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neural_programmer.py
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neural_programmer.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.
# ==============================================================================
"""Implementation of the Neural Programmer model described in https://openreview.net/pdf?id=ry2YOrcge
This file calls functions to load & pre-process data, construct the TF graph
and performs training or evaluation as specified by the flag evaluator_job
Author: aneelakantan (Arvind Neelakantan)
"""
import time
from random import Random
import numpy as np
import tensorflow as tf
import model
import wiki_data
import parameters
import data_utils
tf.flags.DEFINE_integer("train_steps", 100001, "Number of steps to train")
tf.flags.DEFINE_integer("eval_cycle", 500,
"Evaluate model at every eval_cycle steps")
tf.flags.DEFINE_integer("max_elements", 100,
"maximum rows that are considered for processing")
tf.flags.DEFINE_integer(
"max_number_cols", 15,
"maximum number columns that are considered for processing")
tf.flags.DEFINE_integer(
"max_word_cols", 25,
"maximum number columns that are considered for processing")
tf.flags.DEFINE_integer("question_length", 62, "maximum question length")
tf.flags.DEFINE_integer("max_entry_length", 1, "")
tf.flags.DEFINE_integer("max_passes", 4, "number of operation passes")
tf.flags.DEFINE_integer("embedding_dims", 256, "")
tf.flags.DEFINE_integer("batch_size", 20, "")
tf.flags.DEFINE_float("clip_gradients", 1.0, "")
tf.flags.DEFINE_float("eps", 1e-6, "")
tf.flags.DEFINE_float("param_init", 0.1, "")
tf.flags.DEFINE_float("learning_rate", 0.001, "")
tf.flags.DEFINE_float("l2_regularizer", 0.0001, "")
tf.flags.DEFINE_float("print_cost", 50.0,
"weighting factor in the objective function")
tf.flags.DEFINE_string("job_id", "temp", """job id""")
tf.flags.DEFINE_string("output_dir", "../model/",
"""output_dir""")
tf.flags.DEFINE_string("data_dir", "../data/",
"""data_dir""")
tf.flags.DEFINE_integer("write_every", 500, "wrtie every N")
tf.flags.DEFINE_integer("param_seed", 150, "")
tf.flags.DEFINE_integer("python_seed", 200, "")
tf.flags.DEFINE_float("dropout", 0.8, "dropout keep probability")
tf.flags.DEFINE_float("rnn_dropout", 0.9,
"dropout keep probability for rnn connections")
tf.flags.DEFINE_float("pad_int", -20000.0,
"number columns are padded with pad_int")
tf.flags.DEFINE_string("data_type", "double", "float or double")
tf.flags.DEFINE_float("word_dropout_prob", 0.9, "word dropout keep prob")
tf.flags.DEFINE_integer("word_cutoff", 10, "")
tf.flags.DEFINE_integer("vocab_size", 10800, "")
tf.flags.DEFINE_boolean("evaluator_job", False,
"wehther to run as trainer/evaluator")
tf.flags.DEFINE_float(
"bad_number_pre_process", -200000.0,
"number that is added to a corrupted table entry in a number column")
tf.flags.DEFINE_float("max_math_error", 3.0,
"max square loss error that is considered")
tf.flags.DEFINE_float("soft_min_value", 5.0, "")
FLAGS = tf.flags.FLAGS
class Utility:
#holds FLAGS and other variables that are used in different files
def __init__(self):
global FLAGS
self.FLAGS = FLAGS
self.unk_token = "UNK"
self.entry_match_token = "entry_match"
self.column_match_token = "column_match"
self.dummy_token = "dummy_token"
self.tf_data_type = {}
self.tf_data_type["double"] = tf.float64
self.tf_data_type["float"] = tf.float32
self.np_data_type = {}
self.np_data_type["double"] = np.float64
self.np_data_type["float"] = np.float32
self.operations_set = ["count"] + [
"prev", "next", "first_rs", "last_rs", "group_by_max", "greater",
"lesser", "geq", "leq", "max", "min", "word-match"
] + ["reset_select"] + ["print"]
self.word_ids = {}
self.reverse_word_ids = {}
self.word_count = {}
self.random = Random(FLAGS.python_seed)
def evaluate(sess, data, batch_size, graph, i):
#computes accuracy
num_examples = 0.0
gc = 0.0
for j in range(0, len(data) - batch_size + 1, batch_size):
[ct] = sess.run([graph.final_correct],
feed_dict=data_utils.generate_feed_dict(data, j, batch_size,
graph))
gc += ct * batch_size
num_examples += batch_size
print "dev set accuracy after ", i, " : ", gc / num_examples
print num_examples, len(data)
print "--------"
def Train(graph, utility, batch_size, train_data, sess, model_dir,
saver):
#performs training
curr = 0
train_set_loss = 0.0
utility.random.shuffle(train_data)
start = time.time()
for i in range(utility.FLAGS.train_steps):
curr_step = i
if (i > 0 and i % FLAGS.write_every == 0):
model_file = model_dir + "/model_" + str(i)
saver.save(sess, model_file)
if curr + batch_size >= len(train_data):
curr = 0
utility.random.shuffle(train_data)
step, cost_value = sess.run(
[graph.step, graph.total_cost],
feed_dict=data_utils.generate_feed_dict(
train_data, curr, batch_size, graph, train=True, utility=utility))
curr = curr + batch_size
train_set_loss += cost_value
if (i > 0 and i % FLAGS.eval_cycle == 0):
end = time.time()
time_taken = end - start
print "step ", i, " ", time_taken, " seconds "
start = end
print " printing train set loss: ", train_set_loss / utility.FLAGS.eval_cycle
train_set_loss = 0.0
def master(train_data, dev_data, utility):
#creates TF graph and calls trainer or evaluator
batch_size = utility.FLAGS.batch_size
model_dir = utility.FLAGS.output_dir + "/model" + utility.FLAGS.job_id + "/"
#create all paramters of the model
param_class = parameters.Parameters(utility)
params, global_step, init = param_class.parameters(utility)
key = "test" if (FLAGS.evaluator_job) else "train"
graph = model.Graph(utility, batch_size, utility.FLAGS.max_passes, mode=key)
graph.create_graph(params, global_step)
prev_dev_error = 0.0
final_loss = 0.0
final_accuracy = 0.0
#start session
with tf.Session() as sess:
sess.run(init.name)
sess.run(graph.init_op.name)
to_save = params.copy()
saver = tf.train.Saver(to_save, max_to_keep=500)
if (FLAGS.evaluator_job):
while True:
selected_models = {}
file_list = tf.gfile.ListDirectory(model_dir)
for model_file in file_list:
if ("checkpoint" in model_file or "index" in model_file or
"meta" in model_file):
continue
if ("data" in model_file):
model_file = model_file.split(".")[0]
model_step = int(
model_file.split("_")[len(model_file.split("_")) - 1])
selected_models[model_step] = model_file
file_list = sorted(selected_models.items(), key=lambda x: x[0])
if (len(file_list) > 0):
file_list = file_list[0:len(file_list) - 1]
print "list of models: ", file_list
for model_file in file_list:
model_file = model_file[1]
print "restoring: ", model_file
saver.restore(sess, model_dir + "/" + model_file)
model_step = int(
model_file.split("_")[len(model_file.split("_")) - 1])
print "evaluating on dev ", model_file, model_step
evaluate(sess, dev_data, batch_size, graph, model_step)
else:
ckpt = tf.train.get_checkpoint_state(model_dir)
print "model dir: ", model_dir
if (not (tf.gfile.IsDirectory(model_dir))):
print "create dir: ", model_dir
tf.gfile.MkDir(model_dir)
Train(graph, utility, batch_size, train_data, sess, model_dir,
saver)
def main(args):
utility = Utility()
train_name = "random-split-1-train.examples"
dev_name = "random-split-1-dev.examples"
test_name = "pristine-unseen-tables.examples"
#load data
dat = wiki_data.WikiQuestionGenerator(train_name, dev_name, test_name, FLAGS.data_dir)
train_data, dev_data, test_data = dat.load()
utility.words = []
utility.word_ids = {}
utility.reverse_word_ids = {}
#construct vocabulary
data_utils.construct_vocab(train_data, utility)
data_utils.construct_vocab(dev_data, utility, True)
data_utils.construct_vocab(test_data, utility, True)
data_utils.add_special_words(utility)
data_utils.perform_word_cutoff(utility)
#convert data to int format and pad the inputs
train_data = data_utils.complete_wiki_processing(train_data, utility, True)
dev_data = data_utils.complete_wiki_processing(dev_data, utility, False)
test_data = data_utils.complete_wiki_processing(test_data, utility, False)
print "# train examples ", len(train_data)
print "# dev examples ", len(dev_data)
print "# test examples ", len(test_data)
print "running open source"
#construct TF graph and train or evaluate
master(train_data, dev_data, utility)
if __name__ == "__main__":
tf.app.run()