def test_convolutional():
x = tensor.tensor4("x")
num_channels = 4
num_filters = 3
batch_size = 5
filter_size = (3, 3)
conv = Convolutional(
filter_size,
num_filters,
num_channels,
image_size=(17, 13),
weights_init=Constant(1.0),
biases_init=Constant(5.0),
)
conv.initialize()
y = conv.apply(x)
func = function([x], y)
x_val = numpy.ones((batch_size, num_channels, 17, 13), dtype=theano.config.floatX)
assert_allclose(
func(x_val), numpy.prod(filter_size) * num_channels * numpy.ones((batch_size, num_filters, 15, 11)) + 5
)
conv.image_size = (17, 13)
conv.batch_size = 2 # This should have effect on get_dim
assert conv.get_dim("output") == (num_filters, 15, 11)
def conv_layer(self, name, wt, bias, image_size):
"""Creates a Convolutional brick with the given name, weights,
bias, and image_size."""
layer = Convolutional(
name=name,
filter_size=wt.shape[0:2],
num_channels=wt.shape[2], # in
num_filters=wt.shape[3], # out
weights_init=Constant(0), # does not matter
biases_init=Constant(0), # does not matter
tied_biases=True,
border_mode='valid',
)
if image_size:
layer.image_size = image_size
layer.initialize()
weights = self.to_bc01(wt)
layer.parameters[0].set_value(weights.astype("float32")) # W
layer.parameters[1].set_value(bias.squeeze().astype("float32")) # b
return (layer, layer.get_dim("output")[1:3])
def test_convolutional():
x = tensor.tensor4('x')
num_channels = 4
num_filters = 3
batch_size = 5
filter_size = (3, 3)
conv = Convolutional(filter_size,
num_filters,
num_channels,
image_size=(17, 13),
weights_init=Constant(1.),
biases_init=Constant(5.))
conv.initialize()
y = conv.apply(x)
func = function([x], y)
x_val = numpy.ones((batch_size, num_channels, 17, 13),
dtype=theano.config.floatX)
assert_allclose(
func(x_val),
numpy.prod(filter_size) * num_channels * numpy.ones(
(batch_size, num_filters, 15, 11)) + 5)
conv.image_size = (17, 13)
conv.batch_size = 2 # This should have effect on get_dim
assert conv.get_dim('output') == (num_filters, 15, 11)
def conv_layer(self, name, wt, bias, image_size):
"""Creates a Convolutional brick with the given name, weights,
bias, and image_size."""
layer = Convolutional(
name=name,
filter_size=wt.shape[0:2],
num_channels=wt.shape[2], # in
num_filters=wt.shape[3], # out
weights_init=Constant(0), # does not matter
biases_init=Constant(0), # does not matter
tied_biases=True,
border_mode="valid",
)
if image_size:
layer.image_size = image_size
layer.initialize()
weights = self.to_bc01(wt)
layer.parameters[0].set_value(weights.astype("float32")) # W
layer.parameters[1].set_value(bias.squeeze().astype("float32")) # b
return (layer, layer.get_dim("output")[1:3])
