if __name__ == "__main__":
args = parse_args()
dataset = args.dataset
mini_batch_size = args.mini_batch_size
mini_batch_size_valid = args.mini_batch_size_valid
time_length = args.time_length
rnn_type = args.rnn_type
# Make sure we don't have skip_connections with only one hidden layer
assert(not(args.skip_connections and args.layers == 1))
# Prepare data
train_stream, valid_stream = get_minibatch(
dataset, mini_batch_size, mini_batch_size_valid,
time_length, args.tot_num_char)
# Build the model
gate_values = None
if rnn_type == "simple":
(cost, unregularized_cost, updates,
hidden_states) = build_model_vanilla(args)
elif rnn_type == "clockwork":
cost, unregularized_cost, updates, hidden_states = build_model_cw(args)
elif rnn_type == "lstm":
(cost, unregularized_cost, updates, gate_values,
hidden_states) = build_model_lstm(args)
elif rnn_type == "soft":
(cost, unregularized_cost, updates, gate_values,
hidden_states) = build_model_soft(args)
if __name__ == "__main__":
args = parse_args()
dataset = args.dataset
mini_batch_size = args.mini_batch_size
mini_batch_size_valid = args.mini_batch_size_valid
time_length = args.time_length
rnn_type = args.rnn_type
# Make sure we don't have skip_connections with only one hidden layer
assert (not (args.skip_connections and args.layers == 1))
# Prepare data
train_stream, valid_stream = get_minibatch(dataset, mini_batch_size,
mini_batch_size_valid,
time_length, args.tot_num_char)
# Build the model
gate_values = None
if rnn_type == "simple":
(cost, unregularized_cost, updates,
hidden_states) = build_model_vanilla(args)
elif rnn_type == "clockwork":
cost, unregularized_cost, updates, hidden_states = build_model_cw(args)
elif rnn_type == "lstm":
(cost, unregularized_cost, updates, gate_values,
hidden_states) = build_model_lstm(args)
elif rnn_type == "soft":
(cost, unregularized_cost, updates, gate_values,
hidden_states) = build_model_soft(args)
from rnn.datasets.dataset import get_minibatch
from rnn.train import train_model
from rnn.utils import parse_args
from rnn.visualize import run_visualizations
if __name__ == "__main__":
args = parse_args()
rnn_type = args.rnn_type
# Make sure we don't have skip_connections with only one hidden layer
assert (not (args.skip_connections and args.layers == 1))
# Prepare data
train_stream, valid_stream = get_minibatch(args)
# Build the model
gate_values = None
if rnn_type == "simple":
(cost, unregularized_cost, updates,
hidden_states) = build_model_vanilla(args)
elif rnn_type == "clockwork":
cost, unregularized_cost, updates, hidden_states = build_model_cw(args)
elif rnn_type == "lstm":
(cost, unregularized_cost, updates, gate_values,
hidden_states) = build_model_lstm(args)
elif rnn_type == "soft":
(cost, unregularized_cost, updates, gate_values,
hidden_states) = build_model_soft(args)
elif rnn_type == "hard":
from rnn.datasets.dataset import get_minibatch
from rnn.train import train_model
from rnn.utils import parse_args
from rnn.visualize import run_visualizations
if __name__ == "__main__":
args = parse_args()
rnn_type = args.rnn_type
# Make sure we don't have skip_connections with only one hidden layer
assert(not(args.skip_connections and args.layers == 1))
# Prepare data
train_stream, valid_stream = get_minibatch(args)
# Build the model
gate_values = None
if rnn_type == "simple":
(cost, unregularized_cost, updates,
hidden_states) = build_model_vanilla(args)
elif rnn_type == "clockwork":
cost, unregularized_cost, updates, hidden_states = build_model_cw(args)
elif rnn_type == "lstm":
(cost, unregularized_cost, updates, gate_values,
hidden_states) = build_model_lstm(args)
elif rnn_type == "soft":
(cost, unregularized_cost, updates, gate_values,
hidden_states) = build_model_soft(args)
elif rnn_type == "hard":
