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train.py
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train.py
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import os
import time
import datetime
import numpy as np
import tensorflow as tf
import utils.data_utils as utils
from models.quinn import Quinn
tf.logging.set_verbosity(tf.logging.ERROR)
train_file = './data/dumps/train.pckl'
val_file = './data/dumps/val.pckl'
# Model Parameters
max_length = 600
vocab_size = 3193
embedding_dims = 300
hidden_layers = 256
# Training Parameters
l2_lambda = 1e-4
batch_size = 64
num_epochs = 100
num_checkpoints = 3
checkpoint_every = 10
# Prepare and load training and validation data
if not os.path.exists(train_file):
print ("Train dump not found. Preparing data ...")
train_tsv_path = './data/english/All_Train.tsv'
utils.create_dump(train_tsv_path, train_file)
if not os.path.exists(val_file):
print ("Validation dump not found. Preparing data ...")
val_tsv_path = './data/english/All_Dev.tsv'
utils.create_dump(val_tsv_path, val_file)
print ('Loading dataset from ./data/dumps/ ...')
x_train, x_train_map, y_train, y_train_prob = utils.fetch(train_file)
x_val, x_val_map, y_val, y_val_prob = utils.fetch(val_file)
# Load embeddings
embedding_path = './data/dumps/embeddings.npy'
embedding = utils.load_embeddings(embedding_path, vocab_size, dimensions=300)
print ("Embeddings loaded, Vocabulary Size: {:d}.".format(vocab_size))
# Shuffle training data
np.random.seed(10)
shuff_idx = np.random.permutation(np.arange(len(y_train)))
x_train, x_train_map, y_train, y_train_prob = \
x_train[shuff_idx], x_train_map[shuff_idx], y_train[shuff_idx], y_train_prob[shuff_idx]
print ("Generating graph and starting training ...")
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False)
sess = tf.Session(config=session_conf)
with sess.as_default():
quinn = Quinn(max_length=max_length, vocab_size=vocab_size,
embedding_dims=embedding_dims,
hidden_layers=hidden_layers,
l2_lambda=l2_lambda)
# Define Training procedure
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(quinn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
# Output directory for models and summaries
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(os.path.curdir, 'runs', timestamp))
print("Writing to {}\n".format(out_dir))
# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
checkpoint_dir = os.path.abspath(os.path.join(out_dir, 'checkpoints'))
checkpoint_prefix = os.path.join(checkpoint_dir, 'model')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=num_checkpoints)
# Initialize all variables
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
sess.run(quinn.embedding_init, feed_dict={quinn.embedding_placeholder: embedding})
def train_step(x_batch, x_map, y_batch):
# seq_length = np.array([list(x).index(0) + 1 for x in x_batch])
feed_dict = {
quinn.input_x: x_batch,
quinn.input_y: y_batch,
quinn.attention_map: x_map
}
_, step, loss, mae, _update_op = sess.run(
[train_op, global_step, quinn.loss, quinn.mae, quinn.update_op],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, mae {:g}".format(time_str, step, loss, mae))
def val_step(x_batch, x_map, y_batch):
feed_dict = {
quinn.input_x: x_batch,
quinn.input_y: y_batch,
quinn.attention_map: x_map
}
step, loss, mae, _update_op = sess.run(
[global_step, quinn.loss, quinn.mae, quinn.update_op],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, mae {:g}".format(time_str, step, loss, mae))
# Generate batches
batches = utils.batch_iter(list(zip(x_train, x_train_map, y_train_prob)), batch_size, num_epochs)
# Training loop. For each batch...
for batch in batches:
x_batch, x_map, y_batch = zip(*batch)
train_step(x_batch, x_map, y_batch)
current_step = tf.train.global_step(sess, global_step)
epoch_step = (int((len(x_train[0]) - 1) / batch_size) + 1)
if current_step % epoch_step == 0:
print("\nValidation:")
# Randomly draw a validation batch
shuff_idx = np.random.permutation(np.arange(batch_size))
x_batch_val, x_batch_val_map, y_batch_val_prob = \
x_val[shuff_idx], x_val_map[shuff_idx], y_val_prob[shuff_idx]
val_step(x_batch_val, x_batch_val_map, y_batch_val_prob)
print("")
if current_step % checkpoint_every == 0:
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))