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)
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)
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)
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)
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)