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model.py
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model.py
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# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""Author: aneelakantan (Arvind Neelakantan)
"""
import numpy as np
import tensorflow as tf
import nn_utils
class Graph():
def __init__(self, utility, batch_size, max_passes, mode="train"):
self.utility = utility
self.data_type = self.utility.tf_data_type[self.utility.FLAGS.data_type]
self.max_elements = self.utility.FLAGS.max_elements
max_elements = self.utility.FLAGS.max_elements
self.num_cols = self.utility.FLAGS.max_number_cols
self.num_word_cols = self.utility.FLAGS.max_word_cols
self.question_length = self.utility.FLAGS.question_length
self.batch_size = batch_size
self.max_passes = max_passes
self.mode = mode
self.embedding_dims = self.utility.FLAGS.embedding_dims
#input question and a mask
self.batch_question = tf.placeholder(tf.int32,
[batch_size, self.question_length])
self.batch_question_attention_mask = tf.placeholder(
self.data_type, [batch_size, self.question_length])
#ground truth scalar answer and lookup answer
self.batch_answer = tf.placeholder(self.data_type, [batch_size])
self.batch_print_answer = tf.placeholder(
self.data_type,
[batch_size, self.num_cols + self.num_word_cols, max_elements])
#number columns and its processed version
self.batch_number_column = tf.placeholder(
self.data_type, [batch_size, self.num_cols, max_elements
]) #columns with numeric entries
self.batch_processed_number_column = tf.placeholder(
self.data_type, [batch_size, self.num_cols, max_elements])
self.batch_processed_sorted_index_number_column = tf.placeholder(
tf.int32, [batch_size, self.num_cols, max_elements])
#word columns and its processed version
self.batch_processed_word_column = tf.placeholder(
self.data_type, [batch_size, self.num_word_cols, max_elements])
self.batch_processed_sorted_index_word_column = tf.placeholder(
tf.int32, [batch_size, self.num_word_cols, max_elements])
self.batch_word_column_entry_mask = tf.placeholder(
tf.int32, [batch_size, self.num_word_cols, max_elements])
#names of word and number columns along with their mask
self.batch_word_column_names = tf.placeholder(
tf.int32,
[batch_size, self.num_word_cols, self.utility.FLAGS.max_entry_length])
self.batch_word_column_mask = tf.placeholder(
self.data_type, [batch_size, self.num_word_cols])
self.batch_number_column_names = tf.placeholder(
tf.int32,
[batch_size, self.num_cols, self.utility.FLAGS.max_entry_length])
self.batch_number_column_mask = tf.placeholder(self.data_type,
[batch_size, self.num_cols])
#exact match and group by max operation
self.batch_exact_match = tf.placeholder(
self.data_type,
[batch_size, self.num_cols + self.num_word_cols, max_elements])
self.batch_column_exact_match = tf.placeholder(
self.data_type, [batch_size, self.num_cols + self.num_word_cols])
self.batch_group_by_max = tf.placeholder(
self.data_type,
[batch_size, self.num_cols + self.num_word_cols, max_elements])
#numbers in the question along with their position. This is used to compute arguments to the comparison operations
self.batch_question_number = tf.placeholder(self.data_type, [batch_size, 1])
self.batch_question_number_one = tf.placeholder(self.data_type,
[batch_size, 1])
self.batch_question_number_mask = tf.placeholder(
self.data_type, [batch_size, max_elements])
self.batch_question_number_one_mask = tf.placeholder(self.data_type,
[batch_size, 1])
self.batch_ordinal_question = tf.placeholder(
self.data_type, [batch_size, self.question_length])
self.batch_ordinal_question_one = tf.placeholder(
self.data_type, [batch_size, self.question_length])
def LSTM_question_embedding(self, sentence, sentence_length):
#LSTM processes the input question
lstm_params = "question_lstm"
hidden_vectors = []
sentence = self.batch_question
question_hidden = tf.zeros(
[self.batch_size, self.utility.FLAGS.embedding_dims], self.data_type)
question_c_hidden = tf.zeros(
[self.batch_size, self.utility.FLAGS.embedding_dims], self.data_type)
if (self.utility.FLAGS.rnn_dropout > 0.0):
if (self.mode == "train"):
rnn_dropout_mask = tf.cast(
tf.random_uniform(
tf.shape(question_hidden), minval=0.0, maxval=1.0) <
self.utility.FLAGS.rnn_dropout,
self.data_type) / self.utility.FLAGS.rnn_dropout
else:
rnn_dropout_mask = tf.ones_like(question_hidden)
for question_iterator in range(self.question_length):
curr_word = sentence[:, question_iterator]
question_vector = nn_utils.apply_dropout(
nn_utils.get_embedding(curr_word, self.utility, self.params),
self.utility.FLAGS.dropout, self.mode)
question_hidden, question_c_hidden = nn_utils.LSTMCell(
question_vector, question_hidden, question_c_hidden, lstm_params,
self.params)
if (self.utility.FLAGS.rnn_dropout > 0.0):
question_hidden = question_hidden * rnn_dropout_mask
hidden_vectors.append(tf.expand_dims(question_hidden, 0))
hidden_vectors = tf.concat(axis=0, values=hidden_vectors)
return question_hidden, hidden_vectors
def history_recurrent_step(self, curr_hprev, hprev):
#A single RNN step for controller or history RNN
return tf.tanh(
tf.matmul(
tf.concat(axis=1, values=[hprev, curr_hprev]), self.params[
"history_recurrent"])) + self.params["history_recurrent_bias"]
def question_number_softmax(self, hidden_vectors):
#Attention on quetsion to decide the question number to passed to comparison ops
def compute_ans(op_embedding, comparison):
op_embedding = tf.expand_dims(op_embedding, 0)
#dot product of operation embedding with hidden state to the left of the number occurrence
first = tf.transpose(
tf.matmul(op_embedding,
tf.transpose(
tf.reduce_sum(hidden_vectors * tf.tile(
tf.expand_dims(
tf.transpose(self.batch_ordinal_question), 2),
[1, 1, self.utility.FLAGS.embedding_dims]), 0))))
second = self.batch_question_number_one_mask + tf.transpose(
tf.matmul(op_embedding,
tf.transpose(
tf.reduce_sum(hidden_vectors * tf.tile(
tf.expand_dims(
tf.transpose(self.batch_ordinal_question_one), 2
), [1, 1, self.utility.FLAGS.embedding_dims]), 0))))
question_number_softmax = tf.nn.softmax(tf.concat(axis=1, values=[first, second]))
if (self.mode == "test"):
cond = tf.equal(question_number_softmax,
tf.reshape(
tf.reduce_max(question_number_softmax, 1),
[self.batch_size, 1]))
question_number_softmax = tf.where(
cond,
tf.fill(tf.shape(question_number_softmax), 1.0),
tf.fill(tf.shape(question_number_softmax), 0.0))
question_number_softmax = tf.cast(question_number_softmax,
self.data_type)
ans = tf.reshape(
tf.reduce_sum(question_number_softmax * tf.concat(
axis=1, values=[self.batch_question_number, self.batch_question_number_one]),
1), [self.batch_size, 1])
return ans
def compute_op_position(op_name):
for i in range(len(self.utility.operations_set)):
if (op_name == self.utility.operations_set[i]):
return i
def compute_question_number(op_name):
op_embedding = tf.nn.embedding_lookup(self.params_unit,
compute_op_position(op_name))
return compute_ans(op_embedding, op_name)
curr_greater_question_number = compute_question_number("greater")
curr_lesser_question_number = compute_question_number("lesser")
curr_geq_question_number = compute_question_number("geq")
curr_leq_question_number = compute_question_number("leq")
return curr_greater_question_number, curr_lesser_question_number, curr_geq_question_number, curr_leq_question_number
def perform_attention(self, context_vector, hidden_vectors, length, mask):
#Performs attention on hiddent_vectors using context vector
context_vector = tf.tile(
tf.expand_dims(context_vector, 0), [length, 1, 1]) #time * bs * d
attention_softmax = tf.nn.softmax(
tf.transpose(tf.reduce_sum(context_vector * hidden_vectors, 2)) +
mask) #batch_size * time
attention_softmax = tf.tile(
tf.expand_dims(tf.transpose(attention_softmax), 2),
[1, 1, self.embedding_dims])
ans_vector = tf.reduce_sum(attention_softmax * hidden_vectors, 0)
return ans_vector
#computes embeddings for column names using parameters of question module
def get_column_hidden_vectors(self):
#vector representations for the column names
self.column_hidden_vectors = tf.reduce_sum(
nn_utils.get_embedding(self.batch_number_column_names, self.utility,
self.params), 2)
self.word_column_hidden_vectors = tf.reduce_sum(
nn_utils.get_embedding(self.batch_word_column_names, self.utility,
self.params), 2)
def create_summary_embeddings(self):
#embeddings for each text entry in the table using parameters of the question module
self.summary_text_entry_embeddings = tf.reduce_sum(
tf.expand_dims(self.batch_exact_match, 3) * tf.expand_dims(
tf.expand_dims(
tf.expand_dims(
nn_utils.get_embedding(self.utility.entry_match_token_id,
self.utility, self.params), 0), 1),
2), 2)
def compute_column_softmax(self, column_controller_vector, time_step):
#compute softmax over all the columns using column controller vector
column_controller_vector = tf.tile(
tf.expand_dims(column_controller_vector, 1),
[1, self.num_cols + self.num_word_cols, 1]) #max_cols * bs * d
column_controller_vector = nn_utils.apply_dropout(
column_controller_vector, self.utility.FLAGS.dropout, self.mode)
self.full_column_hidden_vectors = tf.concat(
axis=1, values=[self.column_hidden_vectors, self.word_column_hidden_vectors])
self.full_column_hidden_vectors += self.summary_text_entry_embeddings
self.full_column_hidden_vectors = nn_utils.apply_dropout(
self.full_column_hidden_vectors, self.utility.FLAGS.dropout, self.mode)
column_logits = tf.reduce_sum(
column_controller_vector * self.full_column_hidden_vectors, 2) + (
self.params["word_match_feature_column_name"] *
self.batch_column_exact_match) + self.full_column_mask
column_softmax = tf.nn.softmax(column_logits) #batch_size * max_cols
return column_softmax
def compute_first_or_last(self, select, first=True):
#perform first ot last operation on row select with probabilistic row selection
answer = tf.zeros_like(select)
running_sum = tf.zeros([self.batch_size, 1], self.data_type)
for i in range(self.max_elements):
if (first):
current = tf.slice(select, [0, i], [self.batch_size, 1])
else:
current = tf.slice(select, [0, self.max_elements - 1 - i],
[self.batch_size, 1])
curr_prob = current * (1 - running_sum)
curr_prob = curr_prob * tf.cast(curr_prob >= 0.0, self.data_type)
running_sum += curr_prob
temp_ans = []
curr_prob = tf.expand_dims(tf.reshape(curr_prob, [self.batch_size]), 0)
for i_ans in range(self.max_elements):
if (not (first) and i_ans == self.max_elements - 1 - i):
temp_ans.append(curr_prob)
elif (first and i_ans == i):
temp_ans.append(curr_prob)
else:
temp_ans.append(tf.zeros_like(curr_prob))
temp_ans = tf.transpose(tf.concat(axis=0, values=temp_ans))
answer += temp_ans
return answer
def make_hard_softmax(self, softmax):
#converts soft selection to hard selection. used at test time
cond = tf.equal(
softmax, tf.reshape(tf.reduce_max(softmax, 1), [self.batch_size, 1]))
softmax = tf.where(
cond, tf.fill(tf.shape(softmax), 1.0), tf.fill(tf.shape(softmax), 0.0))
softmax = tf.cast(softmax, self.data_type)
return softmax
def compute_max_or_min(self, select, maxi=True):
#computes the argmax and argmin of a column with probabilistic row selection
answer = tf.zeros([
self.batch_size, self.num_cols + self.num_word_cols, self.max_elements
], self.data_type)
sum_prob = tf.zeros([self.batch_size, self.num_cols + self.num_word_cols],
self.data_type)
for j in range(self.max_elements):
if (maxi):
curr_pos = j
else:
curr_pos = self.max_elements - 1 - j
select_index = tf.slice(self.full_processed_sorted_index_column,
[0, 0, curr_pos], [self.batch_size, -1, 1])
select_mask = tf.equal(
tf.tile(
tf.expand_dims(
tf.tile(
tf.expand_dims(tf.range(self.max_elements), 0),
[self.batch_size, 1]), 1),
[1, self.num_cols + self.num_word_cols, 1]), select_index)
curr_prob = tf.expand_dims(select, 1) * tf.cast(
select_mask, self.data_type) * self.select_bad_number_mask
curr_prob = curr_prob * tf.expand_dims((1 - sum_prob), 2)
curr_prob = curr_prob * tf.expand_dims(
tf.cast((1 - sum_prob) > 0.0, self.data_type), 2)
answer = tf.where(select_mask, curr_prob, answer)
sum_prob += tf.reduce_sum(curr_prob, 2)
return answer
def perform_operations(self, softmax, full_column_softmax, select,
prev_select_1, curr_pass):
#performs all the 15 operations. computes scalar output, lookup answer and row selector
column_softmax = tf.slice(full_column_softmax, [0, 0],
[self.batch_size, self.num_cols])
word_column_softmax = tf.slice(full_column_softmax, [0, self.num_cols],
[self.batch_size, self.num_word_cols])
init_max = self.compute_max_or_min(select, maxi=True)
init_min = self.compute_max_or_min(select, maxi=False)
#operations that are column independent
count = tf.reshape(tf.reduce_sum(select, 1), [self.batch_size, 1])
select_full_column_softmax = tf.tile(
tf.expand_dims(full_column_softmax, 2),
[1, 1, self.max_elements
]) #BS * (max_cols + max_word_cols) * max_elements
select_word_column_softmax = tf.tile(
tf.expand_dims(word_column_softmax, 2),
[1, 1, self.max_elements]) #BS * max_word_cols * max_elements
select_greater = tf.reduce_sum(
self.init_select_greater * select_full_column_softmax,
1) * self.batch_question_number_mask #BS * max_elements
select_lesser = tf.reduce_sum(
self.init_select_lesser * select_full_column_softmax,
1) * self.batch_question_number_mask #BS * max_elements
select_geq = tf.reduce_sum(
self.init_select_geq * select_full_column_softmax,
1) * self.batch_question_number_mask #BS * max_elements
select_leq = tf.reduce_sum(
self.init_select_leq * select_full_column_softmax,
1) * self.batch_question_number_mask #BS * max_elements
select_max = tf.reduce_sum(init_max * select_full_column_softmax,
1) #BS * max_elements
select_min = tf.reduce_sum(init_min * select_full_column_softmax,
1) #BS * max_elements
select_prev = tf.concat(axis=1, values=[
tf.slice(select, [0, 1], [self.batch_size, self.max_elements - 1]),
tf.cast(tf.zeros([self.batch_size, 1]), self.data_type)
])
select_next = tf.concat(axis=1, values=[
tf.cast(tf.zeros([self.batch_size, 1]), self.data_type), tf.slice(
select, [0, 0], [self.batch_size, self.max_elements - 1])
])
select_last_rs = self.compute_first_or_last(select, False)
select_first_rs = self.compute_first_or_last(select, True)
select_word_match = tf.reduce_sum(self.batch_exact_match *
select_full_column_softmax, 1)
select_group_by_max = tf.reduce_sum(self.batch_group_by_max *
select_full_column_softmax, 1)
length_content = 1
length_select = 13
length_print = 1
values = tf.concat(axis=1, values=[count])
softmax_content = tf.slice(softmax, [0, 0],
[self.batch_size, length_content])
#compute scalar output
output = tf.reduce_sum(tf.multiply(softmax_content, values), 1)
#compute lookup answer
softmax_print = tf.slice(softmax, [0, length_content + length_select],
[self.batch_size, length_print])
curr_print = select_full_column_softmax * tf.tile(
tf.expand_dims(select, 1),
[1, self.num_cols + self.num_word_cols, 1
]) #BS * max_cols * max_elements (conisders only column)
self.batch_lookup_answer = curr_print * tf.tile(
tf.expand_dims(softmax_print, 2),
[1, self.num_cols + self.num_word_cols, self.max_elements
]) #BS * max_cols * max_elements
self.batch_lookup_answer = self.batch_lookup_answer * self.select_full_mask
#compute row select
softmax_select = tf.slice(softmax, [0, length_content],
[self.batch_size, length_select])
select_lists = [
tf.expand_dims(select_prev, 1), tf.expand_dims(select_next, 1),
tf.expand_dims(select_first_rs, 1), tf.expand_dims(select_last_rs, 1),
tf.expand_dims(select_group_by_max, 1),
tf.expand_dims(select_greater, 1), tf.expand_dims(select_lesser, 1),
tf.expand_dims(select_geq, 1), tf.expand_dims(select_leq, 1),
tf.expand_dims(select_max, 1), tf.expand_dims(select_min, 1),
tf.expand_dims(select_word_match, 1),
tf.expand_dims(self.reset_select, 1)
]
select = tf.reduce_sum(
tf.tile(tf.expand_dims(softmax_select, 2), [1, 1, self.max_elements]) *
tf.concat(axis=1, values=select_lists), 1)
select = select * self.select_whole_mask
return output, select
def one_pass(self, select, question_embedding, hidden_vectors, hprev,
prev_select_1, curr_pass):
#Performs one timestep which involves selecting an operation and a column
attention_vector = self.perform_attention(
hprev, hidden_vectors, self.question_length,
self.batch_question_attention_mask) #batch_size * embedding_dims
controller_vector = tf.nn.relu(
tf.matmul(hprev, self.params["controller_prev"]) + tf.matmul(
tf.concat(axis=1, values=[question_embedding, attention_vector]), self.params[
"controller"]))
column_controller_vector = tf.nn.relu(
tf.matmul(hprev, self.params["column_controller_prev"]) + tf.matmul(
tf.concat(axis=1, values=[question_embedding, attention_vector]), self.params[
"column_controller"]))
controller_vector = nn_utils.apply_dropout(
controller_vector, self.utility.FLAGS.dropout, self.mode)
self.operation_logits = tf.matmul(controller_vector,
tf.transpose(self.params_unit))
softmax = tf.nn.softmax(self.operation_logits)
soft_softmax = softmax
#compute column softmax: bs * max_columns
weighted_op_representation = tf.transpose(
tf.matmul(tf.transpose(self.params_unit), tf.transpose(softmax)))
column_controller_vector = tf.nn.relu(
tf.matmul(
tf.concat(axis=1, values=[
column_controller_vector, weighted_op_representation
]), self.params["break_conditional"]))
full_column_softmax = self.compute_column_softmax(column_controller_vector,
curr_pass)
soft_column_softmax = full_column_softmax
if (self.mode == "test"):
full_column_softmax = self.make_hard_softmax(full_column_softmax)
softmax = self.make_hard_softmax(softmax)
output, select = self.perform_operations(softmax, full_column_softmax,
select, prev_select_1, curr_pass)
return output, select, softmax, soft_softmax, full_column_softmax, soft_column_softmax
def compute_lookup_error(self, val):
#computes lookup error.
cond = tf.equal(self.batch_print_answer, val)
inter = tf.where(
cond, self.init_print_error,
tf.tile(
tf.reshape(tf.constant(1e10, self.data_type), [1, 1, 1]), [
self.batch_size, self.utility.FLAGS.max_word_cols +
self.utility.FLAGS.max_number_cols,
self.utility.FLAGS.max_elements
]))
return tf.reduce_min(tf.reduce_min(inter, 1), 1) * tf.cast(
tf.greater(
tf.reduce_sum(tf.reduce_sum(tf.cast(cond, self.data_type), 1), 1),
0.0), self.data_type)
def soft_min(self, x, y):
return tf.maximum(-1.0 * (1 / (
self.utility.FLAGS.soft_min_value + 0.0)) * tf.log(
tf.exp(-self.utility.FLAGS.soft_min_value * x) + tf.exp(
-self.utility.FLAGS.soft_min_value * y)), tf.zeros_like(x))
def error_computation(self):
#computes the error of each example in a batch
math_error = 0.5 * tf.square(tf.subtract(self.scalar_output, self.batch_answer))
#scale math error
math_error = math_error / self.rows
math_error = tf.minimum(math_error, self.utility.FLAGS.max_math_error *
tf.ones(tf.shape(math_error), self.data_type))
self.init_print_error = tf.where(
self.batch_gold_select, -1 * tf.log(self.batch_lookup_answer + 1e-300 +
self.invert_select_full_mask), -1 *
tf.log(1 - self.batch_lookup_answer)) * self.select_full_mask
print_error_1 = self.init_print_error * tf.cast(
tf.equal(self.batch_print_answer, 0.0), self.data_type)
print_error = tf.reduce_sum(tf.reduce_sum((print_error_1), 1), 1)
for val in range(1, 58):
print_error += self.compute_lookup_error(val + 0.0)
print_error = print_error * self.utility.FLAGS.print_cost / self.num_entries
if (self.mode == "train"):
error = tf.where(
tf.logical_and(
tf.not_equal(self.batch_answer, 0.0),
tf.not_equal(
tf.reduce_sum(tf.reduce_sum(self.batch_print_answer, 1), 1),
0.0)),
self.soft_min(math_error, print_error),
tf.where(
tf.not_equal(self.batch_answer, 0.0), math_error, print_error))
else:
error = tf.where(
tf.logical_and(
tf.equal(self.scalar_output, 0.0),
tf.equal(
tf.reduce_sum(tf.reduce_sum(self.batch_lookup_answer, 1), 1),
0.0)),
tf.ones_like(math_error),
tf.where(
tf.equal(self.scalar_output, 0.0), print_error, math_error))
return error
def batch_process(self):
#Computes loss and fraction of correct examples in a batch.
self.params_unit = nn_utils.apply_dropout(
self.params["unit"], self.utility.FLAGS.dropout, self.mode)
batch_size = self.batch_size
max_passes = self.max_passes
num_timesteps = 1
max_elements = self.max_elements
select = tf.cast(
tf.fill([self.batch_size, max_elements], 1.0), self.data_type)
hprev = tf.cast(
tf.fill([self.batch_size, self.embedding_dims], 0.0),
self.data_type) #running sum of the hidden states of the model
output = tf.cast(tf.fill([self.batch_size, 1], 0.0),
self.data_type) #output of the model
correct = tf.cast(
tf.fill([1], 0.0), self.data_type
) #to compute accuracy, returns number of correct examples for this batch
total_error = 0.0
prev_select_1 = tf.zeros_like(select)
self.create_summary_embeddings()
self.get_column_hidden_vectors()
#get question embedding
question_embedding, hidden_vectors = self.LSTM_question_embedding(
self.batch_question, self.question_length)
#compute arguments for comparison operation
greater_question_number, lesser_question_number, geq_question_number, leq_question_number = self.question_number_softmax(
hidden_vectors)
self.init_select_greater = tf.cast(
tf.greater(self.full_processed_column,
tf.expand_dims(greater_question_number, 2)), self.
data_type) * self.select_bad_number_mask #bs * max_cols * max_elements
self.init_select_lesser = tf.cast(
tf.less(self.full_processed_column,
tf.expand_dims(lesser_question_number, 2)), self.
data_type) * self.select_bad_number_mask #bs * max_cols * max_elements
self.init_select_geq = tf.cast(
tf.greater_equal(self.full_processed_column,
tf.expand_dims(geq_question_number, 2)), self.
data_type) * self.select_bad_number_mask #bs * max_cols * max_elements
self.init_select_leq = tf.cast(
tf.less_equal(self.full_processed_column,
tf.expand_dims(leq_question_number, 2)), self.
data_type) * self.select_bad_number_mask #bs * max_cols * max_elements
self.init_select_word_match = 0
if (self.utility.FLAGS.rnn_dropout > 0.0):
if (self.mode == "train"):
history_rnn_dropout_mask = tf.cast(
tf.random_uniform(
tf.shape(hprev), minval=0.0, maxval=1.0) <
self.utility.FLAGS.rnn_dropout,
self.data_type) / self.utility.FLAGS.rnn_dropout
else:
history_rnn_dropout_mask = tf.ones_like(hprev)
select = select * self.select_whole_mask
self.batch_log_prob = tf.zeros([self.batch_size], dtype=self.data_type)
#Perform max_passes and at each pass select operation and column
for curr_pass in range(max_passes):
print "step: ", curr_pass
output, select, softmax, soft_softmax, column_softmax, soft_column_softmax = self.one_pass(
select, question_embedding, hidden_vectors, hprev, prev_select_1,
curr_pass)
prev_select_1 = select
#compute input to history RNN
input_op = tf.transpose(
tf.matmul(
tf.transpose(self.params_unit), tf.transpose(
soft_softmax))) #weighted average of emebdding of operations
input_col = tf.reduce_sum(
tf.expand_dims(soft_column_softmax, 2) *
self.full_column_hidden_vectors, 1)
history_input = tf.concat(axis=1, values=[input_op, input_col])
history_input = nn_utils.apply_dropout(
history_input, self.utility.FLAGS.dropout, self.mode)
hprev = self.history_recurrent_step(history_input, hprev)
if (self.utility.FLAGS.rnn_dropout > 0.0):
hprev = hprev * history_rnn_dropout_mask
self.scalar_output = output
error = self.error_computation()
cond = tf.less(error, 0.0001, name="cond")
correct_add = tf.where(
cond, tf.fill(tf.shape(cond), 1.0), tf.fill(tf.shape(cond), 0.0))
correct = tf.reduce_sum(correct_add)
error = error / batch_size
total_error = tf.reduce_sum(error)
total_correct = correct / batch_size
return total_error, total_correct
def compute_error(self):
#Sets mask variables and performs batch processing
self.batch_gold_select = self.batch_print_answer > 0.0
self.full_column_mask = tf.concat(
axis=1, values=[self.batch_number_column_mask, self.batch_word_column_mask])
self.full_processed_column = tf.concat(
axis=1,
values=[self.batch_processed_number_column, self.batch_processed_word_column])
self.full_processed_sorted_index_column = tf.concat(axis=1, values=[
self.batch_processed_sorted_index_number_column,
self.batch_processed_sorted_index_word_column
])
self.select_bad_number_mask = tf.cast(
tf.logical_and(
tf.not_equal(self.full_processed_column,
self.utility.FLAGS.pad_int),
tf.not_equal(self.full_processed_column,
self.utility.FLAGS.bad_number_pre_process)),
self.data_type)
self.select_mask = tf.cast(
tf.logical_not(
tf.equal(self.batch_number_column, self.utility.FLAGS.pad_int)),
self.data_type)
self.select_word_mask = tf.cast(
tf.logical_not(
tf.equal(self.batch_word_column_entry_mask,
self.utility.dummy_token_id)), self.data_type)
self.select_full_mask = tf.concat(
axis=1, values=[self.select_mask, self.select_word_mask])
self.select_whole_mask = tf.maximum(
tf.reshape(
tf.slice(self.select_mask, [0, 0, 0],
[self.batch_size, 1, self.max_elements]),
[self.batch_size, self.max_elements]),
tf.reshape(
tf.slice(self.select_word_mask, [0, 0, 0],
[self.batch_size, 1, self.max_elements]),
[self.batch_size, self.max_elements]))
self.invert_select_full_mask = tf.cast(
tf.concat(axis=1, values=[
tf.equal(self.batch_number_column, self.utility.FLAGS.pad_int),
tf.equal(self.batch_word_column_entry_mask,
self.utility.dummy_token_id)
]), self.data_type)
self.batch_lookup_answer = tf.zeros(tf.shape(self.batch_gold_select))
self.reset_select = self.select_whole_mask
self.rows = tf.reduce_sum(self.select_whole_mask, 1)
self.num_entries = tf.reshape(
tf.reduce_sum(tf.reduce_sum(self.select_full_mask, 1), 1),
[self.batch_size])
self.final_error, self.final_correct = self.batch_process()
return self.final_error
def create_graph(self, params, global_step):
#Creates the graph to compute error, gradient computation and updates parameters
self.params = params
batch_size = self.batch_size
learning_rate = tf.cast(self.utility.FLAGS.learning_rate, self.data_type)
self.total_cost = self.compute_error()
optimize_params = self.params.values()
optimize_names = self.params.keys()
print "optimize params ", optimize_names
if (self.utility.FLAGS.l2_regularizer > 0.0):
reg_cost = 0.0
for ind_param in self.params.keys():
reg_cost += tf.nn.l2_loss(self.params[ind_param])
self.total_cost += self.utility.FLAGS.l2_regularizer * reg_cost
grads = tf.gradients(self.total_cost, optimize_params, name="gradients")
grad_norm = 0.0
for p, name in zip(grads, optimize_names):
print "grads: ", p, name
if isinstance(p, tf.IndexedSlices):
grad_norm += tf.reduce_sum(p.values * p.values)
elif not (p == None):
grad_norm += tf.reduce_sum(p * p)
grad_norm = tf.sqrt(grad_norm)
max_grad_norm = np.float32(self.utility.FLAGS.clip_gradients).astype(
self.utility.np_data_type[self.utility.FLAGS.data_type])
grad_scale = tf.minimum(
tf.cast(1.0, self.data_type), max_grad_norm / grad_norm)
clipped_grads = list()
for p in grads:
if isinstance(p, tf.IndexedSlices):
tmp = p.values * grad_scale
clipped_grads.append(tf.IndexedSlices(tmp, p.indices))
elif not (p == None):
clipped_grads.append(p * grad_scale)
else:
clipped_grads.append(p)
grads = clipped_grads
self.global_step = global_step
params_list = self.params.values()
params_list.append(self.global_step)
adam = tf.train.AdamOptimizer(
learning_rate,
epsilon=tf.cast(self.utility.FLAGS.eps, self.data_type),
use_locking=True)
self.step = adam.apply_gradients(zip(grads, optimize_params),
global_step=self.global_step)
self.init_op = tf.global_variables_initializer()