我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用tensorflow.unique()。
def find_dup(a): """ Find the duplicated elements in 1-D a tensor. Args: a: 1-D tensor. Return: more_than_one_vals: duplicated value in a. indexes_in_a: duplicated value's index in a. dups_in_a: duplicated value with duplicate in a. """ unique_a_vals, unique_idx = tf.unique(a) count_a_unique = tf.unsorted_segment_sum(tf.ones_like(a), unique_idx, tf.shape(a)[0]) more_than_one = tf.greater(count_a_unique, 1) more_than_one_idx = tf.squeeze(tf.where(more_than_one)) more_than_one_vals = tf.squeeze(tf.gather(unique_a_vals, more_than_one_idx)) not_duplicated, _ = tf.setdiff1d(a, more_than_one_vals) dups_in_a, indexes_in_a = tf.setdiff1d(a, not_duplicated) return more_than_one_vals, indexes_in_a, dups_in_a
def predict(self, answer, start_logits, end_logits, mask) -> Prediction: masked_start_logits = exp_mask(start_logits, mask) masked_end_logits = exp_mask(end_logits, mask) if len(answer) == 3: group_ids = answer[2] # Turn the ids into segment ids using tf.unique _, group_segments = tf.unique(group_ids, out_idx=tf.int32) losses = [] for answer_mask, logits in zip(answer, [masked_start_logits, masked_end_logits]): group_norms = segment_logsumexp(logits, group_segments) if self.aggregate == "sum": log_score = segment_logsumexp(logits + VERY_NEGATIVE_NUMBER * (1 - tf.cast(answer_mask, tf.float32)), group_segments) else: raise ValueError() losses.append(tf.reduce_mean(-(log_score - group_norms))) loss = tf.add_n(losses) else: raise NotImplemented() tf.add_to_collection(tf.GraphKeys.LOSSES, loss) return BoundaryPrediction(tf.nn.softmax(masked_start_logits), tf.nn.softmax(masked_end_logits), masked_start_logits, masked_end_logits, mask)
def unique(x, return_index=False): """ Find the unique elements of an array. Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements: the indices of the input array that give the unique values, the indices of the unique array that reconstruct the input array, and the number of times each unique value comes up in the input array. """ y, indices = tensorflow.unique(x) if return_index: return y, indices else: return y
def embedding_lookup_sparse_sumexp(params, sp_ids, name=None): segment_ids = sp_ids.indices[:, 0] if segment_ids.dtype != tf.int32: segment_ids = tf.cast(segment_ids, tf.int32) ids = sp_ids.values ids, idx = tf.unique(ids) embeddings = tf.nn.embedding_lookup(params, ids) embeddings = tf.exp(embeddings) embeddings = tf.sparse_segment_sum(embeddings, idx, segment_ids, name=name) return embeddings
def _apply_sparse(self, cache): """""" x_tm1, g_t, idxs = cache['x_tm1'], cache['g_t'], cache['idxs'] idxs, idxs_ = tf.unique(idxs) g_t_ = tf.unsorted_segment_sum(g_t, idxs_, tf.size(idxs)) updates = cache['updates'] if self.mu > 0: m_t, t_m = self._sparse_moving_average(x_tm1, idxs, g_t_, 'm', beta=self.mu) m_t_ = tf.gather(m_t, idxs) m_bar_t_ = (1-self.gamma) * m_t_ + self.gamma * g_t_ updates.extend([m_t, t_m]) else: m_bar_t_ = g_t_ if self.nu > 0: v_t, t_v = self._sparse_moving_average(x_tm1, idxs, g_t_**2, 'v', beta=self.nu) v_t_ = tf.gather(v_t, idxs) v_bar_t_ = tf.sqrt(v_t_ + self.epsilon) updates.extend([v_t, t_v]) else: v_bar_t_ = 1 s_t_ = self.learning_rate * m_bar_t_ / v_bar_t_ cache['s_t'] = s_t_ cache['g_t'] = g_t_ cache['idxs'] = idxs return cache
def _apply_sparse(self, cache): """""" g_t, idxs = cache['g_t'], cache['idxs'] idxs, idxs_ = tf.unique(idxs) g_t_ = tf.unsorted_segment_sum(g_t, idxs_, tf.size(idxs)) cache['g_t'] = g_t_ cache['idxs'] = idxs cache['s_t'] = self.learning_rate * g_t_ return cache
def query_once(self) -> bool: """ Should the embedder be queried once for each unique word in the input, or once for each word. Intended to support placeholders, although I ended up not experimenting much w/that route """ return False
def shrink_embed(mat, word_ixs: List): """ Build an embedding matrix that contains only the elements in `word_ixs`, and map `word_ixs` to tensors the index into they new embedding matrix. Useful if you want to dropout the embeddings w/o dropping out the entire matrix """ all_words, out_id = tf.unique(tf.concat([tf.reshape(x, (-1,)) for x in word_ixs], axis=0)) mat = tf.gather(mat, all_words) partitions = tf.split(out_id, [tf.reduce_prod(tf.shape(x)) for x in word_ixs]) partitions = [tf.reshape(x, tf.shape(o)) for x,o in zip(partitions, word_ixs)] return mat, partitions
def _apply_sparse(self, cache): """""" x_tm1, g_t, idxs = cache['x_tm1'], cache['g_t'], cache['idxs'] idxs, idxs_ = tf.unique(idxs) g_t_ = tf.unsorted_segment_sum(g_t, idxs_, tf.size(idxs)) updates = cache['updates'] if self.mu > 0: m_t, t_m = self._sparse_moving_average(x_tm1, idxs, g_t_, 'm', beta=self.mu) m_t_ = tf.gather(m_t, idxs) m_bar_t_ = (1-self.gamma) * m_t_ + self.gamma * g_t_ updates.extend([m_t, t_m]) else: m_bar_t_ = g_t_ if self.nu > 0: v_t, t_v = self._sparse_moving_average(x_tm1, idxs, g_t_**2, 'v', beta=self.nu) v_t_ = tf.gather(v_t, idxs) v_bar_t_ = tf.sqrt(v_t_ + self.epsilon) updates.extend([v_t, t_v]) else: v_bar_t_ = 1 s_t_ = self.learning_rate * m_bar_t_ / v_bar_t_ cache['s_t'] = tf.where(tf.is_finite(s_t_), s_t_, tf.zeros_like(s_t_)) cache['g_t'] = g_t_ cache['idxs'] = idxs return cache
def test_Unique(self): t = tf.unique([9, 3, 5, 7, 3, 9, 9], out_idx=tf.int32) self.check(t)
