores.tolist()
def _data_to_arrays(self,
instances,
inverted=False,
init_vectorizer=False):
self.get_options()
get_i, get_o = (lambda inst: inst.input), (lambda inst: inst.output)
get_desc, get_color = (get_o, get_i) if inverted else (get_i, get_o)
get_alt_i, get_alt_o = (lambda inst: inst.alt_inputs), (
lambda inst: inst.alt_outputs)
get_alt_colors = get_alt_i if inverted else get_alt_o
tokenize = TOKENIZERS[self.options.listener_tokenizer]
tokenized = [tokenize(get_desc(inst)) for inst in instances]
context_lens = [len(get_alt_colors(inst)) for inst in instances]
if init_vectorizer:
self.seq_vec = SequenceVectorizer()
self.seq_vec.add_all(tokenized)
unk_replaced = self.seq_vec.unk_replace_all(tokenized)
if init_vectorizer:
config.dump(unk_replaced, 'unk_replaced.train.jsons', lines=True)
self.context_len = context_lens[0]
color_repr = COLOR_REPRS[self.options.listener_color_repr]
self.color_vec = color_repr(self.options.listener_color_resolution,
hsv=self.options.listener_hsv)
assert all(cl == self.context_len
for cl in context_lens), (self.context_len, context_lens)
padded = [(d + ['</s>'] *
(self.seq_vec.max_len - len(d)))[:self.seq_vec.max_len]
for d in unk_replaced]
colors = [c for inst in instances for c in get_alt_colors(inst)]
labels = np.array([
int(i == get_color(inst)) for inst in instances
for i in range(self.context_len)
])
desc_indices = self.seq_vec.vectorize_all(padded)
desc_bow = -np.ones((desc_indices.shape[0], self.seq_vec.num_types))
desc_bow[np.arange(desc_indices.shape[0])[:, np.newaxis],
desc_indices] = 1.
color_feats = self.color_vec.vectorize_all(colors)
color_feats = color_feats.reshape(
(desc_indices.shape[0], self.context_len, color_feats.shape[1]))
feats = np.einsum('ij,ick->icjk', desc_bow, color_feats)
feats = feats.reshape((desc_indices.shape[0] * self.context_len,
desc_bow.shape[1] * color_feats.shape[2]))
return feats, labels
def get_options(self):
if not hasattr(self, 'options'):
self.options = config.options()
class SpeakerLearner(NeuralLearner):
'''
An speaker with a feedforward neural net color input passed into an RNN
to generate a description.
'''
def __init__(self, id=None, context_len=1):
super(SpeakerLearner, self).__init__(id=id)
self.seq_vec = SequenceVectorizer(
unk_threshold=self.options.speaker_unk_threshold)
color_repr = COLOR_REPRS[self.options.speaker_color_repr]
self.color_vec = color_repr(self.options.speaker_color_resolution,
hsv=self.options.speaker_hsv)
self.context_len = context_len
@property
def use_color_mask(self):
return False
def predict(self, eval_instances, random=False, verbosity=0):
result = []
batches = iterators.iter_batches(eval_instances,
self.options.speaker_eval_batch_size)
num_batches = (len(eval_instances) -
1) // self.options.speaker_eval_batch_size + 1
eos_index = self.seq_vec.vectorize(['</s>'])[0]
if self.options.verbosity + verbosity >= 2:
print('Predicting')
if self.options.verbosity + verbosity >= 1:
progress.start_task('Predict batch', num_batches)
for batch_num, batch in enumerate(batches):
if self.options.verbosity + verbosity >= 1:
progress.progress(batch_num)
batch = list(batch)
if self.use_color_mask:
(c, cm, _p, mask), (_y, ) = self._data_to_arrays(batch,
test=True)
else:
(c, _p, mask), (_y, ) = self._data_to_arrays(batch, test=True)
assert mask.all() # We shouldn't be masking anything in prediction
beam_size = 1 if random else self.options.speaker_beam_size
done = np.zeros((len(batch), beam_size), dtype=np.bool)
beam = np.zeros((len(batch), beam_size, self.seq_vec.max_len),
dtype=np.int32)
beam[:, :, 0] = self.seq_vec.vectorize(['<s>'])[0]
beam_scores = np.log(np.zeros((len(batch), beam_size)))
beam_scores[:, 0] = 0.0
c = np.repeat(c, beam_size, axis=0)
mask = np.repeat(mask, beam_size, axis=0)
if self.use_color_mask:
cm = np.repeat(cm, beam_size, axis=0)
for length in range(1, self.seq_vec.max_len):
if done.all():
break
p = beam.reshape(
(beam.shape[0] * beam.shape[1], beam.shape[2]))[:, :-1]
inputs = [c, cm, p, mask
] if self.use_color_mask else [c, p, mask]
probs = self.model.predict(inputs)
if random:
indices = sample(probs[:, length - 1, :])
beam[:, 0, length] = indices
done = np.logical_or(done, indices == eos_index)
else:
assert probs.shape[1] == p.shape[1], (probs.shape[1],
p.shape[1])
assert probs.shape[2] == len(
self.seq_vec.tokens), (probs.shape[2],
len(self.seq_vec.tokens))
scores = np.log(probs)[:, length - 1, :].reshape(
(beam.shape[0], beam.shape[1], probs.shape[2]))
beam_search_step(scores, length, beam, beam_scores, done,
eos_index)
outputs = self.seq_vec.unvectorize_all(beam[:, 0, :])
result.extend([' '.join(strip_invalid_tokens(o)) for o in outputs])
if self.options.verbosity + verbosity >= 1:
progress.end_task()
return result
def score(self, eval_instances, verbosity=0):
result = []
batches = iterators.iter_batches(eval_instances,
self.options.speaker_eval_batch_size)
num_batches = (len(eval_instances) -
1) // self.options.speaker_eval_batch_size + 1
if self.options.verbosity + verbosity >= 2:
print('Scoring')
if self.options.verbosity + verbosity >= 1:
progress.start_task('Score batch', num_batches)
for batch_num, batch in enumerate(batches):
if self.options.verbosity + verbosity >= 1:
progress.progress(batch_num)
batch = list(batch)
xs, (n, ) = self._data_to_arrays(batch, test=False)
if self.use_color_mask:
mask = xs[3]
else:
mask = xs[2]
probs = self.model.predict(xs)
token_probs = probs[np.arange(probs.shape[0])[:, np.newaxis],
np.arange(probs.shape[1]), n]
scores_arr = np.sum(np.log(token_probs) * mask, axis=1)
scores = scores_arr.tolist()
result.extend(scores)
if self.options.verbosity + verbosity >= 1:
progress.end_task()
return result
def _data_to_arrays(self,
training_instances,
init_vectorizer=False,
test=False,
inverted=False):
context_len = self.context_len if hasattr(self, 'context_len') else 1
use_context = context_len > 1
def get_multi(val):
if isinstance(val, tuple):
assert len(val) == 1
return val[0]
else:
return val
get_i, get_o = (lambda inst: inst.input), (lambda inst: inst.output)
get_color, get_desc_simple = (get_o, get_i) if inverted else (get_i,
get_o)
get_desc = lambda inst: get_multi(get_desc_simple(inst))
get_i_ind, get_o_ind = (
(lambda inst: inst.alt_inputs[get_multi(inst.input)]),
(lambda inst: inst.alt_outputs[get_multi(inst.output)]))
get_color_indexed = get_o_ind if inverted else get_i_ind
get_alt_i, get_alt_o = (lambda inst: inst.alt_inputs), (
lambda inst: inst.alt_outputs)
get_alt_colors = get_alt_o if inverted else get_alt_i
if hasattr(self.options, 'speaker_tokenizer'):
tokenize = TOKENIZERS[self.options.speaker_tokenizer]
else:
tokenize = TOKENIZERS['whitespace']
if init_vectorizer:
tokenized = [['<s>'] + tokenize(get_desc(inst)) + ['</s>']
for inst in training_instances]
self.seq_vec.add_all(tokenized)
unk_replaced = self.seq_vec.unk_replace_all(tokenized)
config.dump(unk_replaced, 'unk_replaced.train.jsons', lines=True)
colors = []
previous = []
next_tokens = []
if self.options.verbosity >= 9:
print('%s _data_to_arrays:' % self.id)
for i, inst in enumerate(training_instances):
desc, color = get_desc(inst), get_color(inst)
if isinstance(color, numbers.Number):
color = get_color_indexed(inst)
if test:
full = ['<s>'] + ['</s>'] * (self.seq_vec.max_len - 1)
else:
desc = tokenize(desc)
full = (['<s>'] + desc + ['</s>'] + ['<MASK>'] *
(self.seq_vec.max_len - 1 - len(desc)))
prev = full[:-1]
next = full[1:]
if self.options.verbosity >= 9:
print('%s, %s -> %s' % (repr(color), repr(prev), repr(next)))
colors.append(color)
if use_context:
new_context = get_alt_colors(inst)
index = get_color(inst)
if isinstance(index, tuple):
assert len(index) == 1
index = index[0]
assert len(new_context) == context_len, \
'Inconsistent context lengths: %s' % ((context_len, len(new_context)),)
colors.extend(
[c for j, c in enumerate(new_context) if j != index])
previous.append(prev)
next_tokens.append(next)
P = np.zeros((len(previous), self.seq_vec.max_len - 1), dtype=np.int32)
mask = np.zeros((len(previous), self.seq_vec.max_len - 1),
dtype=np.int32)
N = np.zeros((len(next_tokens), self.seq_vec.max_len - 1),
dtype=np.int32)
c = self.color_vec.vectorize_all(colors, hsv=True)
if len(c.shape) == 1:
c = c.reshape((len(colors) / context_len, context_len))
else:
c = c.reshape((len(colors) / context_len,
context_len * c.shape[1]) + c.shape[2:])
for i, (color, prev,
next) in enumerate(zip(colors, previous, next_tokens)):
if len(prev) > P.shape[1]:
prev = prev[:P.shape[1]]
if len(next) > N.shape[1]:
next = next[:N.shape[1]]
P[i, :len(prev)] = self.seq_vec.vectorize(prev)
N[i, :len(next)] = self.seq_vec.vectorize(next)
for t, token in enumerate(next):
mask[i, t] = (token != '<MASK>')
c = np.tile(c[:, np.newaxis, ...],
[1, self.seq_vec.max_len - 1] + [1] * (c.ndim - 1))
if self.options.verbosity >= 9:
print('c: %s' % (repr(c), ))
print('P: %s' % (repr(P), ))
print('mask: %s' % (repr(mask), ))
print('N: %s' % (repr(N), ))
return [c, P, mask], [N]
def _build_model(self, model_class=SimpleLasagneModel):
id_tag = (self.id + '/') if self.id else ''
input_vars = self.color_vec.get_input_vars(
self.id, recurrent=not self.use_color_mask)
if self.use_color_mask:
input_vars.append(T.imatrix(id_tag + 'color_mask'))
input_vars.extend(
[T.imatrix(id_tag + 'previous'),
T.imatrix(id_tag + 'mask')])
target_var = T.imatrix(id_tag + 'targets')
self.l_out, self.input_layers = self._get_l_out(input_vars)
self.model = model_class(
input_vars, [target_var],
self.l_out,
id=self.id,
loss=self.masked_loss(input_vars),
optimizer=OPTIMIZERS[self.options.speaker_optimizer],
learning_rate=self.options.speaker_learning_rate)
def train_priors(self, training_instances, listener_data=False):
prior_class = PRIORS[self.options.speaker_prior]
self.prior_emp = prior_class(recurrent=True)
self.prior_smooth = prior_class(recurrent=True)
self.prior_emp.train(training_instances, listener_data=listener_data)
self.prior_smooth.train(training_instances,
listener_data=listener_data)
def _get_l_out(self, input_vars):
check_options(self.options)
id_tag = (self.id + '/') if self.id else ''
prev_output_var, mask_var = input_vars[-2:]
color_input_vars = input_vars[:-2]
context_len = self.context_len if hasattr(self, 'context_len') else 1
l_color_repr, color_inputs = self.color_vec.get_input_layer(
color_input_vars,
recurrent_length=self.seq_vec.max_len - 1,
cell_size=self.options.speaker_cell_size,
context_len=context_len,
id=self.id)
l_hidden_color = dimshuffle(l_color_repr, (0, 2, 1))
for i in range(1, self.options.speaker_hidden_color_layers + 1):
l_hidden_color = NINLayer(
l_hidden_color,
num_units=self.options.speaker_cell_size,
nonlinearity=NONLINEARITIES[self.options.speaker_nonlinearity],
name=id_tag + 'hidden_color%d' % i)
l_hidden_color = dimshuffle(l_hidden_color, (0, 2, 1))
l_prev_out = InputLayer(shape=(None, self.seq_vec.max_len - 1),
input_var=prev_output_var,
name=id_tag + 'prev_input')
l_prev_embed = EmbeddingLayer(
l_prev_out,
input_size=len(self.seq_vec.tokens),
output_size=self.options.speaker_cell_size,
name=id_tag + 'prev_embed')
l_in = ConcatLayer([l_hidden_color, l_prev_embed],
axis=2,
name=id_tag + 'color_prev')
l_mask_in = InputLayer(shape=(None, self.seq_vec.max_len - 1),
input_var=mask_var,
name=id_tag + 'mask_input')
l_rec_drop = l_in
cell = CELLS[self.options.speaker_cell]
cell_kwargs = {
'mask_input':
(None if self.options.speaker_no_mask else l_mask_in),
'grad_clipping': self.options.speaker_grad_clipping,
'num_units': self.options.speaker_cell_size,
}
if self.options.speaker_cell == 'LSTM':
cell_kwargs['forgetgate'] = Gate(
b=Constant(self.options.speaker_forget_bias))
if self.options.speaker_cell != 'GRU':
cell_kwargs['nonlinearity'] = NONLINEARITIES[
self.options.speaker_nonlinearity]
for i in range(1, self.options.speaker_recurrent_layers):
l_rec = cell(l_rec_drop, name=id_tag + 'rec%d' % i, **cell_kwargs)
if self.options.speaker_dropout > 0.0:
l_rec_drop = DropoutLayer(l_rec,
p=self.options.speaker_dropout,
name=id_tag + 'rec%d_drop' % i)
else:
l_rec_drop = l_rec
l_rec = cell(l_rec_drop,
name=id_tag +
'rec%d' % self.options.speaker_recurrent_layers,
**cell_kwargs)
l_shape = ReshapeLayer(l_rec, (-1, self.options.speaker_cell_size),
name=id_tag + 'reshape')
l_hidden_out = l_shape
for i in range(1, self.options.speaker_hidden_out_layers + 1):
l_hidden_out = DenseLayer(
l_hidden_out,
num_units=self.options.speaker_cell_size,
nonlinearity=NONLINEARITIES[self.options.speaker_nonlinearity],
name=id_tag + 'hidden_out%d' % i)
l_softmax = DenseLayer(l_hidden_out,
num_units=len(self.seq_vec.tokens),
nonlinearity=softmax,
name=id_tag + 'softmax')
l_out = ReshapeLayer(
l_softmax,
(-1, self.seq_vec.max_len - 1, len(self.seq_vec.tokens)),
name=id_tag + 'out')
return l_out, color_inputs + [l_prev_out, l_mask_in]
def loss_out(self, input_vars=None, target_var=None):
if input_vars is None:
input_vars = self.model.input_vars
if target_var is None:
target_var = self.model.target_var
pred = get_output(self.l_out, dict(zip(self.input_layers, input_vars)))
loss = self.masked_loss(input_vars)
return loss(pred, target_var)
def masked_loss(self, input_vars):
return masked_seq_crossentropy(input_vars[-1])
def sample_prior_smooth(self, num_samples):
return self.prior_smooth.sample(num_samples)
class ListenerLearner(NeuralLearner):
'''
An LSTM-based listener (guesses colors from descriptions).
'''
def __init__(self, id=None):
super(ListenerLearner, self).__init__(id=id)
self.word_counts = Counter()
self.seq_vec = SequenceVectorizer(
unk_threshold=self.options.listener_unk_threshold)
self.color_vec = BucketsVectorizer(
self.options.listener_color_resolution,
hsv=self.options.listener_hsv)
def predict_and_score(self, eval_instances, random=False, verbosity=0):
predictions = []
scores = []
batches = iterators.iter_batches(eval_instances,
self.options.listener_eval_batch_size)
num_batches = (len(eval_instances) -
1) // self.options.listener_eval_batch_size + 1
if self.options.verbosity + verbosity >= 2:
print('Testing')
progress.start_task('Eval batch', num_batches)
for batch_num, batch in enumerate(batches):
progress.progress(batch_num)
batch = list(batch)
xs, (y, ) = self._data_to_arrays(batch, test=True)
probs = self.model.predict(xs)
if random:
indices = sample(probs)
predictions.extend(self.unvectorize(indices, random=True))
else:
predictions.extend(self.unvectorize(probs.argmax(axis=1)))
scores_arr = np.log(probs[np.arange(len(batch)),
y]) + self.bucket_adjustment()
scores.extend(scores_arr.tolist())
progress.end_task()
if self.options.verbosity >= 9:
print('%s %ss:') % (self.id, 'sample' if random else 'prediction')
for inst, prediction in zip(eval_instances, predictions):
print('%s -> %s' % (repr(inst.input), repr(prediction)))
return predictions, scores
def unvectorize(self, indices, random=False):
return self.color_vec.unvectorize_all(indices, random=random, hsv=True)
def bucket_adjustment(self):
bucket_volume = (256.0**3) / self.color_vec.num_types
return -np.log(bucket_volume)
def on_iter_end(self, step, writer):
most_common = [
desc for desc, count in self.word_counts.most_common(10)
]
insts = [instance.Instance(input=desc) for desc in most_common]
xs, (y, ) = self._data_to_arrays(insts, test=True)
probs = self.model.predict(xs)
for i, desc in enumerate(most_common):
dist = probs[i, :]
for image, channel in zip(
self.color_vec.visualize_distribution(dist), '012'):
writer.log_image(step, '%s/%s/%s' % (self.id, desc, channel),
image)
super(ListenerLearner, self).on_iter_end(step, writer)
def _data_to_arrays(self,
training_instances,
init_vectorizer=False,
test=False,
inverted=False):
def get_multi(val):
if isinstance(val, tuple):
assert len(val) == 1
return val[0]
else:
return val
get_i, get_o = (lambda inst: inst.input), (lambda inst: inst.output)
get_desc, get_color = (get_o, get_i) if inverted else (get_i, get_o)
get_i_ind, get_o_ind = (
(lambda inst: inst.alt_inputs[get_multi(inst.input)]),
(lambda inst: inst.alt_outputs[get_multi(inst.output)]))
get_color_indexed = get_i_ind if inverted else get_o_ind
if hasattr(self.options, 'listener_tokenizer'):
tokenize = TOKENIZERS[self.options.listener_tokenizer]
else:
tokenize = TOKENIZERS['whitespace']
if init_vectorizer:
tokenized = [['<s>'] + tokenize(get_desc(inst)) + ['</s>']
for inst in training_instances]
self.seq_vec.add_all(tokenized)
unk_replaced = self.seq_vec.unk_replace_all(tokenized)
self.word_counts.update(
[get_desc(inst) for inst in training_instances])
config.dump(unk_replaced, 'unk_replaced.train.jsons', lines=True)
sentences = []
colors = []
if self.options.verbosity >= 9:
print('%s _data_to_arrays:' % self.id)
for i, inst in enumerate(training_instances):
desc = tokenize(get_desc(inst))
color = get_color(inst)
if isinstance(color, numbers.Number):
color = get_color_indexed(inst)
if not color:
assert test
color = (0.0, 0.0, 0.0)
s = ['<s>'] * (self.seq_vec.max_len - 1 - len(desc)) + desc
s.append('</s>')
if self.options.verbosity >= 9:
print('%s -> %s' % (repr(s), repr(color)))
sentences.append(s)
colors.append(color)
x = np.zeros((len(sentences), self.seq_vec.max_len), dtype=np.int32)
y = np.zeros((len(sentences), ), dtype=np.int32)
for i, sentence in enumerate(sentences):
if len(sentence) > x.shape[1]:
sentence = sentence[:x.shape[1]]
x[i, :] = self.seq_vec.vectorize(sentence)
y[i] = self.color_vec.vectorize(colors[i], hsv=True)
return [x], [y]
def _build_model(self, model_class=SimpleLasagneModel):
id_tag = (self.id + '/') if self.id else ''
input_var = T.imatrix(id_tag + 'inputs')
target_var = T.ivector(id_tag + 'targets')
self.l_out, self.input_layers = self._get_l_out([input_var])
self.loss = categorical_crossentropy
self.model = model_class(
[input_var], [target_var],
self.l_out,
loss=self.loss,
optimizer=OPTIMIZERS[self.options.listener_optimizer],
learning_rate=self.options.listener_learning_rate,
id=self.id)
def train_priors(self, training_instances, listener_data=False):
prior_class = PRIORS[self.options.listener_prior]
self.prior_emp = prior_class(
) # TODO: accurate values for empirical prior
self.prior_smooth = prior_class()
self.prior_emp.train(training_instances, listener_data=listener_data)
self.prior_smooth.train(training_instances,
listener_data=listener_data)
def _get_l_out(self, input_vars):
check_options(self.options)
id_tag = (self.id + '/') if self.id else ''
input_var = input_vars[0]
l_in = InputLayer(shape=(None, self.seq_vec.max_len),
input_var=input_var,
name=id_tag + 'desc_input')
l_in_embed = EmbeddingLayer(
l_in,
input_size=len(self.seq_vec.tokens),
output_size=self.options.listener_cell_size,
name=id_tag + 'desc_embed')
cell = CELLS[self.options.listener_cell]
cell_kwargs = {
'grad_clipping': self.options.listener_grad_clipping,
'num_units': self.options.listener_cell_size,
}
if self.options.listener_cell == 'LSTM':
cell_kwargs['forgetgate'] = Gate(
b=Constant(self.options.listener_forget_bias))
if self.options.listener_cell != 'GRU':
cell_kwargs['nonlinearity'] = NONLINEARITIES[
self.options.listener_nonlinearity]
l_rec1 = cell(l_in_embed, name=id_tag + 'rec1', **cell_kwargs)
if self.options.listener_dropout > 0.0:
l_rec1_drop = DropoutLayer(l_rec1,
p=self.options.listener_dropout,
name=id_tag + 'rec1_drop')
else:
l_rec1_drop = l_rec1
l_rec2 = cell(l_rec1_drop, name=id_tag + 'rec2', **cell_kwargs)
if self.options.listener_dropout > 0.0:
l_rec2_drop = DropoutLayer(l_rec2,
p=self.options.listener_dropout,
name=id_tag + 'rec2_drop')
else:
l_rec2_drop = l_rec2
l_hidden = DenseLayer(
l_rec2_drop,
num_units=self.options.listener_cell_size,
nonlinearity=NONLINEARITIES[self.options.listener_nonlinearity],
name=id_tag + 'hidden')
if self.options.listener_dropout > 0.0:
l_hidden_drop = DropoutLayer(l_hidden,
p=self.options.listener_dropout,
name=id_tag + 'hidden_drop')
else:
l_hidden_drop = l_hidden
l_scores = DenseLayer(l_hidden_drop,
num_units=self.color_vec.num_types,
nonlinearity=None,
name=id_tag + 'scores')
l_out = NonlinearityLayer(l_scores,
nonlinearity=softmax,
name=id_tag + 'out')
return l_out, [l_in]
def sample_prior_smooth(self, num_samples):
return self.prior_smooth.sample(num_samples)
class ListenerLearner(NeuralLearner):
'''
An LSTM-based listener (guesses colors from descriptions).
'''
def __init__(self, id=None):
super(ListenerLearner, self).__init__(id=id)
self.word_counts = Counter()
self.seq_vec = SequenceVectorizer(unk_threshold=self.options.listener_unk_threshold)
self.color_vec = BucketsVectorizer(self.options.listener_color_resolution,
hsv=self.options.listener_hsv)
def predict_and_score(self, eval_instances, random=False, verbosity=0):
predictions = []
scores = []
batches = iterators.iter_batches(eval_instances, self.options.listener_eval_batch_size)
num_batches = (len(eval_instances) - 1) // self.options.listener_eval_batch_size + 1
if self.options.verbosity + verbosity >= 2:
print('Testing')
progress.start_task('Eval batch', num_batches)
for batch_num, batch in enumerate(batches):
progress.progress(batch_num)
batch = list(batch)
xs, (y,) = self._data_to_arrays(batch, test=True)
probs = self.model.predict(xs)
self.on_predict(xs)
if random:
indices = sample(probs)
predictions.extend(self.unvectorize(indices, random=True))
else:
predictions.extend(self.unvectorize(probs.argmax(axis=1)))
scores_arr = np.log(probs[np.arange(len(batch)), y]) + self.bucket_adjustment()
scores.extend(scores_arr.tolist())
progress.end_task()
if self.options.verbosity >= 9:
print('%s %ss:') % (self.id, 'sample' if random else 'prediction')
for inst, prediction in zip(eval_instances, predictions):
print('%s -> %s' % (repr(inst.input), repr(prediction)))
return predictions, scores
def unvectorize(self, indices, random=False):
return self.color_vec.unvectorize_all(indices, random=random, hsv=True)
def bucket_adjustment(self):
bucket_volume = (256.0 ** 3) / self.color_vec.num_types
return -np.log(bucket_volume)
def on_predict(self, xs):
pass
def on_iter_end(self, step, writer):
most_common = [desc for desc, count in self.word_counts.most_common(10)]
insts = [instance.Instance(input=desc) for desc in most_common]
xs, (y,) = self._data_to_arrays(insts, test=True)
probs = self.model.predict(xs)
for i, desc in enumerate(most_common):
dist = probs[i, :]
for image, channel in zip(self.color_vec.visualize_distribution(dist), '012'):
writer.log_image(step, '%s/%s/%s' % (self.id, desc, channel), image)
super(ListenerLearner, self).on_iter_end(step, writer)
def _data_to_arrays(self, training_instances,
init_vectorizer=False, test=False, inverted=False):
def get_multi(val):
if isinstance(val, tuple):
assert len(val) == 1
return val[0]
else:
return val
get_i, get_o = (lambda inst: inst.input), (lambda inst: inst.output)
get_desc, get_color = (get_o, get_i) if inverted else (get_i, get_o)
get_i_ind, get_o_ind = ((lambda inst: inst.alt_inputs[get_multi(inst.input)]),
(lambda inst: inst.alt_outputs[get_multi(inst.output)]))
get_color_indexed = get_i_ind if inverted else get_o_ind
if hasattr(self.options, 'listener_tokenizer'):
tokenize = TOKENIZERS[self.options.listener_tokenizer]
else:
tokenize = TOKENIZERS['whitespace']
if init_vectorizer:
tokenized = [['<s>'] + tokenize(get_desc(inst)) + ['</s>']
for inst in training_instances]
self.seq_vec.add_all(tokenized)
unk_replaced = self.seq_vec.unk_replace_all(tokenized)
self.word_counts.update([get_desc(inst) for inst in training_instances])
config.dump(unk_replaced, 'unk_replaced.train.jsons', lines=True)
sentences = []
colors = []
if self.options.verbosity >= 9:
print('%s _data_to_arrays:' % self.id)
for i, inst in enumerate(training_instances):
desc = tokenize(get_desc(inst))
color = get_color(inst)
if isinstance(color, numbers.Number):
color = get_color_indexed(inst)
if not color:
assert test
color = (0.0, 0.0, 0.0)
s = ['<s>'] * (self.seq_vec.max_len - 1 - len(desc)) + desc
s.append('</s>')
if self.options.verbosity >= 9:
print('%s -> %s' % (repr(s), repr(color)))
sentences.append(s)
colors.append(color)
x = np.zeros((len(sentences), self.seq_vec.max_len), dtype=np.int32)
y = np.zeros((len(sentences),), dtype=np.int32)
for i, sentence in enumerate(sentences):
if len(sentence) > x.shape[1]:
sentence = sentence[:x.shape[1]]
x[i, :] = self.seq_vec.vectorize(sentence)
y[i] = self.color_vec.vectorize(colors[i], hsv=True)
return [x], [y]
def _build_model(self, model_class=SimpleLasagneModel):
id_tag = (self.id + '/') if self.id else ''
input_var = T.imatrix(id_tag + 'inputs')
target_var = T.ivector(id_tag + 'targets')
self.l_out, self.input_layers = self._get_l_out([input_var])
self.loss = categorical_crossentropy
self.model = model_class(
[input_var], [target_var], self.l_out,
loss=self.loss, optimizer=OPTIMIZERS[self.options.listener_optimizer],
learning_rate=self.options.listener_learning_rate,
id=self.id)
def train_priors(self, training_instances, listener_data=False):
prior_class = PRIORS[self.options.listener_prior]
self.prior_emp = prior_class() # TODO: accurate values for empirical prior
self.prior_smooth = prior_class()
self.prior_emp.train(training_instances, listener_data=listener_data)
self.prior_smooth.train(training_instances, listener_data=listener_data)
def _get_l_out(self, input_vars):
check_options(self.options)
id_tag = (self.id + '/') if self.id else ''
input_var = input_vars[0]
l_in = InputLayer(shape=(None, self.seq_vec.max_len), input_var=input_var,
name=id_tag + 'desc_input')
l_in_embed = EmbeddingLayer(l_in, input_size=len(self.seq_vec.tokens),
output_size=self.options.listener_cell_size,
name=id_tag + 'desc_embed')
cell = CELLS[self.options.listener_cell]
cell_kwargs = {
'grad_clipping': self.options.listener_grad_clipping,
'num_units': self.options.listener_cell_size,
}
if self.options.listener_cell == 'LSTM':
cell_kwargs['forgetgate'] = Gate(b=Constant(self.options.listener_forget_bias))
if self.options.listener_cell != 'GRU':
cell_kwargs['nonlinearity'] = NONLINEARITIES[self.options.listener_nonlinearity]
l_rec1 = cell(l_in_embed, name=id_tag + 'rec1', **cell_kwargs)
if self.options.listener_dropout > 0.0:
l_rec1_drop = DropoutLayer(l_rec1, p=self.options.listener_dropout,
name=id_tag + 'rec1_drop')
else:
l_rec1_drop = l_rec1
l_rec2 = cell(l_rec1_drop, name=id_tag + 'rec2', **cell_kwargs)
if self.options.listener_dropout > 0.0:
l_rec2_drop = DropoutLayer(l_rec2, p=self.options.listener_dropout,
name=id_tag + 'rec2_drop')
else:
l_rec2_drop = l_rec2
l_hidden = DenseLayer(l_rec2_drop, num_units=self.options.listener_cell_size,
nonlinearity=NONLINEARITIES[self.options.listener_nonlinearity],
name=id_tag + 'hidden')
if self.options.listener_dropout > 0.0:
l_hidden_drop = DropoutLayer(l_hidden, p=self.options.listener_dropout,
name=id_tag + 'hidden_drop')
else:
l_hidden_drop = l_hidden
l_scores = DenseLayer(l_hidden_drop, num_units=self.color_vec.num_types, nonlinearity=None,
name=id_tag + 'scores')
l_out = NonlinearityLayer(l_scores, nonlinearity=softmax, name=id_tag + 'out')
return l_out, [l_in]
def sample_prior_smooth(self, num_samples):
return self.prior_smooth.sample(num_samples)
class LRContextListenerLearner(Learner):
def train(self, training_instances, validation_instances=None, metrics=None):
X, y = self._data_to_arrays(training_instances, init_vectorizer=True)
self.mod = LogisticRegression(solver='lbfgs')
self.mod.fit(X, y)
@property
def num_params(self):
return np.prod(self.mod.coef_.shape) + np.prod(self.mod.intercept_.shape)
def predict_and_score(self, eval_instances, random=False, verbosity=0):
X, y = self._data_to_arrays(eval_instances)
y = y.reshape((len(eval_instances), self.context_len))
all_scores = self.mod.predict_log_proba(X)[:, 1].reshape((len(eval_instances),
self.context_len))
all_scores -= logsumexp(all_scores, axis=1)[:, np.newaxis]
preds = all_scores.argmax(axis=1)
scores = np.where(y, all_scores, 0).sum(axis=1)
return preds.tolist(), scores.tolist()
def _data_to_arrays(self, instances, inverted=False, init_vectorizer=False):
self.get_options()
get_i, get_o = (lambda inst: inst.input), (lambda inst: inst.output)
get_desc, get_color = (get_o, get_i) if inverted else (get_i, get_o)
get_alt_i, get_alt_o = (lambda inst: inst.alt_inputs), (lambda inst: inst.alt_outputs)
get_alt_colors = get_alt_i if inverted else get_alt_o
tokenize = TOKENIZERS[self.options.listener_tokenizer]
tokenized = [tokenize(get_desc(inst)) for inst in instances]
context_lens = [len(get_alt_colors(inst)) for inst in instances]
if init_vectorizer:
self.seq_vec = SequenceVectorizer()
self.seq_vec.add_all(tokenized)
unk_replaced = self.seq_vec.unk_replace_all(tokenized)
if init_vectorizer:
config.dump(unk_replaced, 'unk_replaced.train.jsons', lines=True)
self.context_len = context_lens[0]
color_repr = COLOR_REPRS[self.options.listener_color_repr]
self.color_vec = color_repr(self.options.listener_color_resolution,
hsv=self.options.listener_hsv)
assert all(cl == self.context_len for cl in context_lens), (self.context_len, context_lens)
padded = [(d + ['</s>'] * (self.seq_vec.max_len - len(d)))[:self.seq_vec.max_len]
for d in unk_replaced]
colors = [c for inst in instances for c in get_alt_colors(inst)]
labels = np.array([int(i == get_color(inst))
for inst in instances
for i in range(self.context_len)])
desc_indices = self.seq_vec.vectorize_all(padded)
desc_bow = -np.ones((desc_indices.shape[0], self.seq_vec.num_types))
desc_bow[np.arange(desc_indices.shape[0])[:, np.newaxis], desc_indices] = 1.
color_feats = self.color_vec.vectorize_all(colors)
color_feats = color_feats.reshape((desc_indices.shape[0],
self.context_len,
color_feats.shape[1]))
feats = np.einsum('ij,ick->icjk', desc_bow, color_feats)
feats = feats.reshape((desc_indices.shape[0] * self.context_len,
desc_bow.shape[1] * color_feats.shape[2]))
return feats, labels
def get_options(self):
if not hasattr(self, 'options'):
self.options = config.options()