使用TensorFlow2.0中的Keras实现基于BiLSTM-CRF的NER.zip

import tensorflow as tf def crf_decode(potentials, transition_params, sequence_length): """解码TensorFlow中标记的最高评分序列。 这是张量的函数。 Args: potentials: A [batch_size, max_seq_len, num_tags] tensor of unary potentials. transition_params: A [num_tags, num_tags] matrix of binary potentials. sequence_length: A [batch_size] vector of true sequence lengths. Returns: decode_tags: A [batch_size, max_seq_len] matrix, with dtype `tf.int32`. Contains the highest scoring tag indices. best_score: A [batch_size] vector, containing the score of `decode_tags`. """ sequence_length = tf.cast(sequence_length, dtype=tf.int32) # 如果max_seq_len为1，则跳过算法，只返回argmax标记和max activation。 def _single_seq_fn(): squeezed_potentials = tf.squeeze(potentials, [1]) decode_tags = tf.expand_dims(tf.argmax(squeezed_potentials, axis=1), 1) best_score = tf.reduce_max(squeezed_potentials, axis=1) return tf.cast(decode_tags, dtype=tf.int32), best_score def _multi_seq_fn(): """最高得分序列的解码。""" # Computes forward decoding. Get last score and backpointers. initial_state = tf.slice(potentials, [0, 0, 0], [-1, 1, -1]) initial_state = tf.squeeze(initial_state, axis=[1]) inputs = tf.slice(potentials, [0, 1, 0], [-1, -1, -1]) sequence_length_less_one = tf.maximum( tf.constant(0, dtype=sequence_length.dtype), sequence_length - 1) backpointers, last_score = crf_decode_forward( inputs, initial_state, transition_params, sequence_length_less_one) backpointers = tf.reverse_sequence( backpointers, sequence_length_less_one, seq_axis=1) initial_state = tf.cast(tf.argmax(last_score, axis=1), dtype=tf.int32) initial_state = tf.expand_dims(initial_state, axis=-1) decode_tags = crf_decode_backward(backpointers, initial_state) decode_tags = tf.squeeze(decode_tags, axis=[2]) decode_tags = tf.concat([initial_state, decode_tags], axis=1) decode_tags = tf.reverse_sequence( decode_tags, sequence_length, seq_axis=1) best_score = tf.reduce_max(last_score, axis=1) return decode_tags, best_score if potentials.shape[1] == 1: return _single_seq_fn() else: return _multi_seq_fn() def crf_decode_forward(inputs, state, transition_params, sequence_lengths): """计算线性链CRF中的正向解码。 Args: inputs: A [batch_size, num_tags] matrix of unary potentials. state: A [batch_size, num_tags] matrix containing the previous step's score values. transition_params: A [num_tags, num_tags] matrix of binary potentials. sequence_lengths: A [batch_size] vector of true sequence lengths. Returns: backpointers: A [batch_size, num_tags] matrix of backpointers. new_state: A [batch_size, num_tags] matrix of new score values. """ sequence_lengths = tf.cast(sequence_lengths, dtype=tf.int32) mask = tf.sequence_mask(sequence_lengths, tf.shape(inputs)[1]) crf_fwd_cell = CrfDecodeForwardRnnCell(transition_params) crf_fwd_layer = tf.keras.layers.RNN( crf_fwd_cell, return_sequences=True, return_state=True) return crf_fwd_layer(inputs, state, mask=mask) def crf_decode_backward(inputs, state): """计算线性链CRF中的反向解码。 Args: inputs: A [batch_size, num_tags] matrix of backpointer of next step (in time order). state: A [batch_size, 1] matrix of tag index of next step. Returns: new_tags: A [batch_size, num_tags] tensor containing the new tag indices. """ inputs = tf.transpose(inputs, [1, 0, 2]) def _scan_fn(state, inputs): state = tf.squeeze(state, axis=[1]) idxs = tf.stack([tf.range(tf.shape(inputs)[0]), state], axis=1) new_tags = tf.expand_dims(tf.gather_nd(inputs, idxs), axis=-1) return new_tags return tf.transpose(tf.scan(_scan_fn, inputs, state), [1, 0, 2]) class CrfDecodeForwardRnnCell(tf.keras.layers.AbstractRNNCell): """计算线性链CRF中的正向解码。""" def __init__(self, transition_params, **kwargs): """初始化CrfDecodeForwardRnnCell。 Args: transition_params: A [num_tags, num_tags] matrix of binary potentials. 这个矩阵将被扩展为[1, num_tags, num_tags] 以在下面的cell中进行广播求和。 """ super(CrfDecodeForwardRnnCell, self).__init__(**kwargs) self._transition_params = tf.expand_dims(transition_params, 0) self._num_tags = transition_params.shape[0] @property def state_size(self): return self._num_tags @property def output_size(self): return self._num_tags def build(self, input_shape): super(CrfDecodeForwardRnnCell, self).build(input_shape) def call(self, inputs, state): """构建CrfDecodeForwardRnnCell。 Args: inputs: A [batch_size, num_tags] matrix of unary potentials. state: A [batch_size, num_tags] matrix containing the previous step's score values. Returns: backpointers: A [batch_size, num_tags] matrix of backpointers. new_state: A [batch_size, num_tags] matrix of new score values. """ state = tf.expand_dims(state[0], 2) transition_scores = state + self._transition_params new_state = inputs + tf.reduce_max(transition_scores, [1]) backpointers = tf.argmax(transition_scores, 1) backpointers = tf.cast(backpointers, dtype=tf.int32) return backpointers, new_state def crf_log_likelihood(inputs, tag_indices, sequence_lengths, transition_params=None): """计算CRF中标记序列的对数似然。 通过crf_sequence_score计算状态序列可能性分数，通过crf_log_norm计算归一化项。 最后返回log_likelihood对数似然。 Args: inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer. tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we compute the log-likelihood. sequence_lengths: A [batch_size] vector of true sequence lengths. transition_params: A [num_tags, num_tags] transition matrix, if available. Returns: log_likelihood: A [batch_size] `Tensor` containing the log-likelihood of each example, given the sequence of tag indices. transition_params: A [num_tags, num_tags] transition matrix. This is either provided by the caller or created in this function. """ num_tags = inputs.shape[2] tag_indices = tf.cast(tag_indices, dtype=tf.int32) sequence_lengths = tf.cast(sequence_lengths, dtype=tf.int32) if transition_params is None: initializer = tf.keras.initializers.GlorotUniform() transition_params = tf.Variable( initializer([num_tags, num_tags]), "transitions") sequence_scores = crf_sequence_score( inputs, tag_indices, sequence_lengths, transition_params) log_norm = crf_log_norm(inputs, sequence_lengths, transition_params) # Normalize the scores to get the log-likelihood per example. log_likelihood = sequence_scores - log_norm return log_likelihood, transition_params def crf_sequence_score(inputs, tag_indices, sequence_lengths, transition_params): """计算标记序列的非标准化分数。 通过crf_unary_score计算状态特征分数，通过crf_binary_score计算转移特征分数。 Args: inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer. tag_indices: A [batch