default='rnn',
choices=['rnn', 'birnn'],
help='type of recurrent layer to use (rnn or birnn)')
parser.add_argument('--use_lut',
action='store_true',
help='choose to use lut as first layer')
parser.set_defaults()
args = parser.parse_args()
# these hyperparameters are from the paper
args.batch_size = 50
time_steps = 150
hidden_size = 500
# download penn treebank
tree_bank_data = PTB(path=args.data_dir)
ptb_data = tree_bank_data.load_data()
train_set = SequentialArrayIterator(ptb_data['train'],
batch_size=args.batch_size,
time_steps=time_steps,
total_iterations=args.num_iterations)
valid_set = SequentialArrayIterator(ptb_data['valid'],
batch_size=args.batch_size,
time_steps=time_steps)
inputs = train_set.make_placeholders()
ax.Y.length = len(tree_bank_data.vocab)
def expand_onehot(x):
from ngraph.frontends.neon import PTB
# parse the command line arguments
parser = NgraphArgparser(__doc__)
parser.set_defaults(batch_size=128, num_iterations=2000)
args = parser.parse_args()
# model parameters
time_steps = 5
hidden_size = 256
gradient_clip_value = 5
# download penn treebank
# set shift_target to be False, since it is going to predict the same sequence
tree_bank_data = PTB(path=args.data_dir, shift_target=False)
ptb_data = tree_bank_data.load_data()
train_set = SequentialArrayIterator(ptb_data['train'],
batch_size=args.batch_size,
time_steps=time_steps,
total_iterations=args.num_iterations,
reverse_target=True,
get_prev_target=True)
valid_set = SequentialArrayIterator(ptb_data['valid'],
batch_size=args.batch_size,
time_steps=time_steps,
total_iterations=10,
reverse_target=True,
get_prev_target=True)
inputs = train_set.make_placeholders()