def main(_):
print("Loading hyperparameters..")
params = util.load_params(FLAGS.params_file)
print("Building model..")
model_dir = FLAGS.model_dir
if FLAGS.clean_model_dir:
util.clean_model_dir(model_dir)
if FLAGS.model_cls == "transformer":
model_cls = TransformerEstimator
elif FLAGS.model_cls == "seq2seq":
model_cls = Seq2SeqEstimator
else:
raise ValueError("Model class not supported.")
model = model_cls(model_dir, params)
print("Getting sources..")
fields = {"train/inputs": "int", "train/targets": "int"}
train_source = DataSource(FLAGS.train_file, fields)
test_source = DataSource(FLAGS.test_file, fields)
field_map = {"inputs": "train/inputs", "targets": "train/targets"}
train_input_fn = train_source.get_input_fn(
"train_in", field_map, None, FLAGS.batch_size)
test_input_fn = test_source.get_input_fn(
"test_in", field_map, 1, FLAGS.batch_size)
print("Processing model..")
model.train(train_input_fn, steps=FLAGS.train_batches)
model.evaluate(test_input_fn)
if FLAGS.interactive:
print("Interactive decoding...")
vocab = Vocabulary(fname=params["vocab_file"])
decoding.cmd_decode(model, vocab)
def main(_):
print("Loading hyperparameters..")
params = util.load_params(FLAGS.params_file)
print("Building model..")
validation_config = tf.estimator.RunConfig(
save_checkpoints_steps=100,
keep_checkpoint_max=None,
)
model_dir = FLAGS.model_dir
if FLAGS.clean_model_dir:
util.clean_model_dir(model_dir)
if FLAGS.model_cls == "transformer":
model_cls = TransformerEstimator
elif FLAGS.model_cls == "seq2seq":
model_cls = Seq2SeqEstimator
else:
raise ValueError("Model class not supported.")
model = model_cls(model_dir, params, config=validation_config)
print("Getting sources..")
fields = {"train/inputs": "int", "train/targets": "int"}
train_source = DataSource(FLAGS.train_file, fields)
test_source = DataSource(FLAGS.test_file, fields)
field_map = {"inputs": "train/inputs", "targets": "train/targets"}
train_input_fn = train_source.get_input_fn("train_in", field_map, None,
FLAGS.batch_size)
test_input_fn = test_source.get_input_fn("test_in", field_map, 1,
FLAGS.batch_size)
print("Processing model..")
model.train(train_input_fn, steps=FLAGS.train_batches)
model.choose_best_checkpoint(test_input_fn)
model.evaluate(test_input_fn)
if FLAGS.interaction != "off":
print("Interactive decoding...")
vocab = Vocabulary(fname=params["vocab_file"])
if FLAGS.interaction == "cmd":
decoding.cmd_decode(model, vocab, persona=True)
elif FLAGS.interaction == "gui":
decoding.gui_decode(model, vocab)
def main(_):
print("Loading parameters..")
params = util.load_params(FLAGS.params_file)
print("Building model..")
model_dir = FLAGS.model_dir
if FLAGS.clean_model_dir:
util.clean_model_dir(model_dir)
first_model = PersonaSeq2SeqEstimator(model_dir, params, scope="first")
second_model_encoder = Seq2SeqEncoderEstimator(model_dir,
params,
scope="second_encoder")
second_model = EstimatorChain([second_model_encoder, first_model.decoder],
model_dir,
params,
scope="second")
mmi_model = PersonaSeq2SeqEstimator(model_dir,
params,
scope="mmi",
is_mmi_model=True)
model_group = EstimatorGroup([first_model, second_model, mmi_model],
model_dir,
params,
scope="group")
print("Getting sources..")
fields = {
"train/inputs": "int",
"train/targets": "int",
"train/speakers": "int"
}
train_source = DataSource(FLAGS.train_file, fields)
autoenc_source = DataSource(FLAGS.autoenc_file, fields)
test_source = DataSource(FLAGS.test_file, fields)
train_field_map = {
"inputs": "train/inputs",
"targets": "train/targets",
"speaker_ids": "train/speakers"
}
autoenc_field_map = {
"inputs": "train/inputs",
"targets": "train/inputs",
"speaker_ids": "train/speakers"
}
mmi_field_map = {
"inputs": "train/targets",
"targets": "train/inputs",
"speaker_ids": "train/speakers"
}
paired_input_fn = train_source.get_input_fn("paired_in", train_field_map,
None, FLAGS.batch_size)
autoenc_input_fn = train_source.get_input_fn("autoenc_in",
autoenc_field_map, None,
FLAGS.batch_size)
mmi_input_fn = train_source.get_input_fn("mmi_in", mmi_field_map, None,
FLAGS.batch_size)
train_input_fn = DataSource.group_input_fns(
["first", "second", "mmi"],
[paired_input_fn, autoenc_input_fn, mmi_input_fn])
test_input_fn = test_source.get_input_fn("test_in", train_field_map, 1,
FLAGS.batch_size)
print("Processing models..")
print("Pretraining primary model..")
model_group.train(train_input_fn,
first_model,
steps=FLAGS.pretrain_batches)
print("Multitask training..")
model_group.train(train_input_fn, {
"first": 1,
"second": 1,
"mmi": 0
},
steps=FLAGS.train_batches)
print("Training MMI model..")
model_group.train(train_input_fn, mmi_model, steps=FLAGS.mmi_batches)
print("Evaluating..")
model_group.evaluate(test_input_fn, first_model)
if FLAGS.interactive:
print("Interactive decoding...")
vocab = Vocabulary(fname=params["vocab_file"])
decoding.cmd_decode(first_model,
vocab,
persona=True,
mmi_component=mmi_model)
def main(_):
'''
This is a simple example of how to build an Icecaps training script, and is essentially
the "Hello World" of Icecaps. Icecaps training scripts follow a basic five-phase pattern
that we describe here. We train a basic model on the paired data stored in
dummy_data/paired_personalized.tfrecord. For information on how to build TFRecords
from text data files, please see data_processing_example.py.
'''
print("Loading hyperparameters..")
# The first phase is to load hyperparameters from a .params file. These files follow a
# simple colon-delimited format (e.g. see dummy_params/simple_example_seq2seq.params).
params = util.load_params(FLAGS.params_file)
print("Building model..")
# Second, we build our architecture based on our loaded hyperparameters. Our architecture
# here is very basic: we use a simple LSTM-based seq2seq model. For information on more
# complex architectures, wee train_persona_mmi_example.py.
model_dir = FLAGS.model_dir
if FLAGS.clean_model_dir:
util.clean_model_dir(model_dir)
model_cls = Seq2SeqEstimator
# Every estimator expects a different set of hyperparmeters. If you set use_default_params
# to True in your .params file, the estimator will employ default values for any unspecified
# hyperparameters. To view the list of hyperparmeters with default values, you can run the
# class method list_params(). E.g. you can open a Python session and run
# Seq2SeqEstimator.list_params() to view what hyperparameters our seq2seq estimator expects.
model = model_cls(model_dir, params)
print("Getting sources..")
# Third, we set up our data sources. DataSource objects allow you to build input_fns that
# efficiently feed data into the training pipeline from TFRecord files. In our simple example,
# we set up two data sources: one for training and one for testing.
# TFRecords are created with name variables per data point. You must create a fields dictionary
# to tell the DataSource which variables to load and what their types are.
fields = {"train/inputs": "int", "train/targets": "int"}
train_source = DataSource(FLAGS.train_file, fields)
test_source = DataSource(FLAGS.test_file, fields)
# Then, you must create a field_map dictionary to tell your estimator how to map the TFRecord's
# variable names to the names expected by the estimator. While this may seem like unnecessary
# overhead in this simple example, it provides useful flexibility in more complex scenarios.
field_map = {"inputs": "train/inputs", "targets": "train/targets"}
# Finally, build input_fns from your DataSources.
train_input_fn = train_source.get_input_fn(
"train_in", field_map, None,
FLAGS.batch_size) # None lets our input_fn run for an unbounded
# number of epochs.
test_input_fn = test_source.get_input_fn(
"test_in", field_map, 1,
FLAGS.batch_size) # For testing, we only want to run the input_fn
# for one epoch instead.
print("Processing model..")
# Fourth, we pipe our input_fns through our model for training and evaluation.
model.train(train_input_fn, steps=FLAGS.train_batches)
model.evaluate(test_input_fn)
if FLAGS.interactive:
print("Interactive decoding...")
# Fifth, you may optionally set up an interactive session to test your system by directly
# engaging with it.
vocab = Vocabulary(fname=params["vocab_file"])
decoding.cmd_decode(model, vocab)
def main(_):
'''
This is a more complex example in which we build an Icecaps script involving
component chaining and multi-task learning. We recommend you start with
train_simple_example.py. In this example, we build a personalized conversation system
that combines paired and unpaired data, and applies MMI during decoding.
'''
print("Loading parameters..")
# When multiple estimators are involved, you can specify which hyperparameters in your
# params file belong to which estimator using scoping. See dummy_params/persona_mmi_example.params
# for an example. If no scope is specified, the hyperparameter is provided to all
# models in your architecture.
params = util.load_params(FLAGS.params_file)
print("Building model..")
model_dir = FLAGS.model_dir
if FLAGS.clean_model_dir:
util.clean_model_dir(model_dir)
# For this system, we will need to build three different estimators.
# The first estimator is a personalized seq2seq estimator that will be responsible for
# learning the conversational model.
first_model = PersonaSeq2SeqEstimator(model_dir, params, scope="first")
# The second estimator is a personalized seq2seq estimator that shares its decoder with
# the first model. This model will learn an autoencoder on an unpaired personalized
# data set. The purpose of this configuration is to influence the first model with
# stylistic information from the unpaired dataset.
# To construct this second estimator, we first build a seq2seq encoder separate from
# the first model. Then, we use an EstimatorChain to chain that encoder to the first
# model's decoder, allowing the two models to share that decoder.
second_model_encoder = Seq2SeqEncoderEstimator(model_dir,
params,
scope="second_encoder")
second_model = EstimatorChain([second_model_encoder, first_model.decoder],
model_dir,
params,
scope="second")
# The third estimator is used for MMI decoding. This model will learn the inverse
# function of the first model. During decoding, this estimator will be used to rerank
# hypotheses generated by the first model during beam search decoding. While this
# won't have much of an effect on our toy data sets, the purpose of this model in
# real-world settings is to penalize generic responses applicable to many contexts
# such as "I don't know."
mmi_model = PersonaSeq2SeqEstimator(model_dir,
params,
scope="mmi",
is_mmi_model=True)
model_group = EstimatorGroup([first_model, second_model, mmi_model],
model_dir,
params,
scope="group")
print("Getting sources..")
# We will use two DataSources for training and one for testing.
fields = {
"train/inputs": "int",
"train/targets": "int",
"train/speakers": "int"
}
paired_source = DataSource(FLAGS.paired_file, fields)
unpaired_source = DataSource(FLAGS.unpaired_file, fields)
test_source = DataSource(FLAGS.test_file, fields)
# We construct three field maps.
# The paired field map is similar to the field map shown in train_simple_example.py
# The unpaired field map maps train/inputs to both the estimator's inputs and targets,
# in order to train an autoencoder.
# The mmi field maps maps train/inputs to targets and train/targets to inputs, in
# order to learn the inverse of the first estimator.
paired_field_map = {
"inputs": "train/inputs",
"targets": "train/targets",
"speaker_ids": "train/speakers"
}
unpaired_field_map = {
"inputs": "train/inputs",
"targets": "train/inputs",
"speaker_ids": "train/speakers"
}
mmi_field_map = {
"inputs": "train/targets",
"targets": "train/inputs",
"speaker_ids": "train/speakers"
}
paired_input_fn = paired_source.get_input_fn("paired_in", paired_field_map,
None, FLAGS.batch_size)
unpaired_input_fn = unpaired_source.get_input_fn("unpaired_in",
unpaired_field_map, None,
FLAGS.batch_size)
mmi_input_fn = paired_source.get_input_fn("mmi_in", mmi_field_map, None,
FLAGS.batch_size)
# For multi-task learning, you will need to group your input_fns together with group_input_fns().
train_input_fn = DataSource.group_input_fns(
["first", "second", "mmi"],
[paired_input_fn, unpaired_input_fn, mmi_input_fn])
test_input_fn = test_source.get_input_fn("test_in", paired_field_map, 1,
FLAGS.batch_size)
print("Processing models..")
# Icecaps supports flexible multi-task training pipelines. You can set up multiple phases
# where each phase trains your architecture with different weights across your objectives.
# In this example, we will first pre-train the first model by itself, then jointly train
# the first and second models, then finally train the MMI model by itself.
print("Pretraining primary model..")
model_group.train(train_input_fn,
first_model,
steps=FLAGS.pretrain_batches)
print("Multitask training..")
model_group.train(train_input_fn, {
"first": 1,
"second": 1,
"mmi": 0
},
steps=FLAGS.train_batches)
print("Training MMI model..")
model_group.train(train_input_fn, mmi_model, steps=FLAGS.mmi_batches)
print("Evaluating..")
model_group.evaluate(test_input_fn, first_model)
if FLAGS.interactive:
print("Interactive decoding...")
vocab = Vocabulary(fname=params["vocab_file"])
# To decode with MMI, you can pass in your MMI model to cmd_decode().
# lambda_balance represents how the first model and MMI model's scores are weighted during decoding.
decoding.cmd_decode(first_model,
vocab,
persona=True,
mmi_component=mmi_model,
lambda_balance=FLAGS.lambda_balance)