def test_trainer(self):
num_batches = 5
x_train = np.random.randint(0, VOCABULARY_SIZE, [BATCH_SIZE * num_batches, SEQUENCE_LENGTH])
y_train = np.eye(2)[np.ones(BATCH_SIZE * num_batches, dtype=np.int32)]
train_data_iter = tfmodels.data.utils.batch_iter(
list(zip(x_train, y_train)), BATCH_SIZE, 1, fill=True, seed=42)
train_data_iter = map(lambda batch: zip(*batch), train_data_iter)
losses = []
with self.graph.as_default(), self.sess.as_default():
cnn = self._build_classifier()
t = CNNClassifierTrainer(cnn)
self.sess.run(tf.initialize_all_variables())
train_iter = t.train_loop(train_data_iter)
for loss, acc, current_step, time_delta in train_iter:
losses = losses + [loss]
self.assertEqual(len(losses), 5)
self.assertEqual(current_step, 5)
def test_trainer(self):
num_batches = 5
x_train = np.random.randint(
0, VOCABULARY_SIZE, [BATCH_SIZE * num_batches, SEQUENCE_LENGTH])
y_train = np.eye(2)[np.ones(BATCH_SIZE * num_batches, dtype=np.int32)]
train_data_iter = tfmodels.data.utils.batch_iter(list(
zip(x_train, y_train)),
BATCH_SIZE,
1,
fill=True,
seed=42)
train_data_iter = map(lambda batch: zip(*batch), train_data_iter)
losses = []
with self.graph.as_default(), self.sess.as_default():
cnn = self._build_classifier()
t = CNNClassifierTrainer(cnn)
self.sess.run(tf.initialize_all_variables())
train_iter = t.train_loop(train_data_iter)
for loss, acc, current_step, time_delta in train_iter:
losses = losses + [loss]
self.assertEqual(len(losses), 5)
self.assertEqual(current_step, 5)
os.makedirs(checkpoint_dir)
checkpoint_file = os.path.join(checkpoint_dir, "model.ckpt")
saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
# Initialization, optinally load from checkpoint
sess.run(tf.initialize_all_variables())
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
if latest_checkpoint:
print("Restoring checkpoint from {}".format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
# Initialize variables
sess.run(tf.initialize_all_variables())
# Training loop
for train_loss, train_acc, current_step, time_delta in trainer.train_loop(
train_data_iter):
examples_per_second = FLAGS.batch_size / time_delta
print("{}: step {}, loss {:g}, acc {:g} ({:g} examples/sec)".format(
datetime.now().isoformat(), current_step, train_loss, train_acc,
examples_per_second))
# Evaluate dev set
if current_step % FLAGS.evaluate_every == 0:
dev_iter = tfmodels.data.utils.xy_iter(x_dev, y_dev,
FLAGS.batch_size, 1)
mean_loss, acc, _ = evaluator.eval(dev_iter,
global_step=trainer.global_step)
print(
"{}: Step {}, Dev Accuracy: {:g}, Dev Mean Loss: {:g}".format(
datetime.now().isoformat(), current_step, acc, mean_loss))
os.makedirs(checkpoint_dir)
checkpoint_file = os.path.join(checkpoint_dir, "model.ckpt")
saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
# Initialization, optinally load from checkpoint
sess.run(tf.initialize_all_variables())
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
if latest_checkpoint:
print("Restoring checkpoint from {}".format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
# Initialize variables
sess.run(tf.initialize_all_variables())
# Training loop
for train_loss, train_acc, current_step, time_delta in trainer.train_loop(train_data_iter):
examples_per_second = FLAGS.batch_size/time_delta
print("{}: step {}, loss {:g}, acc {:g} ({:g} examples/sec)".format(
datetime.now().isoformat(), current_step, train_loss, train_acc, examples_per_second))
# Evaluate dev set
if current_step % FLAGS.evaluate_every == 0:
dev_iter = tfmodels.data.utils.xy_iter(x_dev, y_dev, FLAGS.batch_size, 1)
mean_loss, acc, _ = evaluator.eval(dev_iter, global_step=trainer.global_step)
print("{}: Step {}, Dev Accuracy: {:g}, Dev Mean Loss: {:g}".format(
datetime.now().isoformat(), current_step, acc, mean_loss))
# Checkpoint Model
if current_step % FLAGS.checkpoint_every == 0:
save_path = saver.save(sess, checkpoint_file, global_step=trainer.global_step)
print("Saved {}".format(save_path))