def train_and_evaluate(seed, model_dir, num_epochs, batch_size, embedding_size,
hidden_size, min_freq, max_seq_len, dropout,
emb_dropout, word_dropout_rate, learning_rate,
checkpoints_to_keep, l2_reg):
"""Executes model training and evaluation loop.
Args:
seed: Random seed for network initialization.
model_dir: Directory to store model data.
num_epochs: Number of training epochs.
batch_size: Batch size for training.
embedding_size: Size of the word embeddings.
hidden_size: Hidden size for the LSTM and MLP.
min_freq: Minimum frequency for training set words to be in vocabulary.
max_seq_len: Maximum sequence length in the dataset.
dropout: Dropout rate.
emb_dropout: Embedding dropout rate.
word_dropout_rate: Word dropout rate.
learning_rate: The learning rate for the Adam optimizer.
checkpoints_to_keep: Number of checkpoints to keep.
l2_reg: L2 regularization to keep.
"""
tf.enable_v2_behavior()
# Prepare data.
data_source = input_pipeline.SST2DataSource(min_freq=min_freq)
# Create model.
model = sst2_model.create_model(
seed, batch_size, max_seq_len,
dict(vocab_size=data_source.vocab_size,
embedding_size=embedding_size,
hidden_size=hidden_size,
output_size=1,
unk_idx=data_source.unk_idx,
dropout=dropout,
emb_dropout=emb_dropout,
word_dropout_rate=word_dropout_rate))
# Train the model.
_, model = train(model,
learning_rate=learning_rate,
num_epochs=num_epochs,
seed=seed,
model_dir=model_dir,
data_source=data_source,
batch_size=batch_size,
checkpoints_to_keep=checkpoints_to_keep,
l2_reg=l2_reg)
# Evaluate the best model.
valid_batches = input_pipeline.get_batches(data_source.valid_dataset,
batch_size=batch_size)
metrics = evaluate(model, valid_batches)
logging.info('Best validation accuracy: %.2f', metrics['acc'])
def main(argv):
"""Main function."""
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
assert FLAGS.model_dir is not None, 'Please provide model_dir.'
if not gfile.exists(FLAGS.model_dir):
gfile.makedirs(FLAGS.model_dir)
tf.enable_v2_behavior()
# Prepare data.
data_source = input_pipeline.SST2DataSource(min_freq=FLAGS.min_freq)
# Create model.
model = sst2_model.create_model(
FLAGS.seed, FLAGS.batch_size, FLAGS.max_seq_len,
dict(vocab_size=data_source.vocab_size,
embedding_size=FLAGS.embedding_size,
hidden_size=FLAGS.hidden_size,
output_size=1,
unk_idx=data_source.unk_idx,
dropout=FLAGS.dropout,
emb_dropout=FLAGS.emb_dropout,
word_dropout_rate=FLAGS.word_dropout_rate))
# Train the model.
train_stats, model = train(model,
learning_rate=FLAGS.learning_rate,
num_epochs=FLAGS.num_epochs,
seed=FLAGS.seed,
model_dir=FLAGS.model_dir,
data_source=data_source,
batch_size=FLAGS.batch_size,
checkpoints_to_keep=FLAGS.checkpoints_to_keep,
l2_reg=FLAGS.l2_reg)
# Evaluate the best model.
valid_batches = input_pipeline.get_batches(data_source.valid_dataset,
batch_size=FLAGS.batch_size)
metrics = evaluate(model, valid_batches)
logging.info('Best validation accuracy: %.2f', metrics['acc'])