def test_sgd_no_mon():
# tests that we can run the sgd algorithm
# wihout a monitoring dataset
# does not test for correctness at all, just
# that the algorithm runs without dying
dim = 3
m = 10
rng = np.random.RandomState([25, 9, 2012])
X = rng.randn(m, dim)
idx = rng.randint(0, dim, (m, ))
Y = np.zeros((m, dim))
for i in xrange(m):
Y[i, idx[i]] = 1
dataset = DenseDesignMatrix(X=X, y=Y)
m = 15
X = rng.randn(m, dim)
idx = rng.randint(0, dim, (m, ))
Y = np.zeros((m, dim))
for i in xrange(m):
Y[i, idx[i]] = 1
model = SoftmaxModel(dim)
learning_rate = 1e-3
batch_size = 5
cost = CrossEntropy()
# We need to include this so the test actually stops running at some point
termination_criterion = EpochCounter(5)
algorithm = SGD(learning_rate,
cost,
batch_size=5,
monitoring_dataset=None,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
train = Train(dataset,
model,
algorithm,
save_path=None,
save_freq=0,
extensions=None)
train.main_loop()
def test_sgd_topo():
# tests that we can run the sgd algorithm
# on data with topology
# does not test for correctness at all, just
# that the algorithm runs without dying
rows = 3
cols = 4
channels = 2
dim = rows * cols * channels
m = 10
rng = np.random.RandomState([25, 9, 2012])
dataset = get_topological_dataset(rng, rows, cols, channels, m)
# including a monitoring datasets lets us test that
# the monitor works with supervised data
m = 15
monitoring_dataset = get_topological_dataset(rng, rows, cols, channels, m)
model = TopoSoftmaxModel(rows, cols, channels)
learning_rate = 1e-3
batch_size = 5
cost = CrossEntropy()
# We need to include this so the test actually stops running at some point
termination_criterion = EpochCounter(5)
algorithm = SGD(learning_rate,
cost,
batch_size=5,
monitoring_batches=3,
monitoring_dataset=monitoring_dataset,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
train = Train(dataset,
model,
algorithm,
save_path=None,
save_freq=0,
extensions=None)
train.main_loop()
def test_reject_mon_batch_without_mon():
# tests that setting up the sgd algorithm
# without a monitoring dataset
# but with monitoring_batches specified is an error
dim = 3
m = 10
rng = np.random.RandomState([25, 9, 2012])
X = rng.randn(m, dim)
idx = rng.randint(0, dim, (m, ))
Y = np.zeros((m, dim))
for i in xrange(m):
Y[i, idx[i]] = 1
dataset = DenseDesignMatrix(X=X, y=Y)
m = 15
X = rng.randn(m, dim)
idx = rng.randint(0, dim, (m, ))
Y = np.zeros((m, dim))
for i in xrange(m):
Y[i, idx[i]] = 1
model = SoftmaxModel(dim)
learning_rate = 1e-3
batch_size = 5
cost = CrossEntropy()
try:
algorithm = SGD(learning_rate,
cost,
batch_size=5,
monitoring_batches=3,
monitoring_dataset=None,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
except ValueError:
return
assert False
def test_sgd_topo():
# tests that we can run the sgd algorithm
# on data with topology
# does not test for correctness at all, just
# that the algorithm runs without dying
rows = 3
cols = 4
channels = 2
dim = rows * cols * channels
m = 10
rng = np.random.RandomState([25, 9, 2012])
X = rng.randn(m, rows, cols, channels)
idx = rng.randint(0, dim, (m, ))
Y = np.zeros((m, dim))
for i in xrange(m):
Y[i, idx[i]] = 1
dataset = DenseDesignMatrix(topo_view=X, y=Y)
m = 15
X = rng.randn(m, rows, cols, channels)
idx = rng.randint(0, dim, (m, ))
Y = np.zeros((m, dim))
for i in xrange(m):
Y[i, idx[i]] = 1
# including a monitoring datasets lets us test that
# the monitor works with supervised data
monitoring_dataset = DenseDesignMatrix(topo_view=X, y=Y)
model = TopoSoftmaxModel(rows, cols, channels)
learning_rate = 1e-3
batch_size = 5
cost = CrossEntropy()
# We need to include this so the test actually stops running at some point
termination_criterion = EpochCounter(5)
algorithm = SGD(learning_rate,
cost,
batch_size=5,
monitoring_batches=3,
monitoring_dataset=monitoring_dataset,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
train = Train(dataset,
model,
algorithm,
save_path=None,
save_freq=0,
callbacks=None)
train.main_loop()