def learn(self, wave, mgc, batch_size):
# disc, wave = self.dio.ulaw_encode(wave)
# from ipdb import set_trace
# set_trace()
last_proc = 0
dy.renew_cg()
total_loss = 0
losses = []
cnt = 0
noise = np.random.normal(0, 1.0, (len(wave) + self.UPSAMPLE_COUNT))
for mgc_index in range(len(mgc)):
curr_proc = int((mgc_index + 1) * 100 / len(mgc))
if curr_proc % 5 == 0 and curr_proc != last_proc:
while last_proc < curr_proc:
last_proc += 5
sys.stdout.write(' ' + str(last_proc))
sys.stdout.flush()
if mgc_index < len(mgc) - 1:
output, excitation, filter, vuv = self._predict_one(mgc[mgc_index],
noise[
self.UPSAMPLE_COUNT * mgc_index:self.UPSAMPLE_COUNT * mgc_index + 2 * self.UPSAMPLE_COUNT])
# reconstruction error
t_vect = wave[self.UPSAMPLE_COUNT * mgc_index:self.UPSAMPLE_COUNT * mgc_index + self.UPSAMPLE_COUNT]
loss = dy.squared_distance(output, dy.inputVector(t_vect))
# dynamic error
o1 = dy.pickrange(output, 0, self.UPSAMPLE_COUNT - 1)
o2 = dy.pickrange(output, 1, self.UPSAMPLE_COUNT)
delta = o2 - o1
real_delta = t_vect[1:self.UPSAMPLE_COUNT] - t_vect[0:self.UPSAMPLE_COUNT - 1]
loss += dy.squared_distance(delta, dy.inputVector(real_delta))
# excitation error
# loss += dy.sum_elems(excitation)
# o1 = dy.pickrange(excitation, 0, self.UPSAMPLE_COUNT - 1)
# o2 = dy.pickrange(excitation, 1, self.UPSAMPLE_COUNT)
# loss += dy.sum_elems(dy.abs(o2 - o1))
losses.append(loss)
cnt += self.UPSAMPLE_COUNT
if len(losses) >= batch_size:
loss = dy.esum(losses)
total_loss += loss.value()
loss.backward()
self.trainer.update()
losses = []
dy.renew_cg()
if len(losses) > 0:
loss = dy.esum(losses)
total_loss += loss.value()
loss.backward()
self.trainer.update()
dy.renew_cg()
return total_loss / cnt
def train(self, mini_batch, num_train, k):
words, pos_tags, chars, langs, signs, masks = mini_batch
# Getting the last hidden layer from BiLSTM.
rnn_out = self.rnn_mlp(mini_batch, True)
h_out = rnn_out[-1]
t_out_d = dy.reshape(h_out, (h_out.dim()[0][0], h_out.dim()[1]))
t_out = dy.transpose(t_out_d)
# Calculating the kq values for NCE.
kq = dy.scalarInput(float(k) / num_train)
lkq = dy.log(kq)
loss_values = []
for i in range(len(langs)):
for j in range(i + 1, len(langs)):
if (langs[i] != langs[j]) and (signs[i] == 1 or signs[j] == 1):
lu = -dy.squared_distance(t_out[i], t_out[j])
denom = dy.log(dy.exp(lu) + kq)
if signs[i] == signs[j]: # both one
nom = lu
else:
nom = lkq
loss_values.append(denom - nom)
err_value = 0
if len(loss_values) > 0:
err = dy.esum(loss_values) / len(loss_values)
err.forward()
err_value = err.value()
err.backward()
self.trainer.update()
dy.renew_cg()
return err_value
def calc_loss(self,
model: 'model_base.ConditionedModel',
src: Union[sent.Sentence, 'batchers.Batch'],
trg: Union[sent.Sentence, 'batchers.Batch']) -> losses.FactoredLossExpr:
search_outputs = model.generate_search_output(src, self.search_strategy)
sign = -1 if self.inv_eval else 1
total_loss = losses.FactoredLossExpr()
for search_output in search_outputs:
# Calculate rewards
eval_score = []
for trg_i, sample_i in zip(trg, search_output.word_ids):
# Removing EOS
sample_i = self.remove_eos(sample_i.tolist())
ref_i = trg_i.words[:trg_i.len_unpadded()]
score = self.evaluation_metric.evaluate_one_sent(ref_i, sample_i)
eval_score.append(sign * score)
reward = dy.inputTensor(eval_score, batched=True)
# Composing losses
loss = losses.FactoredLossExpr()
baseline_loss = []
cur_losses = []
for state, mask in zip(search_output.state, search_output.mask):
bs_score = self.baseline.transform(dy.nobackprop(state.as_vector()))
baseline_loss.append(dy.squared_distance(reward, bs_score))
logsoft = model.decoder.scorer.calc_log_probs(state.as_vector())
loss_i = dy.cmult(logsoft, reward - bs_score)
cur_losses.append(dy.cmult(loss_i, dy.inputTensor(mask, batched=True)))
loss.add_loss("reinforce", dy.sum_elems(dy.esum(cur_losses)))
loss.add_loss("reinf_baseline", dy.sum_elems(dy.esum(baseline_loss)))
# Total losses
total_loss.add_factored_loss_expr(loss)
return loss
def calc_loss(self, translator, src, trg):
search_outputs = translator.generate_search_output(src, self.search_strategy)
sign = -1 if self.inv_eval else 1
total_loss = FactoredLossExpr()
for search_output in search_outputs:
self.eval_score = []
for trg_i, sample_i in zip(trg, search_output.word_ids):
# Removing EOS
sample_i = self.remove_eos(sample_i.tolist())
ref_i = trg_i.words[:trg_i.len_unpadded()]
score = self.evaluation_metric.evaluate_one_sent(ref_i, sample_i)
self.eval_score.append(sign * score)
self.reward = dy.inputTensor(self.eval_score, batched=True)
# Composing losses
loss = FactoredLossExpr()
if self.baseline is not None:
baseline_loss = []
losses = []
for state, logsoft, mask in zip(search_output.state,
search_output.logsoftmaxes,
search_output.mask):
bs_score = self.baseline.transform(state)
baseline_loss.append(dy.squared_distance(self.reward, bs_score))
loss_i = dy.cmult(logsoft, self.reward - bs_score)
valid = list(np.nonzero(mask)[0])
losses.append(dy.cmult(loss_i, dy.inputTensor(mask, batched=True)))
loss.add_loss("reinforce", dy.sum_elems(dy.esum(losses)))
loss.add_loss("reinf_baseline", dy.sum_elems(dy.esum(baseline_loss)))
else:
loss.add_loss("reinforce", dy.sum_elems(dy.cmult(self.true_score, dy.esum(logsofts))))
total_loss.add_factored_loss_expr(loss)
return loss
def on_calc_additional_loss(self, reward):
if not self.learn_segmentation:
return None
ret = LossBuilder()
if self.length_prior_alpha > 0:
reward += self.segment_length_prior * self.length_prior_alpha
reward = dy.cdiv(reward - dy.mean_batches(reward),
dy.std_batches(reward))
# Baseline Loss
if self.use_baseline:
baseline_loss = []
for i, baseline in enumerate(self.bs):
baseline_loss.append(dy.squared_distance(reward, baseline))
ret.add_loss("Baseline", dy.esum(baseline_loss))
# Reinforce Loss
lmbd = self.lmbd.get_value(self.warmup_counter)
if lmbd > 0.0:
reinforce_loss = []
# Calculating the loss of the baseline and reinforce
for i in range(len(self.segment_decisions)):
ll = dy.pick_batch(self.segment_logsoftmaxes[i],
self.segment_decisions[i])
if self.use_baseline:
r_i = reward - self.bs[i]
else:
r_i = reward
reinforce_loss.append(dy.logistic(r_i) * ll)
ret.add_loss("Reinforce", -dy.esum(reinforce_loss) * lmbd)
# Total Loss
return ret
def loss(self, instance):
trans = instance.transformation
#trans = 'lol'
if trans not in self.known_transformations:
newtrans = list(self.param_dict.keys())[0][0] ### SUPER ARBITRARY
tqdm.write(
"WARNING: unknown transformtion picked for instance {}; using transformation {}"
.format(trans, newtrans))
trans = newtrans
b1 = dy.parameter(self.param_dict[(trans, 'b1')])
W1 = dy.parameter(self.param_dict[(trans, 'W1')])
b2 = dy.parameter(self.param_dict[(trans, 'b2')])
W2 = dy.parameter(self.param_dict[(trans, 'W2')])
#b3 = dy.parameter(self.param_dict[(trans, 'b3')])
#W3 = dy.parameter(self.param_dict[(trans, 'W3')])
#b = dy.parameter(self.param_dict[(trans, 'b')])
#W = dy.parameter(self.param_dict[(trans, 'W')])
x = dy.inputVector(instance.xs_distr_vec)
y = dy.inputVector(instance.ys_distr_vec)
#prediction = dy.affine_transform([b, W, x])
prediction = dy.affine_transform(
[b2, W2, dy.tanh(dy.affine_transform([b1, W1, x]))])
#prediction = dy.affine_transform(
# [b3, W3, dy.tanh(dy.affine_transform(
# [b2, W2, dy.tanh(dy.affine_transform([b1, W1, x])) ] ))])
loss = dy.squared_distance(prediction, y)
return prediction, loss
def __call__(self, translator, dec_state, src, trg):
# TODO: apply trg.mask ?
samples = []
logsofts = []
self.bs = []
done = [False for _ in range(len(trg))]
for _ in range(self.sample_length):
dec_state.context = translator.attender.calc_context(dec_state.rnn_state.output())
if self.use_baseline:
h_t = dy.tanh(translator.decoder.context_projector(dy.concatenate([dec_state.rnn_state.output(), dec_state.context])))
self.bs.append(self.baseline(dy.nobackprop(h_t)))
logsoft = dy.log_softmax(translator.decoder.get_scores(dec_state))
sample = logsoft.tensor_value().categorical_sample_log_prob().as_numpy()[0]
# Keep track of previously sampled EOS
sample = [sample_i if not done_i else Vocab.ES for sample_i, done_i in zip(sample, done)]
# Appending and feeding in the decoder
logsoft = dy.pick_batch(logsoft, sample)
logsofts.append(logsoft)
samples.append(sample)
dec_state = translator.decoder.add_input(dec_state, translator.trg_embedder.embed(xnmt.batcher.mark_as_batch(sample)))
# Check if we are done.
done = list(six.moves.map(lambda x: x == Vocab.ES, sample))
if all(done):
break
samples = np.stack(samples, axis=1).tolist()
self.eval_score = []
for trg_i, sample_i in zip(trg, samples):
# Removing EOS
try:
idx = sample_i.index(Vocab.ES)
sample_i = sample_i[:idx]
except ValueError:
pass
try:
idx = trg_i.words.index(Vocab.ES)
trg_i.words = trg_i.words[:idx]
except ValueError:
pass
# Calculate the evaluation score
score = 0 if not len(sample_i) else self.evaluation_metric.evaluate_fast(trg_i.words, sample_i)
self.eval_score.append(score)
self.true_score = dy.inputTensor(self.eval_score, batched=True)
loss = LossBuilder()
if self.use_baseline:
for i, (score, _) in enumerate(zip(self.bs, logsofts)):
logsofts[i] = dy.cmult(logsofts[i], score - self.true_score)
loss.add_loss("Reinforce", dy.sum_elems(dy.esum(logsofts)))
else:
loss.add_loss("Reinforce", dy.sum_elems(dy.cmult(-self.true_score, dy.esum(logsofts))))
if self.use_baseline:
baseline_loss = []
for bs in self.bs:
baseline_loss.append(dy.squared_distance(self.true_score, bs))
loss.add_loss("Baseline", dy.sum_elems(dy.esum(baseline_loss)))
return loss
def on_calc_additional_loss(self, translator_loss):
if not self.learn_segmentation or self.segment_decisions is None:
return None
reward = -translator_loss["mle"]
if not self.log_reward:
reward = dy.exp(reward)
reward = dy.nobackprop(reward)
# Make sure that reward is not scalar, but rather based on the each batch item
assert reward.dim()[1] == len(self.src_sent)
# Mask
enc_mask = self.enc_mask.get_active_one_mask().transpose() if self.enc_mask is not None else None
# Compose the lose
ret = LossBuilder()
## Length prior
alpha = self.length_prior_alpha.value() if self.length_prior_alpha is not None else 0
if alpha > 0:
reward += self.segment_length_prior * alpha
# reward z-score normalization
if self.z_normalization:
reward = dy.cdiv(reward-dy.mean_batches(reward), dy.std_batches(reward) + EPS)
## Baseline Loss
if self.use_baseline:
baseline_loss = []
for i, baseline in enumerate(self.bs):
loss = dy.squared_distance(reward, baseline)
if enc_mask is not None:
loss = dy.cmult(dy.inputTensor(enc_mask[i], batched=True), loss)
baseline_loss.append(loss)
ret.add_loss("Baseline", dy.esum(baseline_loss))
if self.print_sample:
print(dy.exp(self.segment_logsoftmaxes[i]).npvalue().transpose()[0])
## Reinforce Loss
lmbd = self.lmbd.value()
if lmbd > 0.0:
reinforce_loss = []
# Calculating the loss of the baseline and reinforce
for i in range(len(self.segment_decisions)):
ll = dy.pick_batch(self.segment_logsoftmaxes[i], self.segment_decisions[i])
if self.use_baseline:
r_i = reward - dy.nobackprop(self.bs[i])
else:
r_i = reward
if enc_mask is not None:
ll = dy.cmult(dy.inputTensor(enc_mask[i], batched=True), ll)
reinforce_loss.append(r_i * -ll)
loss = dy.esum(reinforce_loss) * lmbd
ret.add_loss("Reinforce", loss)
if self.confidence_penalty:
ls_loss = self.confidence_penalty(self.segment_logsoftmaxes, enc_mask)
ret.add_loss("Confidence Penalty", ls_loss)
# Total Loss
return ret
def cost_sensitive_reg_loss(indices, logits, logit_len, costs=None, valid_actions=None, verbose=False):
"""Computes MSE loss over actions thereby solving cost-sensitive multiclass classification.
Takes the same arguments as other losses for dynamic oracles for compatibility.
The lower the cost the better, therefore all invalid actions (-np.inf) are remapped to
large cost numbers."""
# map costs between 0 and 1, with a margin for invalid actions
costs[np.isinf(costs)] = np.max(costs) + 1
costs = costs / np.max(costs) # given that 0. costs are in there, we end up perfectly in [0, 1]
# NB! Note the minus to cancel out the minus added in the batch loss
#if verbose and np.random.rand() > 0.99: print 'Costs, logits: ', costs, logits.npvalue()#[valid_actions]
return -dy.squared_distance(logits, dy.inputVector(costs))
def print_outputs(self, samples):
for sample in samples:
dy.renew_cg(immediate_compute=False, check_validity=False)
graph_output = self.deserializer.buildSample(sample.neurons)
label = dy.scalarInput(sample.target)
loss = dy.squared_distance(graph_output, label)
# dy.print_text_graphviz()
print(f'label: {label.value()}, output: {graph_output.value()}')
def calc_baseline_loss(self, reward, only_final_reward):
pred_rewards = []
cur_losses = []
for i, state in enumerate(self.states):
pred_reward = self.baseline.transform(dy.nobackprop(state))
pred_rewards.append(dy.nobackprop(pred_reward))
seq_reward = reward if only_final_reward else reward[i]
if self.valid_pos is not None:
pred_reward = dy.pick_batch_elems(pred_reward, self.valid_pos[i])
act_reward = dy.pick_batch_elems(seq_reward, self.valid_pos[i])
else:
act_reward = seq_reward
cur_losses.append(dy.sum_batches(dy.squared_distance(pred_reward, dy.nobackprop(act_reward))))
return pred_rewards, dy.esum(cur_losses)
def eval(self, mini_batch):
words, pos_tags, chars, langs, signs, masks = mini_batch
h_out = self.rnn_mlp(mini_batch, False)[-1]
t_out = dy.transpose(
dy.reshape(h_out, (h_out.dim()[0][0], h_out.dim()[1])))
sims = []
for i in range(len(langs)):
for j in range(i + 1, len(langs)):
sims.append(dy.sqrt(dy.squared_distance(t_out[i], t_out[j])))
sim = dy.esum(sims)
sim.forward()
sim_value = sim.value() / len(sims)
dy.renew_cg()
return sim_value
def calc_baseline_loss(self, rewards):
avg_rewards = dy.average(
rewards) # Taking average of the rewards accross multiple samples
pred_rewards = []
loss = []
for i, state in enumerate(self.states):
pred_reward = self.baseline(dy.nobackprop(state))
pred_rewards.append(dy.nobackprop(pred_reward))
if self.valid_pos is not None:
pred_reward = dy.pick_batch_elems(pred_reward,
self.valid_pos[i])
avg_reward = dy.pick_batch_elems(avg_rewards,
self.valid_pos[i])
else:
avg_reward = avg_rewards
loss.append(
dy.sum_batches(dy.squared_distance(pred_reward, avg_reward)))
return pred_rewards, dy.esum(loss)
def _perform_calc_loss(
self, model: 'model_base.ConditionedModel',
src: Union[sent.Sentence, 'batchers.Batch'],
trg: Union[sent.Sentence,
'batchers.Batch']) -> losses.FactoredLossExpr:
search_outputs = model.generate_search_output(src,
self.search_strategy)
sign = -1 if self.inv_eval else 1
# TODO: Fix units
total_loss = collections.defaultdict(int)
for search_output in search_outputs:
# Calculate rewards
eval_score = []
for trg_i, sample_i in zip(trg, search_output.word_ids):
# Removing EOS
sample_i = utils.remove_eos(sample_i.tolist(), vocabs.Vocab.ES)
ref_i = trg_i.words[:trg_i.len_unpadded()]
score = self.evaluation_metric.evaluate_one_sent(
ref_i, sample_i)
eval_score.append(sign * score)
reward = dy.inputTensor(eval_score, batched=True)
# Composing losses
baseline_loss = []
cur_losses = []
for state, mask in zip(search_output.state, search_output.mask):
bs_score = self.baseline.transform(
dy.nobackprop(state.as_vector()))
baseline_loss.append(dy.squared_distance(reward, bs_score))
logsoft = model.decoder.scorer.calc_log_probs(
state.as_vector())
loss_i = dy.cmult(logsoft, reward - bs_score)
cur_losses.append(
dy.cmult(loss_i, dy.inputTensor(mask, batched=True)))
total_loss["polc_loss"] += dy.sum_elems(dy.esum(cur_losses))
total_loss["base_loss"] += dy.sum_elems(dy.esum(baseline_loss))
units = [t.len_unpadded() for t in trg]
total_loss = losses.FactoredLossExpr(
{k: losses.LossExpr(v, units)
for k, v in total_loss.items()})
return losses.FactoredLossExpr({"risk": total_loss})
def learn(self, src, dst):
softmax_list, aux_list = self._predict(src, dst=dst, num_predictions=len(dst) + 1, runtime=False)
for softmax, aux, entry in zip(softmax_list, aux_list, dst):
word = entry.word.decode('utf-8').lower()
if word in self.output_encodings.word2int:
w_index = self.output_encodings.word2int[word]
else:
w_index = self.output_encodings.word2int["<UNK>"]
w_emb, found = self.dst_we.get_word_embeddings(entry.word.decode('utf-8'))
self.losses.append(-dy.log(dy.pick(softmax, w_index)))
if found:
vec1=aux
vec2=dy.inputVector(w_emb)
cosine = dy.dot_product(vec1, vec2) * dy.pow(dy.l2_norm(vec1) * dy.l2_norm(vec2),
dy.scalarInput(-1))
self.losses.append(dy.squared_distance(cosine, dy.scalarInput(1.0)))
self.losses.append(-dy.log(dy.pick(softmax_list[-1], self.EOS)))
def __call__(self, translator, initial_state, src, trg):
# TODO(philip30): currently only using the best hypothesis / first sample for reinforce loss
# A small further implementation is needed if we want to do reinforce with multiple samples.
search_output = translator.search_strategy.generate_output(
translator, initial_state)[0]
# Calculate evaluation scores
self.eval_score = []
for trg_i, sample_i in zip(trg, search_output.word_ids):
# Removing EOS
sample_i = self.remove_eos(sample_i.tolist())
ref_i = self.remove_eos(trg_i.words)
# Evaluating
if len(sample_i) == 0:
score = 0
else:
score = self.evaluation_metric.evaluate(ref_i, sample_i) * \
(-1 if self.inv_eval else 1)
self.eval_score.append(score)
self.true_score = dy.inputTensor(self.eval_score, batched=True)
# Composing losses
loss = LossBuilder()
if self.use_baseline:
baseline_loss = []
losses = []
for state, logsoft, mask in zip(search_output.state,
search_output.logsoftmaxes,
search_output.mask):
bs_score = self.baseline(state)
baseline_loss.append(
dy.squared_distance(self.true_score, bs_score))
loss_i = dy.cmult(logsoft, self.true_score - bs_score)
losses.append(
dy.cmult(loss_i, dy.inputTensor(mask, batched=True)))
loss.add_loss("reinforce", dy.sum_elems(dy.esum(losses)))
loss.add_loss("reinf_baseline",
dy.sum_elems(dy.esum(baseline_loss)))
else:
loss.add_loss(
"reinforce",
dy.sum_elems(dy.cmult(self.true_score, dy.esum(logsofts))))
return loss
def learn(self, samples):
for iter in range(self.epochae):
if (iter > 0 and iter % self.printouts == 0):
print(iter, " average loss is:", total_loss / seen_instances)
seen_instances = 0
total_loss = 0
for sample in samples:
dy.renew_cg(immediate_compute=False, check_validity=False)
label = dy.scalarInput(sample.target)
graph_output = self.deserializer.buildSample(sample.neurons)
loss = dy.squared_distance(graph_output, label)
seen_instances += 1
total_loss += loss.value()
loss.backward()
self.trainer.update()
self.print_outputs(samples)
def model(self):
print("Model Creating...")
hidden_size = 64
vocabulary_size = len(self.vocab_to_index)
input_size = output_size = vocabulary_size # Input and Output size are equal to V.
m = dy.Model()
W = m.add_parameters((hidden_size, input_size))
b = m.add_parameters(hidden_size)
V = m.add_parameters((output_size, hidden_size)) # Softmax weights
a = m.add_parameters(output_size) # Softmax bias
x = dy.vecInput(input_size)
y = dy.vecInput(output_size)
h = dy.tanh((W * x) + b)
output = dy.softmax(V * h) # Softmax
loss = dy.squared_distance(output, y)
trainer = dy.SimpleSGDTrainer(m)
epoch = 1
for iter in range(epoch):
my_loss = 0.0
seen_instances = 0
for binary_word in self.bigram_poem:
x.set(self.word_vectors[binary_word[0]])
y.set(self.word_vectors[binary_word[1]])
seen_instances += 1
my_loss += loss.scalar_value()
loss.forward()
loss.backward()
trainer.update()
if (seen_instances > 1 and seen_instances % 100 == 0):
print(seen_instances, "/", len(self.bigram_poem),
"***average loss is:", my_loss / seen_instances)
print(my_loss / seen_instances)
def create_graph(init_weight, init_con, init_bias, weight, bias, con, input,
target, number, nodes):
input_layer, layer_nodes, sizes, matrices, mapping_numbers, inputs, layer, maximum = graphs[
number]
values = [0] * nodes
dy.renew_cg()
shared_nodes = graph_shared[number]
#node je srialove cislo a layer je vrstva
for node, serial_node, layer in shared_nodes.keys():
#prejdenie cez vsetky zdielane vrcholy
serial, serial_number, vrstva, graf = shared_nodes[(node, serial_node,
layer)][0]
we = shared_models[graf][3][vrstva].value()
wt = weight[layer].value()
wt[node][serial_node] = we[serial_number][serial]
weight[layer].set_value(wt)
x = dy.vecInput(input.size)
x.set(input)
y = dy.vecInput(target.size)
y.set(target)
result = init_weight * x
hgt = dy.cmult(init_weight, init_con)
init_weight.set_value(hgt.value())
result = dy.logistic(result + init_bias)
for j in range(layer_nodes[0].__len__()):
values[layer_nodes[0][j]] = result[j]
for i in range(maximum):
inp = []
for node in input_layer[i]:
inp.extend([values[node]])
inp = dy.concatenate(inp)
weight[i].set_value(dy.cmult(weight[i], con[i]).value())
result = weight[i] * inp
result = dy.logistic(result + bias[i])
for j in range(layer_nodes[i + 1].__len__()):
values[layer_nodes[i + 1][j]] = result[j]
loss = dy.squared_distance(y, result)
return loss
def train(self, train_word_to_type, test_word_to_type=None, iterations=50):
training_examples = self.build_example_vectors(train_word_to_type)
for iteration_idx in xrange(1, iterations+1):
print "Starting training iteration %d/%d" % (iteration_idx, iterations)
random.shuffle(training_examples)
loss = 0
for example_index, (word_index, expected_output) in enumerate(training_examples, 1):
out_expression = self.build_expression(word_index)
expected_output_expr = dy.vecInput(len(self.type_indexer))
expected_output_expr.set(expected_output)
sentence_error = dy.squared_distance(out_expression, expected_output_expr)
# sentence_error = dy.pickneglogsoftmax(out_expression, np.argmax(expected_output))
loss += sentence_error.scalar_value()
sentence_error.backward()
self.trainer.update()
# Trainer Status
self.trainer.status()
print loss / float(len(training_examples))
def create_graph(init_weight, init_con, init_bias, weight, bias, con, input, target):
x = dy.vecInput(input.size)
x.set(input)
y=dy.vecInput(target.size)
y.set(target)
result = init_weight * x
hgt = dy.cmult(init_weight, init_con)
init_weight.set_value(hgt.value())
result = dy.logistic(result + init_bias)
for j in range(layer_nodes[0].__len__()):
values[layer_nodes[0][j]] = result[j]
for i in range(maximum):
inp = []
for node in input_layer[i]:
inp.extend([values[node]])
inp = dy.concatenate(inp)
weight[i].set_value(dy.cmult(weight[i], con[i]).value())
result = weight[i] * inp
result = dy.logistic(result + bias[i])
for j in range(layer_nodes[i + 1].__len__()):
values[layer_nodes[i + 1][j]] = result[j]
loss = dy.squared_distance(y, result)
return loss
def fit(self,
train_X,
train_Y,
num_epochs,
train_algo,
val_X=None,
val_Y=None,
patience=2,
model_path=None,
seed=None,
word_dropout_rate=0.25,
learning_rate=0,
trg_vectors=None,
unsup_weight=1.0):
"""
train the tagger
:param trg_vectors: the prediction targets used for the unsupervised loss
in temporal ensembling
:param unsup_weight: weight for the unsupervised consistency loss
used in temporal ensembling
"""
print("read training data", file=sys.stderr)
if seed:
print(">>> using seed: ", seed, file=sys.stderr)
random.seed(seed) #setting random seed
training_algo = TRAINER_MAP[train_algo]
if learning_rate > 0:
trainer = training_algo(self.model, learning_rate=learning_rate)
else:
trainer = training_algo(self.model)
# if we use word dropout keep track of counts
if word_dropout_rate > 0.0:
widCount = Counter()
for sentence, _ in train_X:
widCount.update([w for w in sentence])
assert (len(train_X) == len(train_Y))
train_data = list(zip(train_X, train_Y))
# if we use target vectors, keep track of the targets per sentence
if trg_vectors is not None:
trg_start_id = 0
sentence_trg_vectors = []
for i, (example, y) in enumerate(train_data):
sentence_trg_vectors.append(
trg_vectors[trg_start_id:trg_start_id +
len(example[0]), :])
trg_start_id += len(example[0])
assert trg_start_id == len(trg_vectors),\
'Error: Idx %d is not at %d.' % (trg_start_id, len(trg_vectors))
print('Starting training for %d epochs...' % num_epochs)
best_val_acc, epochs_no_improvement = 0., 0
if val_X is not None and val_Y is not None and model_path is not None:
print('Using early stopping with patience of %d...' % patience)
for cur_iter in range(num_epochs):
bar = Bar('Training epoch %d/%d...' % (cur_iter + 1, num_epochs),
max=len(train_data),
flush=True)
total_loss = 0.0
total_tagged = 0.0
random_indices = np.arange(len(train_data))
random.shuffle(random_indices)
for i, idx in enumerate(random_indices):
(word_indices, char_indices), y = train_data[idx]
if word_dropout_rate > 0.0:
word_indices = [
self.w2i["_UNK"] if
(random.random() >
(widCount.get(w) /
(word_dropout_rate + widCount.get(w)))) else w
for w in word_indices
]
output = self.predict(word_indices, char_indices, train=True)
if len(y) == 1 and y[0] == 0:
# in temporal ensembling, we assign a dummy label of [0] for
# unlabeled sequences; we skip the supervised loss for these
loss = dynet.scalarInput(0)
else:
if trg_vectors:
# use average instead of sum here so long sequences are not
# preferred and so it can be combined with aux loss
loss = dynet.average([
self.pick_neg_log(pred, gold)
for pred, gold in zip(output, y)
])
else:
loss = dynet.esum([
self.pick_neg_log(pred, gold)
for pred, gold in zip(output, y)
])
if trg_vectors is not None:
# the consistency loss in temporal ensembling is used for
# both supervised and unsupervised input
targets = sentence_trg_vectors[idx]
assert len(output) == len(targets)
other_loss = unsup_weight * dynet.average([
dynet.squared_distance(o, dynet.inputVector(t))
for o, t in zip(output, targets)
])
loss += other_loss
total_loss += loss.value()
total_tagged += len(word_indices)
loss.backward()
trainer.update()
bar.next()
print("iter {2} {0:>12}: {1:.2f}".format("total loss",
total_loss / total_tagged,
cur_iter),
file=sys.stderr)
if val_X is not None and val_Y is not None and model_path is not None:
# get the best accuracy on the validation set
val_correct, val_total = self.evaluate(val_X, val_Y)
val_accuracy = val_correct / val_total
if val_accuracy > best_val_acc:
print(
'Accuracy %.4f is better than best val accuracy %.4f.'
% (val_accuracy, best_val_acc))
best_val_acc = val_accuracy
epochs_no_improvement = 0
save_tagger(self, model_path)
else:
print('Accuracy %.4f is worse than best val loss %.4f.' %
(val_accuracy, best_val_acc))
epochs_no_improvement += 1
if epochs_no_improvement == patience:
print('No improvement for %d epochs. Early stopping...' %
epochs_no_improvement)
break
def loss(self, observation, target_rep):
return dy.squared_distance(observation, dy.inputVector(target_rep))
inp = []
for node in input_layer[i]:
inp.extend([values[node]])
a = matrices[i]
weight[i] = m.add_parameters(a.shape, init=a)
inp = dy.concatenate(inp)
bias = m.add_parameters(layer_nodes[i + 1].__len__())
con = dy.const_parameter(m.add_parameters(a.shape, init=a))
weight[i] = dy.cmult(weight[i], con)
result = weight[i] * inp
result = dy.logistic(result + bias)
for j in range(layer_nodes[i + 1].__len__()):
values[layer_nodes[i + 1][j]] = result[j]
y_pred = result
y = dy.vecInput(sizes[maximum])
loss = dy.squared_distance(y_pred, y)
mloss = 0.0
for learning_cycle in range(number_of_learning_cycles):
for number in range(numberOfGraphs):
inp = np.zeros(sizes[0])
for i in range(sizes[0]):
if layer_nodes[0][i] in graph_inputs[number][0]:
inp[i] = 1
input.set(np.random.uniform(0, 2, sizes[0]))
target = y_pred.value()
for i in range(sizes[maximum]):
if layer_nodes[maximum][i] in graph_inputs[number][1]:
target[i] = 1
y.set(target)
e1, stddev
) # add a noise to each element from a gausian with standard-dev = stddev
dy.dropout(e1, p) # apply dropout with probability p
# functions over lists of expressions
e = dy.esum([e1, e2, ...]) # sum
e = dy.average([e1, e2, ...]) # average
e = dy.concatenate_cols(
[e1, e2, ...]
) # e1, e2,.. are column vectors. return a matrix. (sim to np.hstack([e1,e2,...])
e = dy.concatenate([e1, e2, ...]) # concatenate
e = dy.affine_transform([e0, e1, e2, ...]) # e = e0 + ((e1*e2) + (e3*e4) ...)
## Loss functions
e = dy.squared_distance(e1, e2)
e = dy.l1_distance(e1, e2)
e = dy.huber_distance(e1, e2, c=1.345)
# e1 must be a scalar that is a value between 0 and 1
# e2 (ty) must be a scalar that is a value between 0 and 1
# e = ty * log(e1) + (1 - ty) * log(1 - e1)
e = dy.binary_log_loss(e1, e2)
# e1 is row vector or scalar
# e2 is row vector or scalar
# m is number
# e = max(0, m - (e1 - e2))
e = dy.pairwise_rank_loss(e1, e2, m=1.0)
# Convolutions
vectorSize = len(wordVector)
hiddenNeuronNumber = int(vectorSize / 100)
m = dy.ParameterCollection()
W = m.add_parameters((hiddenNeuronNumber, vectorSize * 2))
V = m.add_parameters((vectorSize, hiddenNeuronNumber))
b = m.add_parameters((hiddenNeuronNumber))
dy.renew_cg()
x = dy.vecInput(vectorSize * 2)
output = dy.logistic(V * (dy.tanh((W * x) + b)))
y = dy.vecInput(vectorSize)
loss = dy.squared_distance(output, y)
trainer = dy.SimpleSGDTrainer(m)
epoch = 0
while epoch < 10:
epochLoss = 0
for i in range(0, len(wordList) - 2):
x.set(
defineWordVector(wordList[0], wordVector) +
defineWordVector(wordList[1], wordVector))
y.set(defineWordVector(wordList[2], wordVector))
loss.backward()
trainer.update()
def test_wsj():
print
print '# test on wsj subset'
data, n_types, n_labels = pickle.load(open('wsj.pkl', 'r'))
d_emb = 50
d_rnn = 51
d_hid = 52
d_actemb = 5
minibatch_size = 5
n_epochs = 10
preprocess_minibatch = True
model = dy.ParameterCollection()
embed_word = model.add_lookup_parameters((n_types, d_emb))
f_gru = dy.GRUBuilder(1, d_emb, d_rnn, model)
b_gru = dy.GRUBuilder(1, d_emb, d_rnn, model)
embed_action = model.add_lookup_parameters((n_labels, d_actemb))
combine_arh_W = model.add_parameters((d_hid, d_actemb + d_rnn * 2 + d_hid))
combine_arh_b = model.add_parameters(d_hid)
initial_h = model.add_parameters(d_hid, dy.ConstInitializer(0))
initial_actemb = model.add_parameters(d_actemb, dy.ConstInitializer(0))
policy_W = model.add_parameters((n_labels, d_hid))
policy_b = model.add_parameters(n_labels)
optimizer = dy.AdamTrainer(model, alpha=0.01)
for _ in xrange(n_epochs):
total_loss = 0
for batch in minibatch(data, minibatch_size, True):
dy.renew_cg()
combine_arh_We = dy.parameter(combine_arh_W)
combine_arh_be = dy.parameter(combine_arh_b)
policy_We = dy.parameter(policy_W)
policy_be = dy.parameter(policy_b)
loss = 0
if preprocess_minibatch:
# for efficiency, combine RNN outputs on entire
# minibatch in one go (requires padding with zeros,
# should be masked but isn't right now)
all_tokens = [ex.tokens for ex in batch]
max_length = max(map(len, all_tokens))
all_tokens = [[x[i] if len(x) > i else 0 for x in all_tokens]
for i in range(max_length)]
all_e = [dy.lookup_batch(embed_word, x) for x in all_tokens]
all_rnn_out = bi_gru(f_gru, b_gru, all_e)
losses = []
for batch_id, ex in enumerate(batch):
N = len(ex.tokens)
if preprocess_minibatch:
rnn_out = [
dy.pick_batch_elem(x, batch_id)
for x in all_rnn_out[:N]
]
else:
e = [embed_word[x] for x in ex.tokens]
rnn_out = bi_gru(f_gru, b_gru, e)
prev_h = dy.parameter(initial_h) # previous hidden state
actemb = dy.parameter(
initial_actemb) # embedding of previous action
output = []
for t in xrange(N):
# update hidden state based on most recent
# *predicted* action (not ground truth)
inputs = [actemb, prev_h, rnn_out[t]]
h = dy.rectify(
dy.affine_transform([
combine_arh_be, combine_arh_We,
dy.concatenate(inputs)
]))
# make prediction
pred_vec = dy.affine_transform([policy_be, policy_We, h])
pred = pred_vec.npvalue().argmin()
output.append(pred)
# accumulate loss (squared error against costs)
truth = np.ones(n_labels)
truth[ex.labels[t]] = 0
losses.append(
dy.squared_distance(pred_vec, dy.inputTensor(truth)))
# cache hidden state, previous action embedding
prev_h = h
actemb = embed_action[pred]
# print 'output=%s, truth=%s' % (output, ex.labels)
loss = dy.esum(losses)
loss.backward()
total_loss += loss.value()
optimizer.update()
print total_loss
def predict_loss(self, encodings, usr):
avg_enc = dy.mean_dim(encodings, 1)
h = dy.rectify(dy.affine_transform([self.bh, self.Wh, avg_enc]))
s = dy.affine_transform([self.bu, self.Wu, h])
return dy.mean_batches(dy.squared_distance(s, self.usr_vec))
def calc_loss(self, policy_reward, results={}):
"""
Calc policy networks loss.
"""
assert len(policy_reward) == len(self.states), "There should be a reward for every action taken"
batch_size = self.states[0].dim()[1]
loss = {}
# Calculate the baseline loss of the reinforce loss for each timestep:
# b = W_b * s + b_b
# R = r - b
# Also calculate the baseline loss
# b = r_p (predicted)
# loss_b = squared_distance(r_p - r_r)
rewards = []
baseline_loss = []
units = np.zeros(batch_size)
for i, state in enumerate(self.states):
r_p = self.baseline.transform(dy.nobackprop(state))
rewards.append(policy_reward[i] - r_p)
if self.valid_pos[i] is not None:
r_p = dy.pick_batch_elems(r_p, self.valid_pos[i])
r_r = dy.pick_batch_elems(policy_reward[i], self.valid_pos[i])
units[self.valid_pos[i]] += 1
else:
r_r = policy_reward[i]
units += 1
baseline_loss.append(dy.sum_batches(dy.squared_distance(r_p, r_r)))
loss["rl_baseline"] = losses.LossExpr(dy.esum(baseline_loss), units)
# Z Normalization
# R = R - mean(R) / std(R)
rewards = dy.concatenate(rewards, d=0)
r_dim = rewards.dim()
if self.z_normalization:
rewards_shape = dy.reshape(rewards, (r_dim[0][0], r_dim[1]))
rewards_mean = dy.mean_elems(rewards_shape)
rewards_std = dy.std_elems(rewards_shape) + 1e-20
rewards = (rewards - rewards_mean.value()) / rewards_std.value()
rewards = dy.nobackprop(rewards)
# Calculate Confidence Penalty
if self.confidence_penalty:
loss["rl_confpen"] = self.confidence_penalty.calc_loss(self.policy_lls)
# Calculate Reinforce Loss
# L = - sum([R-b] * pi_ll)
reinf_loss = []
units = np.zeros(batch_size)
for i, (policy, action) in enumerate(zip(self.policy_lls, self.actions)):
reward = dy.pick(rewards, i)
ll = dy.pick_batch(policy, action)
if self.valid_pos[i] is not None:
ll = dy.pick_batch_elems(ll, self.valid_pos[i])
reward = dy.pick_batch_elems(reward, self.valid_pos[i])
units[self.valid_pos[i]] += 1
else:
units += 1
reinf_loss.append(dy.sum_batches(dy.cmult(ll, reward)))
loss["rl_reinf"] = losses.LossExpr(-dy.esum(reinf_loss), units)
# Pack up + return
return losses.FactoredLossExpr(loss)
a = m.add_parameters(1)
if len(sys.argv) == 2:
m.populate_from_textfile(sys.argv[1])
x = dy.vecInput(2)
y = dy.scalarInput(0)
h = dy.tanh((W*x) + b)
if xsent:
y_pred = dy.logistic((V*h) + a)
loss = dy.binary_log_loss(y_pred, y)
T = 1
F = 0
else:
y_pred = (V*h) + a
loss = dy.squared_distance(y_pred, y)
T = 1
F = -1
for iter in range(ITERATIONS):
mloss = 0.0
for mi in range(4):
x1 = mi % 2
x2 = (mi // 2) % 2
x.set([T if x1 else F, T if x2 else F])
y.set(T if x1 != x2 else F)
mloss += loss.scalar_value()
loss.backward()
trainer.update()
mloss /= 4.
def unsupervised_with_baseline(self):
decoder = self.create_decoder()
assert (os.path.exists(self.options.result_dir + 'model_dec'))
self.load_decoder(decoder)
encoder = self.create_encoder()
assert (os.path.exists(self.options.result_dir + 'model_enc'))
self.load_encoder(encoder)
baseline = self.create_baseline()
if os.path.exists(self.options.result_dir + 'baseline'):
self.load_baseline(baseline)
enc_trainer = optimizers[self.options.optimizer](encoder.model)
dec_trainer = optimizers[self.options.optimizer](decoder.model)
baseline_trainer = optimizers[self.options.optimizer](baseline.model)
lr = self.options.lr #used only for sgd
i = 0
lowest_valid_loss = 9999
print('unsupervised training...')
for epoch in range(self.options.epochs):
sents = 0
total_loss = 0.0
train = self.reader.next_example(0)
train_size = len(self.reader.data[0])
for data in train:
s1, s2, s3, pos, act = data[0], data[1], data[2], data[
3], data[4]
sents += 1
# random sample
enc_loss_act, _, act = encoder.parse(s1,
s2,
s3,
pos,
sample=True)
_, dec_loss_act, dec_loss_word = decoder.compute_loss(s3, act)
# save reward
logpx = -dec_loss_word.scalar_value()
total_loss -= logpx
# reconstruction and regularization loss backprop to theta_d
dec_loss_total = dec_loss_word + dec_loss_act * dy.scalarInput(
self.options.dec_reg)
dec_loss_total = dec_loss_total * dy.scalarInput(
1.0 / self.options.mcsamples)
dec_loss_total.scalar_value()
dec_loss_total.backward()
# update decoder
if self.options.optimizer == 'sgd':
dec_trainer.update(lr)
else:
dec_trainer.update()
if self.options.enc_update > 0:
# compute baseline and backprop to theta_b
b = baseline(s3)
logpxb = b.scalar_value()
b_loss = dy.squared_distance(b, dy.scalarInput(logpx))
b_loss.value()
b_loss.backward()
# update baseline
if self.options.optimizer == 'sgd':
baseline_trainer.update(lr)
else:
baseline_trainer.update()
# policy and and regularization loss backprop to theta_e
enc_loss_act = encoder.train(s1, s2, s3, pos, act)
enc_loss_policy = enc_loss_act * dy.scalarInput(
(logpx - logpxb) / len(s1))
enc_loss_total = enc_loss_policy * dy.scalarInput(
self.options.enc_update
) - enc_loss_act * dy.scalarInput(self.options.enc_reg)
enc_loss_total = enc_loss_total * dy.scalarInput(
1.0 / self.options.mcsamples)
enc_loss_total.value()
enc_loss_total.backward()
# update encoder
if self.options.optimizer == 'sgd':
enc_trainer.update(lr)
else:
enc_trainer.update()
e = float(i) / train_size
if i % self.options.print_every == 0:
print('epoch {}: loss per sentence: {}'.format(
e, total_loss / sents))
sents = 0
total_loss = 0.0
if i != 0 and i % self.options.save_every == 0:
print('computing loss on validation set...')
total_valid_loss = 0
valid = self.reader.next_example(1)
valid_size = len(self.reader.data[1])
for vdata in valid:
s1, s2, s3, pos, act = vdata[0], vdata[1], vdata[
2], vdata[3], vdata[4]
_, _, valid_word_loss = decoder.compute_loss(s3, act)
if valid_word_loss is not None:
total_valid_loss += valid_word_loss.scalar_value()
total_valid_loss = total_valid_loss * 1.0 / valid_size
if total_valid_loss < lowest_valid_loss:
lowest_valid_loss = total_valid_loss
print('saving model...')
encoder.Save(self.options.result_dir + 'model_enc')
decoder.Save(self.options.result_dir + 'model_dec')
baseline.Save(self.options.result_dir + 'baseline')
else:
lr = lr * self.options.decay
i += 1
