Exemple #1
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def test_pylearn2_trainin():
    # Construct the model
    mlp = MLP(activations=[Sigmoid(), Sigmoid()],
              dims=[784, 100, 784],
              weights_init=IsotropicGaussian(),
              biases_init=Constant(0.01))
    mlp.initialize()
    cost = SquaredError()

    block_cost = BlocksCost(cost)
    block_model = BlocksModel(mlp, (VectorSpace(dim=784), 'features'))

    # Load the data
    rng = numpy.random.RandomState(14)
    train_dataset = random_dense_design_matrix(rng, 1024, 784, 10)
    valid_dataset = random_dense_design_matrix(rng, 1024, 784, 10)

    # Silence Pylearn2's logger
    logger = logging.getLogger(pylearn2.__name__)
    logger.setLevel(logging.ERROR)

    # Training algorithm
    sgd = SGD(learning_rate=0.01,
              cost=block_cost,
              batch_size=128,
              monitoring_dataset=valid_dataset)
    train = Train(train_dataset, block_model, algorithm=sgd)
    train.main_loop(time_budget=3)
Exemple #2
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def test_pylearn2_training():
    # Construct the model
    mlp = MLP(activations=[Sigmoid(), Sigmoid()], dims=[784, 100, 784],
              weights_init=IsotropicGaussian(), biases_init=Constant(0.01))
    mlp.initialize()
    cost = SquaredError()

    # Load the data
    rng = numpy.random.RandomState(14)
    train_dataset = random_dense_design_matrix(rng, 1024, 784, 10)
    valid_dataset = random_dense_design_matrix(rng, 1024, 784, 10)

    x = tensor.matrix('features')
    block_cost = Pylearn2Cost(cost.apply(x, mlp.apply(x)))
    block_model = Pylearn2Model(mlp)

    # Silence Pylearn2's logger
    logger = logging.getLogger(pylearn2.__name__)
    logger.setLevel(logging.ERROR)

    # Training algorithm
    sgd = SGD(learning_rate=0.01, cost=block_cost, batch_size=128,
              monitoring_dataset=valid_dataset)
    train = Pylearn2Train(train_dataset, block_model, algorithm=sgd)
    train.main_loop(time_budget=3)
Exemple #3
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def test_zca_dataset():
    """
    Tests the ZCA_Dataset class.
    """
    # Preparation
    rng = np.random.RandomState([2014, 11, 4])
    start = 0
    stop = 990
    num_examples = 1000
    num_feat = 5
    num_classes = 2

    # random_dense_design_matrix has values that are centered and of
    # unit stdev, which is not useful to test the ZCA.
    # So, we replace its value by an uncentered uniform one.
    raw = random_dense_design_matrix(rng, num_examples, num_feat, num_classes)
    x = rng.uniform(low=-0.5, high=2.0, size=(num_examples, num_feat))
    x = x.astype(np.float32)
    raw.X = x

    zca = ZCA(filter_bias=0.0)
    zca.apply(raw, can_fit=True)
    zca_dataset = ZCA_Dataset(raw, zca, start, stop)

    # Testing general behaviour
    mean = zca_dataset.X.mean(axis=0)
    var = zca_dataset.X.std(axis=0)
    assert_allclose(mean, np.zeros(num_feat), atol=1e-2)
    assert_allclose(var, np.ones(num_feat), atol=1e-2)

    # Testing mapback()
    y = zca_dataset.mapback(zca_dataset.X)
    assert_allclose(x[start:stop], y)

    # Testing mapback_for_viewer()
    y = zca_dataset.mapback_for_viewer(zca_dataset.X)
    z = x/np.abs(x).max(axis=0)
    assert_allclose(z[start:stop], y, rtol=1e-2)

    # Testing adjust_for_viewer()
    y = zca_dataset.adjust_for_viewer(x.T).T
    z = x/np.abs(x).max(axis=0)
    assert_allclose(z, y)

    # Testing adjust_to_be_viewed_with()
    y = zca_dataset.adjust_to_be_viewed_with(x, 2*x, True)
    z = zca_dataset.adjust_for_viewer(x)
    assert_allclose(z/2, y)
    y = zca_dataset.adjust_to_be_viewed_with(x, 2*x, False)
    z = x/np.abs(x).max()
    assert_allclose(z/2, y)

    # Testing has_targets()
    assert zca_dataset.has_targets()
def test_zca_dataset():
    """
    Test that a ZCA dataset can be constructed without crashing. No
    attempt to verify correctness of behavior.
    """

    rng = np.random.RandomState([2014, 11, 4])
    num_examples = 5
    dim = 3
    num_classes = 2
    raw = random_dense_design_matrix(rng, num_examples, dim, num_classes)
    zca = ZCA()
    zca.apply(raw, can_fit=True)
    zca_dataset = ZCA_Dataset(raw, zca, start=1, stop=4)
Exemple #5
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def test_hdf5_convert_to_one_hot():
    """Train using an HDF5 dataset with one-hot target conversion."""
    skip_if_no_h5py()
    import h5py

    # save random data to HDF5
    handle, filename = tempfile.mkstemp()
    dataset = random_dense_design_matrix(np.random.RandomState(1),
                                         num_examples=10, dim=5, num_classes=3)
    with h5py.File(filename, 'w') as f:
        f.create_dataset('X', data=dataset.get_design_matrix())
        f.create_dataset('y', data=dataset.get_targets())

    # instantiate Train object
    trainer = yaml_parse.load(convert_to_one_hot_yaml % {'filename': filename})
    trainer.main_loop()

    # cleanup
    os.remove(filename)
Exemple #6
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def test_hdf5_convert_to_one_hot():
    """Train using an HDF5 dataset with one-hot target conversion."""
    skip_if_no_h5py()
    import h5py

    # save random data to HDF5
    handle, filename = tempfile.mkstemp()
    dataset = random_dense_design_matrix(np.random.RandomState(1),
                                         num_examples=10,
                                         dim=5,
                                         num_classes=3)
    with h5py.File(filename, 'w') as f:
        f.create_dataset('X', data=dataset.get_design_matrix())
        f.create_dataset('y', data=dataset.get_targets())

    # instantiate Train object
    trainer = yaml_parse.load(convert_to_one_hot_yaml % {'filename': filename})
    trainer.main_loop()

    # cleanup
    os.remove(filename)