def test_HorizontalKernel(self):
nn = MLPR(layers=[
C("Rectifier", channels=7, kernel_shape=(16,1)),
L("Linear", units=5)])
a_in = numpy.zeros((8,16,16,1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [256, 16 * 7, 5])
def test_SquareKernelFull(self):
nn = MLPR(layers=[
C("Rectifier", channels=4, kernel_shape=(3,3), border_mode='full'),
L("Linear", units=5)])
a_in = numpy.zeros((8,32,32,1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [1024, 32 * 32 * 4, 5])
def test_VerticalKernel(self):
nn = MLPR(layers=[
C("Rectifier", channels=4, kernel_shape=(1,16)),
L("Linear", units=7)])
a_in = numpy.zeros((8,16,16,1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [256, 16 * 4, 7])
def test_SquareKernelPool(self):
nn = MLPR(layers=[
C("Rectifier", channels=4, kernel_shape=(3,3), pool_shape=(2,2)),
L("Linear", units=5)])
a_in = numpy.zeros((8,32,32,1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [1024, 15 * 15 * 4, 5])
def test_SquareKernelFull(self):
nn = MLPR(layers=[
C("ExpLin", channels=4, kernel_shape=(3, 3), border_mode='full'),
L("Linear", units=5)
])
a_in = numpy.zeros((8, 32, 32, 1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [1024, 4624, 5])
def test_SquareKernelPool(self):
# TODO: After creation the outputs don't seem to correspond; pooling enabled?
nn = MLPR(layers=[
C("Rectifier", channels=4, kernel_shape=(3,3), pool_shape=(2,2)),
L("Linear", units=5)])
a_in = numpy.zeros((8,32,32,1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [1024, 15 * 15 * 4, 5])
def test_SquareKernelPool(self):
nn = MLPR(layers=[
C("Rectifier", channels=4, kernel_shape=(3, 3), pool_shape=(2, 2)),
L("Linear", units=5)
])
a_in = numpy.zeros((8, 32, 32, 1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [1024, 15 * 15 * 4, 5])
def test_VerticalKernel(self):
nn = MLPR(layers=[
C("Rectifier", channels=4, kernel_shape=(1, 16)),
L("Linear", units=7)
])
a_in = numpy.zeros((8, 16, 16, 1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [256, 16 * 4, 7])
def test_HorizontalKernel(self):
nn = MLPR(layers=[
C("Rectifier", channels=7, kernel_shape=(16, 1)),
L("Linear", units=5)
])
a_in = numpy.zeros((8, 16, 16, 1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [256, 16 * 7, 5])
def test_Upscaling(self):
nn = MLPR(
layers=[
C("Rectifier", channels=4, kernel_shape=(1, 1), scale_factor=(2, 2), border_mode="same"),
L("Linear", units=5),
]
)
a_in = numpy.zeros((8, 32, 32, 1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [1024, 64 * 64 * 4, 5])
def test_SmallSquareKernel(self):
nn = MLPR(layers=[
C("Rectifier",
channels=4,
kernel_shape=(3, 3),
border_mode='valid'),
L("Linear", units=5)
])
a_in = numpy.zeros((8, 32, 32, 1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [1024, 30 * 30 * 4, 5])
def test_Upscaling(self):
nn = MLPR(layers=[
C("Rectifier",
channels=4,
kernel_shape=(1, 1),
scale_factor=(2, 2),
border_mode='same'),
L("Linear", units=5)
])
a_in = numpy.zeros((8, 32, 32, 1))
nn._create_specs(a_in)
assert_equal(nn.unit_counts, [1024, 64 * 64 * 4, 5])
