def test_convolutional_layer():
x = tensor.tensor4('x')
num_channels = 4
batch_size = 5
pooling_size = 3
num_filters = 3
filter_size = (3, 3)
activation = Rectifier().apply
conv = ConvolutionalLayer(activation, filter_size, num_filters,
(pooling_size, pooling_size),
num_channels, image_size=(17, 13),
batch_size=batch_size,
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, 5, 4)) + 5)
assert_equal(conv.convolution.batch_size, batch_size)
assert_equal(conv.pooling.batch_size, batch_size)
def test_convolutional_layer():
x = tensor.tensor4('x')
num_channels = 4
batch_size = 5
pooling_size = 3
num_filters = 3
filter_size = (3, 3)
activation = Rectifier().apply
conv = ConvolutionalLayer(activation,
filter_size,
num_filters, (pooling_size, pooling_size),
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, 5, 4)) + 5)
def test_convolutional_layer_use_bias():
act = ConvolutionalLayer(Rectifier().apply, (3, 3),
5, (2, 2),
6,
image_size=(9, 9),
use_bias=False)
act.allocate()
assert not act.convolution.use_bias
assert len(ComputationGraph([act.apply(tensor.tensor4())]).parameters) == 1
def test_convolutional_layer_use_bias():
act = ConvolutionalLayer(Rectifier().apply, (3, 3), 5, (2, 2), 6, image_size=(9, 9), use_bias=False)
act.allocate()
assert not act.convolution.use_bias
assert len(ComputationGraph([act.apply(tensor.tensor4())]).parameters) == 1
