From d5159a1c03c4ce92eb3bdb77ac1723d0bdbc052c Mon Sep 17 00:00:00 2001 From: Tim Dozat Date: Wed, 7 Jun 2017 14:27:30 -0700 Subject: [PATCH] Added file for Sebastian --- .../neural/models/nlp/parsers/bin_parser.py | 118 ++++++++++++++++++ 1 file changed, 118 insertions(+) create mode 100644 parser/neural/models/nlp/parsers/bin_parser.py diff --git a/parser/neural/models/nlp/parsers/bin_parser.py b/parser/neural/models/nlp/parsers/bin_parser.py new file mode 100644 index 0000000..07c9961 --- /dev/null +++ b/parser/neural/models/nlp/parsers/bin_parser.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- + +# Copyright 2016 Timothy Dozat +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +from parser.neural.models.nlp.parsers.base_parser import BaseParser + +#*************************************************************** +class BinParser(BaseParser): + """""" + + #============================================================= + def __call__(self, vocabs, moving_params=None): + """""" + + top_recur = super(BinParser, self).__call__(vocabs, moving_params=moving_params) + int_tokens_to_keep = tf.to_int32(self.tokens_to_keep) + + with tf.variable_scope('MLP'): + dep_mlp, head_mlp = self.MLP(top_recur, self.arc_mlp_size + self.rel_mlp_size + self.p_mlp_size, + n_splits=2) + arc_dep_mlp, rel_dep_mlp, p_dep_mlp = tf.split(dep_mlp, [self.arc_mlp_size, self.rel_mlp_size, self.p_mlp_size], axis=2) + arc_head_mlp, rel_head_mlp, p_head_mlp = tf.split(head_mlp, [self.arc_mlp_size, self.rel_mlp_size, self.p_mlp_size], axis=2) + + with tf.variable_scope('p'): + # (n x b x d) o (d x 1 x d) o (n x b x d).T -> (n x b x b) + arc_ps = self.bilinear(p_dep_mlp, p_head_mlp, 1) + # (b x 1) + arc_logits = -tf.nn.softplus(arc_ps) + + with tf.variable_scope('Arc'): + # (n x b x d) o (d x 1 x d) o (n x b x d).T -> (n x b x b) + arc_logits += self.bilinear(arc_dep_mlp, arc_head_mlp, 1, add_bias2=False) + # (n x b x b) + arc_probs = tf.nn.softmax(arc_logits) + # (n x b) + arc_preds = tf.to_int32(tf.argmax(arc_logits, axis=-1)) + # (n x b) + arc_targets = self.vocabs['heads'].placeholder + # (n x b) + arc_correct = tf.to_int32(tf.equal(arc_preds, arc_targets))*int_tokens_to_keep + # () + arc_loss = tf.losses.sparse_softmax_cross_entropy(arc_targets, arc_logits, self.tokens_to_keep) + + with tf.variable_scope('Rel'): + # (n x b x d) o (d x r x d) o (n x b x d).T -> (n x b x r x b) + rel_logits = self.bilinear(rel_dep_mlp, rel_head_mlp, len(self.vocabs['rels'])) + # (n x b x r x b) + rel_probs = tf.nn.softmax(rel_logits, dim=2) + # (n x b x b) + one_hot = tf.one_hot(arc_preds if moving_params is not None else arc_targets, self.bucket_size) + # (n x b x b) -> (n x b x b x 1) + one_hot = tf.expand_dims(one_hot, axis=3) + # (n x b x r x b) o (n x b x b x 1) -> (n x b x r x 1) + select_rel_logits = tf.matmul(rel_logits, one_hot) + # (n x b x r x 1) -> (n x b x r) + select_rel_logits = tf.squeeze(select_rel_logits, axis=3) + # (n x b) + rel_preds = tf.to_int32(tf.argmax(select_rel_logits, axis=-1)) + # (n x b) + rel_targets = self.vocabs['rels'].placeholder + # (n x b) + rel_correct = tf.to_int32(tf.equal(rel_preds, rel_targets))*int_tokens_to_keep + # () + rel_loss = tf.losses.sparse_softmax_cross_entropy(rel_targets, select_rel_logits, self.tokens_to_keep) + + n_arc_correct = tf.reduce_sum(arc_correct) + n_rel_correct = tf.reduce_sum(rel_correct) + correct = arc_correct * rel_correct + n_correct = tf.reduce_sum(correct) + n_seqs_correct = tf.reduce_sum(tf.to_int32(tf.equal(tf.reduce_sum(correct, axis=1), self.sequence_lengths-1))) + loss = arc_loss + rel_loss + + outputs = { + 'arc_logits': arc_logits, + 'arc_probs': arc_probs, + 'arc_preds': arc_preds, + 'arc_targets': arc_targets, + 'arc_correct': arc_correct, + 'arc_loss': arc_loss, + 'n_arc_correct': n_arc_correct, + + 'rel_logits': rel_logits, + 'rel_probs': rel_probs, + 'rel_preds': rel_preds, + 'rel_targets': rel_targets, + 'rel_correct': rel_correct, + 'rel_loss': rel_loss, + 'n_rel_correct': n_rel_correct, + + 'n_tokens': self.n_tokens, + 'n_seqs': self.batch_size, + 'tokens_to_keep': self.tokens_to_keep, + 'n_correct': n_correct, + 'n_seqs_correct': n_seqs_correct, + 'loss': loss + } + + return outputs