Ejemplos de Experiment en Python

Ejemplo n.º 1

        filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.leaky_rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.Conv2DLayer(
        network,
        num_filters=8,
        filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.leaky_rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.DenseLayer(
        lasagne.layers.dropout(network, p=.5),
        num_units=2,
        nonlinearity=lasagne.nonlinearities.softmax)

    return network


if __name__ == '__main__':
    if len(sys.argv) < 4:
        print('Usage: python3 path/to/data_dir path/to/output_dir n_epochs')
        exit()
    exp = Experiment(data=sys.argv[1], directory=sys.argv[2], network=network)
    n = 10 if len(sys.argv) == 3 else int(sys.argv[3])
    exp.train(epochs=n)
    exp.test()
    exp.save()

Ejemplo n.º 2

Archivo: testnetwork.py Proyecto: rchurchley/target-deep-learning

    network = lasagne.layers.Conv2DLayer(
        network, num_filters=8, filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.leaky_rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.Conv2DLayer(
        network, num_filters=8, filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.leaky_rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.DenseLayer(
        lasagne.layers.dropout(network, p=.5),
        num_units=2,
        nonlinearity=lasagne.nonlinearities.softmax)

    return network

if __name__ == '__main__':
    if len(sys.argv) < 3:
        print('Usage: python3 path/to/data_dir path/to/experiment_dir')
        exit()
    exp = Experiment(data=sys.argv[1], directory=sys.argv[2], network=network)
    if len(sys.argv) < 4:
        exp.load_parameters()
    else:
        exp.load_parameters(sys.argv[3])
    exp.test()

Ejemplo n.º 3

Archivo: runexperiment.py Proyecto: rchurchley/target-deep-learning

        input_var=input_var)

    network = lasagne.layers.Conv2DLayer(
        network, num_filters=8, filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.leaky_rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.Conv2DLayer(
        network, num_filters=8, filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.leaky_rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.DenseLayer(
        lasagne.layers.dropout(network, p=.5),
        num_units=2,
        nonlinearity=lasagne.nonlinearities.softmax)

    return network

if __name__ == '__main__':
    if len(sys.argv) < 4:
        print('Usage: python3 path/to/data_dir path/to/output_dir n_epochs')
        exit()
    exp = Experiment(data=sys.argv[1], directory=sys.argv[2], network=network)
    n = 10 if len(sys.argv) == 3 else int(sys.argv[3])
    exp.train(epochs=n)
    exp.test()
    exp.save()

Ejemplo n.º 4

        nonlinearity=lasagne.nonlinearities.leaky_rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.Conv2DLayer(
        network,
        num_filters=8,
        filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.leaky_rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.DenseLayer(
        lasagne.layers.dropout(network, p=.5),
        num_units=2,
        nonlinearity=lasagne.nonlinearities.softmax)

    return network


if __name__ == '__main__':
    if len(sys.argv) < 3:
        print('Usage: python3 path/to/data_dir path/to/experiment_dir')
        exit()
    exp = Experiment(data=sys.argv[1], directory=sys.argv[2], network=network)
    if len(sys.argv) < 4:
        exp.load_parameters()
    else:
        exp.load_parameters(sys.argv[3])
    exp.test()