794 lines
36 KiB
Python
794 lines
36 KiB
Python
# -*- coding: UTF-8 -*-
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# !/usr/bin/python
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# @time :2019/5/10 10:49
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# @author :Mo
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# @function : 1. create model of keras-bert for get [-2] layers
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# 2. create model of AttentionWeightedAverage for get avg attention pooling
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# 3. create layer of
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# code class NonMaskingLayer from https://github.com/jacoxu
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# code class AttentionWeightedAverage from https://github.com/BrikerMan/Kashgari
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# code class CRF most from https://github.com/keras-team/keras-contrib, a little of 'theano' from https://github.com/BrikerMan/Kashgari
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from __future__ import absolute_import
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from __future__ import division
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from keras.engine import InputSpec
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import keras.backend as k_keras
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from keras.engine import Layer
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from keras import initializers
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from keras import backend as K
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from keras import regularizers
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from keras import activations
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from keras import constraints
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import warnings
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import os
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# crf_loss
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from keras.losses import sparse_categorical_crossentropy
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from keras.losses import categorical_crossentropy
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class NonMaskingLayer(Layer):
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"""
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fix convolutional 1D can't receive masked input, detail: https://github.com/keras-team/keras/issues/4978
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thanks for https://github.com/jacoxu
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"""
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def __init__(self, **kwargs):
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self.supports_masking = True
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super(NonMaskingLayer, self).__init__(**kwargs)
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def build(self, input_shape):
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pass
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def compute_mask(self, input, input_mask=None):
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# do not pass the mask to the next layers
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return None
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def call(self, x, mask=None):
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return x
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def compute_output_shape(self, input_shape):
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return input_shape
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class AttentionWeightedAverage(Layer):
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'''
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codes from: https://github.com/BrikerMan/Kashgari
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detail: https://github.com/BrikerMan/Kashgari/blob/master/kashgari/tasks/classification/models.py
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Computes a weighted average of the different channels across timesteps.
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Uses 1 parameter pr. channel to compute the attention value for a single timestep.
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'''
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def __init__(self, return_attention=False, **kwargs):
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self.init = initializers.get('uniform')
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self.supports_masking = True
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self.return_attention = return_attention
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super(AttentionWeightedAverage, self).__init__(**kwargs)
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def build(self, input_shape):
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self.input_spec = [InputSpec(ndim=3)]
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assert len(input_shape) == 3
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self.W = self.add_weight(shape=(input_shape[2], 1),
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name='{}_w'.format(self.name),
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initializer=self.init)
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self.trainable_weights = [self.W]
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super(AttentionWeightedAverage, self).build(input_shape)
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def call(self, x, mask=None):
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# computes a probability distribution over the timesteps
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# uses 'max trick' for numerical stability
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# reshape is done to avoid issue with Tensorflow
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# and 1-dimensional weights
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logits = k_keras.dot(x, self.W)
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x_shape = k_keras.shape(x)
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logits = k_keras.reshape(logits, (x_shape[0], x_shape[1]))
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ai = k_keras.exp(logits - k_keras.max(logits, axis=-1, keepdims=True))
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# masked timesteps have zero weight
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if mask is not None:
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mask = k_keras.cast(mask, k_keras.floatx())
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ai = ai * mask
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att_weights = ai / (k_keras.sum(ai, axis=1, keepdims=True) + k_keras.epsilon())
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weighted_input = x * k_keras.expand_dims(att_weights)
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result = k_keras.sum(weighted_input, axis=1)
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if self.return_attention:
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return [result, att_weights]
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return result
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def get_output_shape_for(self, input_shape):
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return self.compute_output_shape(input_shape)
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def compute_output_shape(self, input_shape):
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output_len = input_shape[2]
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if self.return_attention:
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return [(input_shape[0], output_len), (input_shape[0], input_shape[1])]
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return (input_shape[0], output_len)
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def compute_mask(self, input, input_mask=None):
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if isinstance(input_mask, list):
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return [None] * len(input_mask)
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else:
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return None
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# crf_loss
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def crf_nll(y_true, y_pred):
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"""The negative log-likelihood for linear chain Conditional Random Field (CRF).
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This loss function is only used when the `layers.CRF` layer
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is trained in the "join" mode.
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# Arguments
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y_true: tensor with true targets.
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y_pred: tensor with predicted targets.
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# Returns
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A scalar representing corresponding to the negative log-likelihood.
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# Raises
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TypeError: If CRF is not the last layer.
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# About GitHub
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If you open an issue or a pull request about CRF, please
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add `cc @lzfelix` to notify Luiz Felix.
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"""
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crf, idx = y_pred._keras_history[:2]
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if crf._outbound_nodes:
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raise TypeError('When learn_model="join", CRF must be the last layer.')
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if crf.sparse_target:
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y_true = K.one_hot(K.cast(y_true[:, :, 0], 'int32'), crf.units)
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X = crf._inbound_nodes[idx].input_tensors[0]
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mask = crf._inbound_nodes[idx].input_masks[0]
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nloglik = crf.get_negative_log_likelihood(y_true, X, mask)
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# 新加的
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# nloglik = k_keras.abs(nloglik)
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return nloglik
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def crf_loss(y_true, y_pred):
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"""General CRF loss function depending on the learning mode.
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# Arguments
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y_true: tensor with true targets.
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y_pred: tensor with predicted targets.
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# Returns
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If the CRF layer is being trained in the join mode, returns the negative
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log-likelihood. Otherwise returns the categorical crossentropy implemented
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by the underlying Keras backend.
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# About GitHub
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If you open an issue or a pull request about CRF, please
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add `cc @lzfelix` to notify Luiz Felix.
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"""
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crf, idx = y_pred._keras_history[:2]
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if crf.learn_mode == 'join':
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return crf_nll(y_true, y_pred)
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else:
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if crf.sparse_target:
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return sparse_categorical_crossentropy(y_true, y_pred)
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else:
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return categorical_crossentropy(y_true, y_pred)
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# crf_marginal_accuracy, crf_viterbi_accuracy
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def _get_accuracy(y_true, y_pred, mask, sparse_target=False):
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"""
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:param y_true:
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:param y_pred:
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:param mask:
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:param sparse_target:
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:return:
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"""
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y_pred = K.argmax(y_pred, -1)
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if sparse_target:
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y_true = K.cast(y_true[:, :, 0], K.dtype(y_pred))
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else:
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y_true = K.argmax(y_true, -1)
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judge = K.cast(K.equal(y_pred, y_true), K.floatx())
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if mask is None:
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return K.mean(judge)
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else:
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mask = K.cast(mask, K.floatx())
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return K.sum(judge * mask) / K.sum(mask)
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def crf_viterbi_accuracy(y_true, y_pred):
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'''Use Viterbi algorithm to get best path, and compute its accuracy.
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`y_pred` must be an output from CRF.'''
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crf, idx = y_pred._keras_history[:2]
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X = crf._inbound_nodes[idx].input_tensors[0]
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mask = crf._inbound_nodes[idx].input_masks[0]
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y_pred = crf.viterbi_decoding(X, mask)
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return _get_accuracy(y_true, y_pred, mask, crf.sparse_target)
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def crf_marginal_accuracy(y_true, y_pred):
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'''Use time-wise marginal argmax as prediction.
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`y_pred` must be an output from CRF with `learn_mode="marginal"`.'''
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crf, idx = y_pred._keras_history[:2]
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X = crf._inbound_nodes[idx].input_tensors[0]
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mask = crf._inbound_nodes[idx].input_masks[0]
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y_pred = crf.get_marginal_prob(X, mask)
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return _get_accuracy(y_true, y_pred, mask, crf.sparse_target)
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def crf_accuracy(y_true, y_pred):
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'''Ge default accuracy based on CRF `test_mode`.'''
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crf, idx = y_pred._keras_history[:2]
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if crf.test_mode == 'viterbi':
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return crf_viterbi_accuracy(y_true, y_pred)
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else:
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return crf_marginal_accuracy(y_true, y_pred)
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def to_tuple(shape):
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"""This functions is here to fix an inconsistency between keras and tf.keras.
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In tf.keras, the input_shape argument is an tuple with `Dimensions` objects.
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In keras, the input_shape is a simple tuple of ints or `None`.
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We'll work with tuples of ints or `None` to be consistent
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with keras-team/keras. So we must apply this function to
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all input_shapes of the build methods in custom layers.
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"""
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if os.environ.get("TF_KERAS")==1:
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import tensorflow as tf
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return tuple(tf.TensorShape(shape).as_list())
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else:
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return shape
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class CRF(Layer):
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"""
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codes from: https://github.com/keras-team/keras-contrib
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detail: https://github.com/keras-team/keras-contrib/blob/fff264273d5347613574ff533c598f55f15d4763/keras_contrib/layers/crf.py
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An implementation of linear chain conditional random field (CRF).
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An linear chain CRF is defined to maximize the following likelihood function:
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$$ L(W, U, b; y_1, ..., y_n) := \frac{1}{Z}
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\sum_{y_1, ..., y_n} \exp(-a_1' y_1 - a_n' y_n
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- \sum_{k=1^n}((f(x_k' W + b) y_k) + y_1' U y_2)), $$
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where:
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$Z$: normalization constant
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$x_k, y_k$: inputs and outputs
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This implementation has two modes for optimization:
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1. (`join mode`) optimized by maximizing join likelihood,
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which is optimal in theory of statistics.
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Note that in this case, CRF must be the output/last layer.
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2. (`marginal mode`) return marginal probabilities on each time
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step and optimized via composition
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likelihood (product of marginal likelihood), i.e.,
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using `categorical_crossentropy` loss.
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Note that in this case, CRF can be either the last layer or an
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intermediate layer (though not explored).
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For prediction (test phrase), one can choose either Viterbi
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best path (class indices) or marginal
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probabilities if probabilities are needed.
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However, if one chooses *join mode* for training,
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Viterbi output is typically better than marginal output,
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but the marginal output will still perform
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reasonably close, while if *marginal mode* is used for training,
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marginal output usually performs
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much better. The default behavior and `metrics.crf_accuracy`
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is set according to this observation.
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In addition, this implementation supports masking and accepts either
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onehot or sparse target.
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If you open a issue or a pull request about CRF, please
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add 'cc @lzfelix' to notify Luiz Felix.
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# Examples
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```python
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from keras_contrib.layers import CRF
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from keras_contrib.losses import crf_loss
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from keras_contrib.metrics import crf_viterbi_accuracy
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model = Sequential()
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model.add(Embedding(3001, 300, mask_zero=True)(X)
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# use learn_mode = 'join', test_mode = 'viterbi',
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# sparse_target = True (label indice output)
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crf = CRF(10, sparse_target=True)
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model.add(crf)
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# crf_accuracy is default to Viterbi acc if using join-mode (default).
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# One can add crf.marginal_acc if interested, but may slow down learning
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model.compile('adam', loss=crf_loss, metrics=[crf_viterbi_accuracy])
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# y must be label indices (with shape 1 at dim 3) here,
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# since `sparse_target=True`
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model.fit(x, y)
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# prediction give onehot representation of Viterbi best path
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y_hat = model.predict(x_test)
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```
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The following snippet shows how to load a persisted
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model that uses the CRF layer:
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```python
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from keras.models import load_model
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from keras_contrib.losses import import crf_loss
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from keras_contrib.metrics import crf_viterbi_accuracy
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custom_objects={'CRF': CRF,
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'crf_loss': crf_loss,
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'crf_viterbi_accuracy': crf_viterbi_accuracy}
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loaded_model = load_model('<path_to_model>',
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custom_objects=custom_objects)
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```
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# Arguments
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units: Positive integer, dimensionality of the output space.
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learn_mode: Either 'join' or 'marginal'.
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The former train the model by maximizing join likelihood while the latter
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maximize the product of marginal likelihood over all time steps.
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One should use `losses.crf_nll` for 'join' mode
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and `losses.categorical_crossentropy` or
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`losses.sparse_categorical_crossentropy` for
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`marginal` mode. For convenience, simply
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use `losses.crf_loss`, which will decide the proper loss as described.
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test_mode: Either 'viterbi' or 'marginal'.
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The former is recommended and as default when `learn_mode = 'join'` and
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gives one-hot representation of the best path at test (prediction) time,
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while the latter is recommended and chosen as default
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when `learn_mode = 'marginal'`,
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which produces marginal probabilities for each time step.
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For evaluating metrics, one should
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use `metrics.crf_viterbi_accuracy` for 'viterbi' mode and
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'metrics.crf_marginal_accuracy' for 'marginal' mode, or
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simply use `metrics.crf_accuracy` for
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both which automatically decides it as described.
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One can also use both for evaluation at training.
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sparse_target: Boolean (default False) indicating
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if provided labels are one-hot or
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indices (with shape 1 at dim 3).
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use_boundary: Boolean (default True) indicating if trainable
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start-end chain energies
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should be added to model.
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use_bias: Boolean, whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix,
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used for the linear transformation of the inputs.
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(see [initializers](../initializers.md)).
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chain_initializer: Initializer for the `chain_kernel` weights matrix,
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used for the CRF chain energy.
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(see [initializers](../initializers.md)).
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boundary_initializer: Initializer for the `left_boundary`,
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'right_boundary' weights vectors,
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used for the start/left and end/right boundary energy.
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(see [initializers](../initializers.md)).
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bias_initializer: Initializer for the bias vector
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(see [initializers](../initializers.md)).
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activation: Activation function to use
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(see [activations](../activations.md)).
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If you pass None, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix
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(see [regularizer](../regularizers.md)).
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chain_regularizer: Regularizer function applied to
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the `chain_kernel` weights matrix
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(see [regularizer](../regularizers.md)).
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boundary_regularizer: Regularizer function applied to
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the 'left_boundary', 'right_boundary' weight vectors
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(see [regularizer](../regularizers.md)).
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bias_regularizer: Regularizer function applied to the bias vector
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(see [regularizer](../regularizers.md)).
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kernel_constraint: Constraint function applied to
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the `kernel` weights matrix
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(see [constraints](../constraints.md)).
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chain_constraint: Constraint function applied to
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the `chain_kernel` weights matrix
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(see [constraints](../constraints.md)).
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boundary_constraint: Constraint function applied to
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the `left_boundary`, `right_boundary` weights vectors
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(see [constraints](../constraints.md)).
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bias_constraint: Constraint function applied to the bias vector
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(see [constraints](../constraints.md)).
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input_dim: dimensionality of the input (integer).
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This argument (or alternatively, the keyword argument `input_shape`)
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is required when using this layer as the first layer in a model.
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unroll: Boolean (default False). If True, the network will be
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unrolled, else a symbolic loop will be used.
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Unrolling can speed-up a RNN, although it tends
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to be more memory-intensive.
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Unrolling is only suitable for short sequences.
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# Input shape
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3D tensor with shape `(nb_samples, timesteps, input_dim)`.
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# Output shape
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3D tensor with shape `(nb_samples, timesteps, units)`.
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# Masking
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This layer supports masking for input data with a variable number
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of timesteps. To introduce masks to your data,
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use an [Embedding](embeddings.md) layer with the `mask_zero` parameter
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set to `True`.
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"""
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def __init__(self, units,
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learn_mode='join',
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test_mode=None,
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sparse_target=False,
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use_boundary=True,
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use_bias=True,
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activation='linear',
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kernel_initializer='glorot_uniform',
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chain_initializer='orthogonal',
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bias_initializer='zeros',
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boundary_initializer='zeros',
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kernel_regularizer=None,
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chain_regularizer=None,
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boundary_regularizer=None,
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bias_regularizer=None,
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kernel_constraint=None,
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chain_constraint=None,
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boundary_constraint=None,
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bias_constraint=None,
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input_dim=None,
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unroll=False,
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**kwargs):
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super(CRF, self).__init__(**kwargs)
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self.supports_masking = True
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self.units = units
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self.learn_mode = learn_mode
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assert self.learn_mode in ['join', 'marginal']
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self.test_mode = test_mode
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if self.test_mode is None:
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self.test_mode = 'viterbi' if self.learn_mode == 'join' else 'marginal'
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else:
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assert self.test_mode in ['viterbi', 'marginal']
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self.sparse_target = sparse_target
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self.use_boundary = use_boundary
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self.use_bias = use_bias
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self.activation = activations.get(activation)
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.chain_initializer = initializers.get(chain_initializer)
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self.boundary_initializer = initializers.get(boundary_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.chain_regularizer = regularizers.get(chain_regularizer)
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self.boundary_regularizer = regularizers.get(boundary_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.chain_constraint = constraints.get(chain_constraint)
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self.boundary_constraint = constraints.get(boundary_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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self.unroll = unroll
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def build(self, input_shape):
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# input_shape = to_tuple(input_shape)
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self.input_spec = [InputSpec(shape=input_shape)]
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self.input_dim = input_shape[-1]
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|
|
self.kernel = self.add_weight(shape=(self.input_dim, self.units),
|
|
name='kernel',
|
|
initializer=self.kernel_initializer,
|
|
regularizer=self.kernel_regularizer,
|
|
constraint=self.kernel_constraint)
|
|
self.chain_kernel = self.add_weight(shape=(self.units, self.units),
|
|
name='chain_kernel',
|
|
initializer=self.chain_initializer,
|
|
regularizer=self.chain_regularizer,
|
|
constraint=self.chain_constraint)
|
|
if self.use_bias:
|
|
self.bias = self.add_weight(shape=(self.units,),
|
|
name='bias',
|
|
initializer=self.bias_initializer,
|
|
regularizer=self.bias_regularizer,
|
|
constraint=self.bias_constraint)
|
|
else:
|
|
self.bias = 0
|
|
|
|
if self.use_boundary:
|
|
self.left_boundary = self.add_weight(shape=(self.units,),
|
|
name='left_boundary',
|
|
initializer=self.boundary_initializer,
|
|
regularizer=self.boundary_regularizer,
|
|
constraint=self.boundary_constraint)
|
|
self.right_boundary = self.add_weight(shape=(self.units,),
|
|
name='right_boundary',
|
|
initializer=self.boundary_initializer,
|
|
regularizer=self.boundary_regularizer,
|
|
constraint=self.boundary_constraint)
|
|
self.built = True
|
|
|
|
def call(self, X, mask=None):
|
|
if mask is not None:
|
|
assert K.ndim(mask) == 2, 'Input mask to CRF must have dim 2 if not None'
|
|
|
|
if self.test_mode == 'viterbi':
|
|
test_output = self.viterbi_decoding(X, mask)
|
|
else:
|
|
test_output = self.get_marginal_prob(X, mask)
|
|
|
|
self.uses_learning_phase = True
|
|
if self.learn_mode == 'join':
|
|
train_output = K.zeros_like(K.dot(X, self.kernel))
|
|
out = K.in_train_phase(train_output, test_output)
|
|
else:
|
|
if self.test_mode == 'viterbi':
|
|
train_output = self.get_marginal_prob(X, mask)
|
|
out = K.in_train_phase(train_output, test_output)
|
|
else:
|
|
out = test_output
|
|
return out
|
|
|
|
def compute_output_shape(self, input_shape):
|
|
return input_shape[:2] + (self.units,)
|
|
|
|
def compute_mask(self, input, mask=None):
|
|
if mask is not None and self.learn_mode == 'join':
|
|
return K.any(mask, axis=1)
|
|
return mask
|
|
|
|
def get_config(self):
|
|
config = {
|
|
'units': self.units,
|
|
'learn_mode': self.learn_mode,
|
|
'test_mode': self.test_mode,
|
|
'use_boundary': self.use_boundary,
|
|
'use_bias': self.use_bias,
|
|
'sparse_target': self.sparse_target,
|
|
'kernel_initializer': initializers.serialize(self.kernel_initializer),
|
|
'chain_initializer': initializers.serialize(self.chain_initializer),
|
|
'boundary_initializer': initializers.serialize(
|
|
self.boundary_initializer),
|
|
'bias_initializer': initializers.serialize(self.bias_initializer),
|
|
'activation': activations.serialize(self.activation),
|
|
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
|
|
'chain_regularizer': regularizers.serialize(self.chain_regularizer),
|
|
'boundary_regularizer': regularizers.serialize(
|
|
self.boundary_regularizer),
|
|
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
|
|
'kernel_constraint': constraints.serialize(self.kernel_constraint),
|
|
'chain_constraint': constraints.serialize(self.chain_constraint),
|
|
'boundary_constraint': constraints.serialize(self.boundary_constraint),
|
|
'bias_constraint': constraints.serialize(self.bias_constraint),
|
|
'input_dim': self.input_dim,
|
|
'unroll': self.unroll}
|
|
base_config = super(CRF, self).get_config()
|
|
return dict(list(base_config.items()) + list(config.items()))
|
|
|
|
@property
|
|
def loss_function(self):
|
|
warnings.warn('CRF.loss_function is deprecated '
|
|
'and it might be removed in the future. Please '
|
|
'use losses.crf_loss instead.')
|
|
return crf_loss
|
|
|
|
@property
|
|
def accuracy(self):
|
|
warnings.warn('CRF.accuracy is deprecated and it '
|
|
'might be removed in the future. Please '
|
|
'use metrics.crf_accuracy')
|
|
if self.test_mode == 'viterbi':
|
|
return crf_viterbi_accuracy
|
|
else:
|
|
return crf_marginal_accuracy
|
|
|
|
@property
|
|
def viterbi_acc(self):
|
|
warnings.warn('CRF.viterbi_acc is deprecated and it might '
|
|
'be removed in the future. Please '
|
|
'use metrics.viterbi_acc instead.')
|
|
return crf_viterbi_accuracy
|
|
|
|
@property
|
|
def marginal_acc(self):
|
|
warnings.warn('CRF.moarginal_acc is deprecated and it '
|
|
'might be removed in the future. Please '
|
|
'use metrics.marginal_acc instead.')
|
|
return crf_marginal_accuracy
|
|
|
|
@staticmethod
|
|
def softmaxNd(x, axis=-1):
|
|
m = K.max(x, axis=axis, keepdims=True)
|
|
exp_x = K.exp(x - m)
|
|
prob_x = exp_x / K.sum(exp_x, axis=axis, keepdims=True)
|
|
return prob_x
|
|
|
|
@staticmethod
|
|
def shift_left(x, offset=1):
|
|
assert offset > 0
|
|
return K.concatenate([x[:, offset:], K.zeros_like(x[:, :offset])], axis=1)
|
|
|
|
@staticmethod
|
|
def shift_right(x, offset=1):
|
|
assert offset > 0
|
|
return K.concatenate([K.zeros_like(x[:, :offset]), x[:, :-offset]], axis=1)
|
|
|
|
def add_boundary_energy(self, energy, mask, start, end):
|
|
start = K.expand_dims(K.expand_dims(start, 0), 0)
|
|
end = K.expand_dims(K.expand_dims(end, 0), 0)
|
|
if mask is None:
|
|
energy = K.concatenate([energy[:, :1, :] + start, energy[:, 1:, :]],
|
|
axis=1)
|
|
energy = K.concatenate([energy[:, :-1, :], energy[:, -1:, :] + end],
|
|
axis=1)
|
|
else:
|
|
mask = K.expand_dims(K.cast(mask, K.floatx()))
|
|
start_mask = K.cast(K.greater(mask, self.shift_right(mask)), K.floatx())
|
|
end_mask = K.cast(K.greater(self.shift_left(mask), mask), K.floatx())
|
|
energy = energy + start_mask * start
|
|
energy = energy + end_mask * end
|
|
return energy
|
|
|
|
def get_log_normalization_constant(self, input_energy, mask, **kwargs):
|
|
"""Compute logarithm of the normalization constant Z, where
|
|
Z = sum exp(-E) -> logZ = log sum exp(-E) =: -nlogZ
|
|
"""
|
|
# should have logZ[:, i] == logZ[:, j] for any i, j
|
|
logZ = self.recursion(input_energy, mask, return_sequences=False, **kwargs)
|
|
return logZ[:, 0]
|
|
|
|
def get_energy(self, y_true, input_energy, mask):
|
|
"""Energy = a1' y1 + u1' y1 + y1' U y2 + u2' y2 + y2' U y3 + u3' y3 + an' y3
|
|
"""
|
|
input_energy = K.sum(input_energy * y_true, 2) # (B, T)
|
|
# (B, T-1)
|
|
chain_energy = K.sum(K.dot(y_true[:, :-1, :],
|
|
self.chain_kernel) * y_true[:, 1:, :], 2)
|
|
|
|
if mask is not None:
|
|
mask = K.cast(mask, K.floatx())
|
|
# (B, T-1), mask[:,:-1]*mask[:,1:] makes it work with any padding
|
|
chain_mask = mask[:, :-1] * mask[:, 1:]
|
|
input_energy = input_energy * mask
|
|
chain_energy = chain_energy * chain_mask
|
|
total_energy = K.sum(input_energy, -1) + K.sum(chain_energy, -1) # (B, )
|
|
|
|
return total_energy
|
|
|
|
def get_negative_log_likelihood(self, y_true, X, mask):
|
|
"""Compute the loss, i.e., negative log likelihood (normalize by number of time steps)
|
|
likelihood = 1/Z * exp(-E) -> neg_log_like = - log(1/Z * exp(-E)) = logZ + E
|
|
"""
|
|
input_energy = self.activation(K.dot(X, self.kernel) + self.bias)
|
|
if self.use_boundary:
|
|
input_energy = self.add_boundary_energy(input_energy, mask,
|
|
self.left_boundary,
|
|
self.right_boundary)
|
|
energy = self.get_energy(y_true, input_energy, mask)
|
|
logZ = self.get_log_normalization_constant(input_energy, mask,
|
|
input_length=K.int_shape(X)[1])
|
|
nloglik = logZ + energy
|
|
if mask is not None:
|
|
nloglik = nloglik / K.sum(K.cast(mask, K.floatx()), 1)
|
|
else:
|
|
nloglik = nloglik / K.cast(K.shape(X)[1], K.floatx())
|
|
return nloglik
|
|
|
|
def step(self, input_energy_t, states, return_logZ=True):
|
|
# not in the following `prev_target_val` has shape = (B, F)
|
|
# where B = batch_size, F = output feature dim
|
|
# Note: `i` is of float32, due to the behavior of `K.rnn`
|
|
prev_target_val, i, chain_energy = states[:3]
|
|
t = K.cast(i[0, 0], dtype='int32')
|
|
if len(states) > 3:
|
|
if K.backend() == 'theano':
|
|
m = states[3][:, t:(t + 2)]
|
|
else:
|
|
m = K.tf.slice(states[3], [0, t], [-1, 2])
|
|
input_energy_t = input_energy_t * K.expand_dims(m[:, 0])
|
|
# (1, F, F)*(B, 1, 1) -> (B, F, F)
|
|
chain_energy = chain_energy * K.expand_dims(
|
|
K.expand_dims(m[:, 0] * m[:, 1]))
|
|
if return_logZ:
|
|
# # shapes: (1, B, F) + (B, F, 1) -> (B, F, F)
|
|
# energy = chain_energy + K.expand_dims(input_energy_t - prev_target_val, 2)
|
|
# new_target_val = K.logsumexp(-energy, 1) # shapes: (B, F)
|
|
# return new_target_val, [new_target_val, i + 1]
|
|
|
|
energy = chain_energy + K.expand_dims(input_energy_t - prev_target_val, 2)
|
|
new_target_val = K.logsumexp(-energy, 1)
|
|
# added from here
|
|
if len(states) > 3:
|
|
if K.backend() == 'theano':
|
|
m = states[3][:, t:(t + 2)]
|
|
else:
|
|
m = K.slice(states[3], [0, t], [-1, 2])
|
|
is_valid = K.expand_dims(m[:, 0])
|
|
new_target_val = is_valid * new_target_val + (1 - is_valid) * prev_target_val
|
|
# added until here
|
|
return new_target_val, [new_target_val, i + 1]
|
|
else:
|
|
energy = chain_energy + K.expand_dims(input_energy_t + prev_target_val, 2)
|
|
min_energy = K.min(energy, 1)
|
|
# cast for tf-version `K.rnn
|
|
argmin_table = K.cast(K.argmin(energy, 1), K.floatx())
|
|
return argmin_table, [min_energy, i + 1]
|
|
|
|
def recursion(self, input_energy, mask=None, go_backwards=False,
|
|
return_sequences=True, return_logZ=True, input_length=None):
|
|
"""Forward (alpha) or backward (beta) recursion
|
|
If `return_logZ = True`, compute the logZ, the normalization constant:
|
|
\[ Z = \sum_{y1, y2, y3} exp(-E) # energy
|
|
= \sum_{y1, y2, y3} exp(-(u1' y1 + y1' W y2 + u2' y2 + y2' W y3 + u3' y3))
|
|
= sum_{y2, y3} (exp(-(u2' y2 + y2' W y3 + u3' y3))
|
|
sum_{y1} exp(-(u1' y1' + y1' W y2))) \]
|
|
Denote:
|
|
\[ S(y2) := sum_{y1} exp(-(u1' y1 + y1' W y2)), \]
|
|
\[ Z = sum_{y2, y3} exp(log S(y2) - (u2' y2 + y2' W y3 + u3' y3)) \]
|
|
\[ logS(y2) = log S(y2) = log_sum_exp(-(u1' y1' + y1' W y2)) \]
|
|
Note that:
|
|
yi's are one-hot vectors
|
|
u1, u3: boundary energies have been merged
|
|
If `return_logZ = False`, compute the Viterbi's best path lookup table.
|
|
"""
|
|
chain_energy = self.chain_kernel
|
|
# shape=(1, F, F): F=num of output features. 1st F is for t-1, 2nd F for t
|
|
chain_energy = K.expand_dims(chain_energy, 0)
|
|
# shape=(B, F), dtype=float32
|
|
prev_target_val = K.zeros_like(input_energy[:, 0, :])
|
|
|
|
if go_backwards:
|
|
input_energy = K.reverse(input_energy, 1)
|
|
if mask is not None:
|
|
mask = K.reverse(mask, 1)
|
|
|
|
initial_states = [prev_target_val, K.zeros_like(prev_target_val[:, :1])]
|
|
constants = [chain_energy]
|
|
|
|
if mask is not None:
|
|
mask2 = K.cast(K.concatenate([mask, K.zeros_like(mask[:, :1])], axis=1),
|
|
K.floatx())
|
|
constants.append(mask2)
|
|
|
|
def _step(input_energy_i, states):
|
|
return self.step(input_energy_i, states, return_logZ)
|
|
|
|
target_val_last, target_val_seq, _ = K.rnn(_step, input_energy,
|
|
initial_states,
|
|
constants=constants,
|
|
input_length=input_length,
|
|
unroll=self.unroll)
|
|
|
|
if return_sequences:
|
|
if go_backwards:
|
|
target_val_seq = K.reverse(target_val_seq, 1)
|
|
return target_val_seq
|
|
else:
|
|
return target_val_last
|
|
|
|
def forward_recursion(self, input_energy, **kwargs):
|
|
return self.recursion(input_energy, **kwargs)
|
|
|
|
def backward_recursion(self, input_energy, **kwargs):
|
|
return self.recursion(input_energy, go_backwards=True, **kwargs)
|
|
|
|
def get_marginal_prob(self, X, mask=None):
|
|
input_energy = self.activation(K.dot(X, self.kernel) + self.bias)
|
|
if self.use_boundary:
|
|
input_energy = self.add_boundary_energy(input_energy, mask,
|
|
self.left_boundary,
|
|
self.right_boundary)
|
|
input_length = K.int_shape(X)[1]
|
|
alpha = self.forward_recursion(input_energy, mask=mask,
|
|
input_length=input_length)
|
|
beta = self.backward_recursion(input_energy, mask=mask,
|
|
input_length=input_length)
|
|
if mask is not None:
|
|
input_energy = input_energy * K.expand_dims(K.cast(mask, K.floatx()))
|
|
margin = -(self.shift_right(alpha) + input_energy + self.shift_left(beta))
|
|
return self.softmaxNd(margin)
|
|
|
|
def viterbi_decoding(self, X, mask=None):
|
|
input_energy = self.activation(K.dot(X, self.kernel) + self.bias)
|
|
if self.use_boundary:
|
|
input_energy = self.add_boundary_energy(
|
|
input_energy, mask, self.left_boundary, self.right_boundary)
|
|
|
|
argmin_tables = self.recursion(input_energy, mask, return_logZ=False)
|
|
argmin_tables = K.cast(argmin_tables, 'int32')
|
|
|
|
# backward to find best path, `initial_best_idx` can be any,
|
|
# as all elements in the last argmin_table are the same
|
|
argmin_tables = K.reverse(argmin_tables, 1)
|
|
# matrix instead of vector is required by tf `K.rnn`
|
|
initial_best_idx = [K.expand_dims(argmin_tables[:, 0, 0])]
|
|
if K.backend() == 'theano':
|
|
initial_best_idx = [K.T.unbroadcast(initial_best_idx[0], 1)]
|
|
|
|
def gather_each_row(params, indices):
|
|
n = K.shape(indices)[0]
|
|
if K.backend() == 'theano':
|
|
return params[K.T.arange(n), indices]
|
|
else:
|
|
indices = K.transpose(K.stack([K.tf.range(n), indices]))
|
|
return K.tf.gather_nd(params, indices)
|
|
|
|
def find_path(argmin_table, best_idx):
|
|
next_best_idx = gather_each_row(argmin_table, best_idx[0][:, 0])
|
|
next_best_idx = K.expand_dims(next_best_idx)
|
|
if K.backend() == 'theano':
|
|
next_best_idx = K.T.unbroadcast(next_best_idx, 1)
|
|
return next_best_idx, [next_best_idx]
|
|
|
|
_, best_paths, _ = K.rnn(find_path, argmin_tables, initial_best_idx,
|
|
input_length=K.int_shape(X)[1], unroll=self.unroll)
|
|
best_paths = K.reverse(best_paths, 1)
|
|
best_paths = K.squeeze(best_paths, 2)
|
|
|
|
return K.one_hot(best_paths, self.units)
|