def test_eval(self):
x_dev = np.random.randint(0, VOCABULARY_SIZE, [BATCH_SIZE * 5, SEQUENCE_LENGTH])
y_dev = np.eye(2)[np.ones(BATCH_SIZE * 5, dtype=np.int32)]
def make_eval_iter():
data_iter = tfmodels.data.utils.batch_iter(list(zip(x_dev, y_dev)), BATCH_SIZE, 1, fill=True, seed=42)
return map(lambda batch: zip(*batch), data_iter)
with self.graph.as_default(), self.sess.as_default():
rnn = self._build_classifier()
ev = RNNClassifierEvaluator(rnn)
self.sess.run(tf.initialize_all_variables())
loss, acc, current_step = ev.eval(make_eval_iter())
loss2, acc2, current_step = ev.eval(make_eval_iter())
self.assertGreater(loss, 0)
self.assertGreater(acc, 0)
self.assertEqual(loss, loss2)
self.assertEqual(acc, acc2)
def test_eval(self):
x_dev = np.random.randint(0, VOCABULARY_SIZE,
[BATCH_SIZE * 5, SEQUENCE_LENGTH])
y_dev = np.eye(2)[np.ones(BATCH_SIZE * 5, dtype=np.int32)]
def make_eval_iter():
data_iter = tfmodels.data.utils.batch_iter(list(zip(x_dev, y_dev)),
BATCH_SIZE,
1,
fill=True,
seed=42)
return map(lambda batch: zip(*batch), data_iter)
with self.graph.as_default(), self.sess.as_default():
rnn = self._build_classifier()
ev = RNNClassifierEvaluator(rnn)
self.sess.run(tf.initialize_all_variables())
loss, acc, current_step = ev.eval(make_eval_iter())
loss2, acc2, current_step = ev.eval(make_eval_iter())
self.assertGreater(loss, 0)
self.assertGreater(acc, 0)
self.assertEqual(loss, loss2)
self.assertEqual(acc, acc2)
model_params.update(FLAGS.__flags)
model = RNNClassifier.from_dict(model_params)
model.print_params()
x = tf.placeholder(tf.int32, [FLAGS.batch_size, SEQUENCE_LENGTH])
y = tf.placeholder(tf.float32, [FLAGS.batch_size, 2])
model.build_graph(x, y)
# Directory for training and dev summaries
timestamp = str(int(time.time()))
rundir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
train_dir = os.path.join(rundir, "train")
dev_dir = os.path.join(rundir, "dev")
# Build the Trainer/Evaluator
trainer = RNNClassifierTrainer(model, train_summary_dir=train_dir)
evaluator = RNNClassifierEvaluator(model, summary_dir=dev_dir)
# Saving/Checkpointing
checkpoint_dir = os.path.join(rundir, "checkpoints")
if not os.path.exists(checkpoint_dir):
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)
model_params.update(FLAGS.__flags)
model = RNNClassifier.from_dict(model_params)
model.print_params()
x = tf.placeholder(tf.int32, [FLAGS.batch_size, SEQUENCE_LENGTH])
y = tf.placeholder(tf.float32, [FLAGS.batch_size, 2])
model.build_graph(x, y)
# Directory for training and dev summaries
timestamp = str(int(time.time()))
rundir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
train_dir = os.path.join(rundir, "train")
dev_dir = os.path.join(rundir, "dev")
# Build the Trainer/Evaluator
trainer = RNNClassifierTrainer(model, train_summary_dir=train_dir)
evaluator = RNNClassifierEvaluator(model, summary_dir=dev_dir)
# Saving/Checkpointing
checkpoint_dir = os.path.join(rundir, "checkpoints")
if not os.path.exists(checkpoint_dir):
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)
