from rnn.build_model.build_model_vanilla import build_model_vanilla
from rnn.build_model.build_model_lstm import build_model_lstm
from rnn.build_model.build_model_cw import build_model_cw
from rnn.build_model.build_model_soft import build_model_soft
from rnn.build_model.build_model_hard import build_model_hard
from rnn.build_model.build_model_residual import build_model_residual
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":
        (cost, unregularized_cost, updates,
         hidden_states) = build_model_hard(args)
    elif rnn_type == "residual":
        (cost, unregularized_cost, updates,
         hidden_states) = build_model_residual(args)
    else:
        assert(False)

    # Train the model
    if args.visualize is None:
        train_model(cost, unregularized_cost, updates,
                    train_stream, valid_stream,
                    args,
                    gate_values=gate_values)
    else:
        run_visualizations(cost, updates,
                           train_stream, valid_stream,
                           args,
                           hidden_states=hidden_states,
                           gate_values=gate_values,)

714e51ff2aef1d6b74aa032c9aaea33d816739dc
<mask token>

<mask token>
if __name__ == '__main__':
    args = parse_args()
    rnn_type = args.rnn_type
    assert not (args.skip_connections and args.layers == 1)
    train_stream, valid_stream = get_minibatch(args)
    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':
        cost, unregularized_cost, updates, hidden_states = build_model_hard(
            args)
    elif rnn_type == 'residual':
        cost, unregularized_cost, updates, hidden_states = (
            build_model_residual(args))
    else:
        assert False
    if args.visualize is None:
        train_model(cost, unregularized_cost, updates, train_stream,
            valid_stream, args, gate_values=gate_values)
    else:
        run_visualizations(cost, updates, train_stream, valid_stream, args,
            hidden_states=hidden_states, gate_values=gate_values)

from rnn.build_model.build_model_vanilla import build_model_vanilla
from rnn.build_model.build_model_lstm import build_model_lstm
from rnn.build_model.build_model_cw import build_model_cw
from rnn.build_model.build_model_soft import build_model_soft
from rnn.build_model.build_model_hard import build_model_hard
from rnn.build_model.build_model_residual import build_model_residual
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
    assert not (args.skip_connections and args.layers == 1)
    train_stream, valid_stream = get_minibatch(args)
    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':
        cost, unregularized_cost, updates, hidden_states = build_model_hard(
            args)
    elif rnn_type == 'residual':
        cost, unregularized_cost, updates, hidden_states = (
            build_model_residual(args))
    else:
        assert False
    if args.visualize is None:
        train_model(cost, unregularized_cost, updates, train_stream,
            valid_stream, args, gate_values=gate_values)
    else:
        run_visualizations(cost, updates, train_stream, valid_stream, args,
            hidden_states=hidden_states, gate_values=gate_values)

from rnn.build_model.build_model_vanilla import build_model_vanilla
from rnn.build_model.build_model_lstm import build_model_lstm
from rnn.build_model.build_model_cw import build_model_cw
from rnn.build_model.build_model_soft import build_model_soft
from rnn.build_model.build_model_hard import build_model_hard
from rnn.build_model.build_model_residual import build_model_residual
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":
        (cost, unregularized_cost, updates,
         hidden_states) = build_model_hard(args)
    elif rnn_type == "residual":
        (cost, unregularized_cost, updates,
         hidden_states) = build_model_residual(args)
    else:
        assert(False)

    # Train the model
    if args.visualize is None:
        train_model(cost, unregularized_cost, updates,
                    train_stream, valid_stream,
                    args,
                    gate_values=gate_values)
    else:
        run_visualizations(cost, updates,
                           train_stream, valid_stream,
                           args,
                           hidden_states=hidden_states,
                           gate_values=gate_values,)
