本文整理汇总了Python中allennlp.nn.util.masked_softmax方法的典型用法代码示例。如果您正苦于以下问题:Python util.masked_softmax方法的具体用法?Python util.masked_softmax怎么用?Python util.masked_softmax使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类allennlp.nn.util
的用法示例。
在下文中一共展示了util.masked_softmax方法的14个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: decode
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def decode(self,
initial_state ,
decode_step ,
supervision ) :
cost_function = supervision
finished_states = self._get_finished_states(initial_state, decode_step)
loss = initial_state.score[0].new_zeros(1)
finished_model_scores = self._get_model_scores_by_batch(finished_states)
finished_costs = self._get_costs_by_batch(finished_states, cost_function)
for batch_index in finished_model_scores:
# Finished model scores are log-probabilities of the predicted sequences. We convert
# log probabilities into probabilities and re-normalize them to compute expected cost under
# the distribution approximated by the beam search.
costs = torch.cat([tensor.view(-1) for tensor in finished_costs[batch_index]])
logprobs = torch.cat([tensor.view(-1) for tensor in finished_model_scores[batch_index]])
# Unmasked softmax of log probabilities will convert them into probabilities and
# renormalize them.
renormalized_probs = nn_util.masked_softmax(logprobs, None)
loss += renormalized_probs.dot(costs)
mean_loss = loss / len(finished_model_scores)
return {u'loss': mean_loss,
u'best_action_sequences': self._get_best_action_sequences(finished_states)}
示例2: forward
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def forward(self, inputs: Tensor, memory: Tensor, memory_mask: Tensor):
if not self.batch_first:
inputs = inputs.transpose(0, 1)
memory = memory.transpose(0, 1)
memory_mask = memory_mask.transpose(0, 1)
input_ = self.input_linear(inputs)
memory_ = self.memory_linear(memory)
logits = torch.bmm(input_, memory_.transpose(2, 1)) / (self.attention_size ** 0.5)
memory_mask = memory_mask.unsqueeze(1).expand(-1, inputs.size(1), -1)
score = masked_softmax(logits, memory_mask, dim=-1)
context = torch.bmm(score, memory)
new_input = torch.cat([context, inputs], dim=-1)
if not self.batch_first:
return new_input.transpose(0, 1)
return new_input
示例3: forward
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def forward(
self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
span_indices_mask: torch.BoolTensor = None,
) -> torch.FloatTensor:
# shape (batch_size, sequence_length, 1)
global_attention_logits = self._global_attention(sequence_tensor)
# shape (batch_size, sequence_length, embedding_dim + 1)
concat_tensor = torch.cat([sequence_tensor, global_attention_logits], -1)
concat_output, span_mask = util.batched_span_select(concat_tensor, span_indices)
# Shape: (batch_size, num_spans, max_batch_span_width, embedding_dim)
span_embeddings = concat_output[:, :, :, :-1]
# Shape: (batch_size, num_spans, max_batch_span_width)
span_attention_logits = concat_output[:, :, :, -1]
# Shape: (batch_size, num_spans, max_batch_span_width)
span_attention_weights = util.masked_softmax(span_attention_logits, span_mask)
# Do a weighted sum of the embedded spans with
# respect to the normalised attention distributions.
# Shape: (batch_size, num_spans, embedding_dim)
attended_text_embeddings = util.weighted_sum(span_embeddings, span_attention_weights)
if span_indices_mask is not None:
# Above we were masking the widths of spans with respect to the max
# span width in the batch. Here we are masking the spans which were
# originally passed in as padding.
return attended_text_embeddings * span_indices_mask.unsqueeze(-1)
return attended_text_embeddings
示例4: forward
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def forward(
self, vector: torch.Tensor, matrix: torch.Tensor, matrix_mask: torch.BoolTensor = None
) -> torch.Tensor:
similarities = self._forward_internal(vector, matrix)
if self._normalize:
return masked_softmax(similarities, matrix_mask)
else:
return similarities
示例5: test_masked_softmax_no_mask
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def test_masked_softmax_no_mask(self):
# Testing the general unmasked 1D case.
vector_1d = torch.FloatTensor([[1.0, 2.0, 3.0]])
vector_1d_softmaxed = util.masked_softmax(vector_1d, None).data.numpy()
assert_array_almost_equal(
vector_1d_softmaxed, numpy.array([[0.090031, 0.244728, 0.665241]])
)
assert_almost_equal(1.0, numpy.sum(vector_1d_softmaxed), decimal=6)
vector_1d = torch.FloatTensor([[1.0, 2.0, 5.0]])
vector_1d_softmaxed = util.masked_softmax(vector_1d, None).data.numpy()
assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.017148, 0.046613, 0.93624]]))
# Testing the unmasked 1D case where the input is all 0s.
vector_zero = torch.FloatTensor([[0.0, 0.0, 0.0]])
vector_zero_softmaxed = util.masked_softmax(vector_zero, None).data.numpy()
assert_array_almost_equal(
vector_zero_softmaxed, numpy.array([[0.33333334, 0.33333334, 0.33333334]])
)
# Testing the general unmasked batched case.
matrix = torch.FloatTensor([[1.0, 2.0, 5.0], [1.0, 2.0, 3.0]])
masked_matrix_softmaxed = util.masked_softmax(matrix, None).data.numpy()
assert_array_almost_equal(
masked_matrix_softmaxed,
numpy.array(
[[0.01714783, 0.04661262, 0.93623955], [0.09003057, 0.24472847, 0.66524096]]
),
)
# Testing the unmasked batched case where one of the inputs are all 0s.
matrix = torch.FloatTensor([[1.0, 2.0, 5.0], [0.0, 0.0, 0.0]])
masked_matrix_softmaxed = util.masked_softmax(matrix, None).data.numpy()
assert_array_almost_equal(
masked_matrix_softmaxed,
numpy.array(
[[0.01714783, 0.04661262, 0.93623955], [0.33333334, 0.33333334, 0.33333334]]
),
)
示例6: forward
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def forward(self, # pylint: disable=arguments-differ
vector ,
matrix ,
matrix_mask = None) :
similarities = self._forward_internal(vector, matrix)
if self._normalize:
return masked_softmax(similarities, matrix_mask)
else:
return similarities
示例7: _get_next_state_info_with_agenda
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def _get_next_state_info_with_agenda(
state ,
considered_actions ,
action_logits ,
action_mask ):
u"""
We return a list of log probabilities and checklist states corresponding to next actions that are
not padding. This method is applicable to the case where we do not have target action
sequences and are relying on agendas for training.
"""
considered_action_probs = nn_util.masked_softmax(action_logits, action_mask)
# Mixing model scores and agenda selection probabilities to compute the probabilities of all
# actions for the next step and the corresponding new checklists.
# All action logprobs will keep track of logprob corresponding to each local action index
# for each instance.
all_action_logprobs = []
all_new_checklist_states = []
for group_index, instance_info in enumerate(izip(state.score,
considered_action_probs,
state.checklist_state)):
(instance_score, instance_probs, instance_checklist_state) = instance_info
# We will mix the model scores with agenda selection probabilities and compute their
# logs to fill the following list with action indices and corresponding logprobs.
instance_action_logprobs = []
instance_new_checklist_states = []
for action_index, action_prob in enumerate(instance_probs):
# This is the actual index of the action from the original list of actions.
action = considered_actions[group_index][action_index]
if action == -1:
# Ignoring padding.
continue
new_checklist_state = instance_checklist_state.update(action) # (terminal_actions, 1)
instance_new_checklist_states.append(new_checklist_state)
logprob = instance_score + torch.log(action_prob + 1e-13)
instance_action_logprobs.append((action_index, logprob))
all_action_logprobs.append(instance_action_logprobs)
all_new_checklist_states.append(instance_new_checklist_states)
return all_action_logprobs, all_new_checklist_states
示例8: test_masked_softmax_no_mask
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def test_masked_softmax_no_mask(self):
# Testing the general unmasked 1D case.
vector_1d = torch.FloatTensor([[1.0, 2.0, 3.0]])
vector_1d_softmaxed = util.masked_softmax(vector_1d, None).data.numpy()
assert_array_almost_equal(vector_1d_softmaxed,
numpy.array([[0.090031, 0.244728, 0.665241]]))
assert_almost_equal(1.0, numpy.sum(vector_1d_softmaxed), decimal=6)
vector_1d = torch.FloatTensor([[1.0, 2.0, 5.0]])
vector_1d_softmaxed = util.masked_softmax(vector_1d, None).data.numpy()
assert_array_almost_equal(vector_1d_softmaxed,
numpy.array([[0.017148, 0.046613, 0.93624]]))
# Testing the unmasked 1D case where the input is all 0s.
vector_zero = torch.FloatTensor([[0.0, 0.0, 0.0]])
vector_zero_softmaxed = util.masked_softmax(vector_zero, None).data.numpy()
assert_array_almost_equal(vector_zero_softmaxed,
numpy.array([[0.33333334, 0.33333334, 0.33333334]]))
# Testing the general unmasked batched case.
matrix = torch.FloatTensor([[1.0, 2.0, 5.0], [1.0, 2.0, 3.0]])
masked_matrix_softmaxed = util.masked_softmax(matrix, None).data.numpy()
assert_array_almost_equal(masked_matrix_softmaxed,
numpy.array([[0.01714783, 0.04661262, 0.93623955],
[0.09003057, 0.24472847, 0.66524096]]))
# Testing the unmasked batched case where one of the inputs are all 0s.
matrix = torch.FloatTensor([[1.0, 2.0, 5.0], [0.0, 0.0, 0.0]])
masked_matrix_softmaxed = util.masked_softmax(matrix, None).data.numpy()
assert_array_almost_equal(masked_matrix_softmaxed,
numpy.array([[0.01714783, 0.04661262, 0.93623955],
[0.33333334, 0.33333334, 0.33333334]]))
示例9: decode
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def decode(
self,
initial_state: State,
transition_function: TransitionFunction,
supervision: Callable[[StateType], torch.Tensor],
) -> Dict[str, torch.Tensor]:
cost_function = supervision
finished_states = self._get_finished_states(initial_state, transition_function)
loss = initial_state.score[0].new_zeros(1)
finished_model_scores = self._get_model_scores_by_batch(finished_states)
finished_costs = self._get_costs_by_batch(finished_states, cost_function)
for batch_index in finished_model_scores:
# Finished model scores are log-probabilities of the predicted sequences. We convert
# log probabilities into probabilities and re-normalize them to compute expected cost under
# the distribution approximated by the beam search.
costs = torch.cat([tensor.view(-1) for tensor in finished_costs[batch_index]])
logprobs = torch.cat([tensor.view(-1) for tensor in finished_model_scores[batch_index]])
# Unmasked softmax of log probabilities will convert them into probabilities and
# renormalize them.
renormalized_probs = nn_util.masked_softmax(logprobs, None)
loss += renormalized_probs.dot(costs)
mean_loss = loss / len(finished_model_scores)
return {
"loss": mean_loss,
"best_final_states": self._get_best_final_states(finished_states),
}
示例10: forward
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def forward(self,
vector: torch.Tensor,
matrix: torch.Tensor,
matrix_mask: torch.Tensor = None,
coverage: torch.Tensor = None) -> torch.Tensor:
similarities = self._forward_internal(vector, matrix, coverage)
if self._normalize:
return masked_softmax(similarities, matrix_mask)
else:
return similarities
示例11: _question_pooling
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def _question_pooling(self, question, question_mask):
V_q = self.V_q.expand(question.size(0), question.size(1), -1)
logits = self.question_linear(torch.cat([question, V_q], dim=-1)).squeeze(-1)
score = masked_softmax(logits, question_mask, dim=0)
state = torch.sum(score.unsqueeze(-1) * question, dim=0)
return state
示例12: _passage_attention
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def _passage_attention(self, passage, passage_mask, state):
state_expand = state.unsqueeze(0).expand(passage.size(0), -1, -1)
logits = self.passage_linear(torch.cat([passage, state_expand], dim=-1)).squeeze(-1)
score = masked_softmax(logits, passage_mask, dim=0)
cell_input = torch.sum(score.unsqueeze(-1) * passage, dim=0)
return cell_input, logits
示例13: forward
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def forward(self, inputs: Tensor, memory: Tensor = None, memory_mask: Tensor = None, state: Tensor = None):
"""
:param inputs: B x H
:param memory: T x B x H if not batch_first
:param memory_mask: T x B if not batch_first
:param state: B x H
:return:
"""
if self.batch_first:
memory = memory.transpose(0, 1)
memory_mask = memory_mask.transpose(0, 1)
assert inputs.size(0) == memory.size(1) == memory_mask.size(
1), "inputs batch size does not match memory batch size"
memory_time_length = memory.size(0)
if state is None:
state = inputs.new_zeros(inputs.size(0), self.cell.hidden_size, requires_grad=False)
if self.use_state:
hx = state
if isinstance(state, tuple):
hx = state[0]
attention_input = torch.cat([inputs, hx], dim=-1)
attention_input = attention_input.unsqueeze(0).expand(memory_time_length, -1, -1) # T B H
else:
attention_input = inputs.unsqueeze(0).expand(memory_time_length, -1, -1)
attention_logits = self.attention_w(torch.cat([attention_input, memory], dim=-1)).squeeze(-1)
attention_scores = masked_softmax(attention_logits, memory_mask, dim=0)
attention_vector = torch.sum(attention_scores.unsqueeze(-1) * memory, dim=0)
new_input = torch.cat([inputs, attention_vector], dim=-1)
return self.cell(new_input, state)
示例14: compute_location_spans
# 需要导入模块: from allennlp.nn import util [as 别名]
# 或者: from allennlp.nn.util import masked_softmax [as 别名]
def compute_location_spans(self, contextual_seq_embedding, embedded_sentence_verb_entity, mask):
# # ===============================================================test============================================
# # Layer 5: Span prediction for before and after location
# Shape: (batch_size, passage_length, encoding_dim * 4 + modeling_dim))
batch_size, num_sentences, num_participants, sentence_length, encoder_dim = contextual_seq_embedding.shape
#print("contextual_seq_embedding: ", contextual_seq_embedding.shape)
# size(span_start_input_after): batch_size * num_sentences *
# num_participants * sentence_length * (embedding_size+2+2*seq2seq_output_size)
span_start_input_after = torch.cat([embedded_sentence_verb_entity, contextual_seq_embedding], dim=-1)
#print("span_start_input_after: ", span_start_input_after.shape)
# Shape: (bs, ns , np, sl)
span_start_logits_after = self._span_start_predictor_after(span_start_input_after).squeeze(-1)
#print("span_start_logits_after: ", span_start_logits_after.shape)
# Shape: (bs, ns , np, sl)
span_start_probs_after = util.masked_softmax(span_start_logits_after, mask)
#print("span_start_probs_after: ", span_start_probs_after.shape)
# span_start_representation_after: (bs, ns , np, encoder_dim)
span_start_representation_after = util.weighted_sum(contextual_seq_embedding, span_start_probs_after)
#print("span_start_representation_after: ", span_start_representation_after.shape)
# span_tiled_start_representation_after: (bs, ns , np, sl, 2*seq2seq_output_size)
span_tiled_start_representation_after = span_start_representation_after.unsqueeze(3).expand(batch_size,
num_sentences,
num_participants,
sentence_length,
encoder_dim)
#print("span_tiled_start_representation_after: ", span_tiled_start_representation_after.shape)
# Shape: (batch_size, passage_length, (embedding+2 + encoder_dim + encoder_dim + encoder_dim))
span_end_representation_after = torch.cat([embedded_sentence_verb_entity,
contextual_seq_embedding,
span_tiled_start_representation_after,
contextual_seq_embedding * span_tiled_start_representation_after],
dim=-1)
#print("span_end_representation_after: ", span_end_representation_after.shape)
# Shape: (batch_size, passage_length, encoding_dim)
encoded_span_end_after = self.time_distributed_encoder_span_end_after(span_end_representation_after, mask)
#print("encoded_span_end_after: ", encoded_span_end_after.shape)
span_end_logits_after = self._span_end_predictor_after(encoded_span_end_after).squeeze(-1)
#print("span_end_logits_after: ", span_end_logits_after.shape)
span_end_probs_after = util.masked_softmax(span_end_logits_after, mask)
#print("span_end_probs_after: ", span_end_probs_after.shape)
span_start_logits_after = util.replace_masked_values(span_start_logits_after, mask, -1e7)
span_end_logits_after = util.replace_masked_values(span_end_logits_after, mask, -1e7)
# Fixme: we should condition this on predicted_action so that we can output '-' when needed
# Fixme: also add a functionality to be able to output '?': we can use span_start_probs_after, span_end_probs_after
best_span_after = self.get_best_span(span_start_logits_after, span_end_logits_after)
#print("best_span_after: ", best_span_after)
return best_span_after, span_start_logits_after, span_end_logits_after