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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# 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.
# ==============================================================================
import numpy as np
import tensorflow as tf
import tensorflow_fold as td
from model_n2nmn import netgen_att
from model_n2nmn import assembler
from model_n2nmn.modules import Modules
class Model:
def __init__(self,
config,
kb,
text_seq_batch,
seq_length_batch,
num_vocab_txt,
num_vocab_nmn,
EOS_idx,
num_choices,
decoder_sampling,
use_gt_layout=None,
gt_layout_batch=None,
scope='neural_module_network',
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# Part 1: Seq2seq RNN to generate module layout tokens
embedding_mat = tf.get_variable(
'embedding_mat', [num_vocab_txt, config.embed_dim_txt],
initializer=tf.contrib.layers.xavier_initializer())
with tf.variable_scope('layout_generation'):
att_seq2seq = netgen_att.AttentionSeq2Seq(
config, text_seq_batch, seq_length_batch, num_vocab_txt,
num_vocab_nmn, EOS_idx, decoder_sampling, embedding_mat,
use_gt_layout, gt_layout_batch)
self.att_seq2seq = att_seq2seq
predicted_tokens = att_seq2seq.predicted_tokens
token_probs = att_seq2seq.token_probs
word_vecs = att_seq2seq.word_vecs
neg_entropy = att_seq2seq.neg_entropy
self.atts = att_seq2seq.atts
self.predicted_tokens = predicted_tokens
self.token_probs = token_probs
self.word_vecs = word_vecs
self.neg_entropy = neg_entropy
# log probability of each generated sequence
self.log_seq_prob = tf.reduce_sum(tf.log(token_probs), axis=0)
# Part 2: Neural Module Network
with tf.variable_scope('layout_execution'):
modules = Modules(config, kb, word_vecs, num_choices, embedding_mat)
self.modules = modules
# Recursion of modules
att_shape = [len(kb)]
# Forward declaration of module recursion
att_expr_decl = td.ForwardDeclaration(td.PyObjectType(),
td.TensorType(att_shape))
# _key_find
case_key_find = td.Record([('time_idx', td.Scalar(dtype='int32')),
('batch_idx', td.Scalar(dtype='int32'))])
case_key_find = case_key_find >> td.ScopedLayer(
modules.KeyFindModule, name_or_scope='KeyFindModule')
# _key_filter
case_key_filter = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_key_filter = case_key_filter >> td.ScopedLayer(
modules.KeyFilterModule, name_or_scope='KeyFilterModule')
recursion_cases = td.OneOf(
td.GetItem('module'),
{'_key_find': case_key_find,
'_key_filter': case_key_filter})
att_expr_decl.resolve_to(recursion_cases)
# _val_desc: output scores for choice (for valid expressions)
predicted_scores = td.Record([('input_0', recursion_cases),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
predicted_scores = predicted_scores >> td.ScopedLayer(
modules.ValDescribeModule, name_or_scope='ValDescribeModule')
# For invalid expressions, define a dummy answer
# so that all answers have the same form
INVALID = assembler.INVALID_EXPR
dummy_scores = td.Void() >> td.FromTensor(
np.zeros(num_choices, np.float32))
output_scores = td.OneOf(
td.GetItem('module'),
{'_val_desc': predicted_scores,
INVALID: dummy_scores})
# compile and get the output scores
self.compiler = td.Compiler.create(output_scores)
self.scores = self.compiler.output_tensors[0]
# Regularization: Entropy + L2
self.entropy_reg = tf.reduce_mean(neg_entropy)
module_weights = [
v for v in tf.trainable_variables()
if (scope in v.op.name and v.op.name.endswith('weights'))
]
self.l2_reg = tf.add_n([tf.nn.l2_loss(v) for v in module_weights])
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