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better code to employ decaying learning rate #70

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better code to employ decaying learning rate #70

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6 changes: 4 additions & 2 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,12 +51,14 @@ def loop(prev, _):
args.vocab_size)
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
self.final_state = last_state
self.lr = tf.Variable(0.0, trainable=False)
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self.lr = tf.train.exponential_decay(
args.learning_rate, self.global_step, args.decay_step, args.decay_rate)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
args.grad_clip)
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
self.train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=self.global_step)

def sample(self, sess, chars, vocab, num=200, prime='The ', sampling_type=1):
state = sess.run(self.cell.zero_state(1, tf.float32))
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10 changes: 5 additions & 5 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,8 @@ def main():
def train(args):
data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length)
args.vocab_size = data_loader.vocab_size

args.decay_step = data_loader.num_batches

# check compatibility if training is continued from previously saved model
if args.init_from is not None:
# check if all necessary files exist
Expand Down Expand Up @@ -88,7 +89,6 @@ def train(args):
if args.init_from is not None:
saver.restore(sess, ckpt.model_checkpoint_path)
for e in range(args.num_epochs):
sess.run(tf.assign(model.lr, args.learning_rate * (args.decay_rate ** e)))
data_loader.reset_batch_pointer()
state = sess.run(model.initial_state)
for b in range(data_loader.num_batches):
Expand All @@ -98,12 +98,12 @@ def train(args):
for i, (c, h) in enumerate(model.initial_state):
feed[c] = state[i].c
feed[h] = state[i].h
train_loss, state, _ = sess.run([model.cost, model.final_state, model.train_op], feed)
lr, train_loss, state, _ = sess.run([model.lr, model.cost, model.final_state, model.train_op], feed)
end = time.time()
print("{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \
print("{}/{} (epoch {}), lr = {:.6f}, train_loss = {:.3f}, time/batch = {:.3f}" \
.format(e * data_loader.num_batches + b,
args.num_epochs * data_loader.num_batches,
e, train_loss, end - start))
e, lr, train_loss, end - start))
if (e * data_loader.num_batches + b) % args.save_every == 0\
or (e==args.num_epochs-1 and b == data_loader.num_batches-1): # save for the last result
checkpoint_path = os.path.join(args.save_dir, 'model.ckpt')
Expand Down