def test_multi_convolutional_feature_map_fprop():
cplane1 = ConvolutionalPlane((5, 5), (20, 20), bias=False)
cplane2 = ConvolutionalPlane((5, 5), (20, 20), bias=False)
sigmoid = TanhSigmoid((16, 16), bias=True)
mfmap = MultiConvolutionalFeatureMap((5, 5), (20, 20), 2)
mfmap.initialize()
cplane1.params[:] = mfmap.planes[0].params
cplane2.params[:] = mfmap.planes[1].params
sigmoid.params[:] = mfmap.params[0:1]
inputs1 = random.normal(size=(20, 20))
inputs2 = random.normal(size=(20, 20))
control = sigmoid.fprop(cplane1.fprop(inputs1) + cplane2.fprop(inputs2))
mfmap_out = mfmap.fprop([inputs1, inputs2])
assert_array_almost_equal(control, mfmap_out)
def __init__(self, fsize, imsize):
"""Construct a feature map with given filter size and image size."""
super(NaiveConvolutionalFeatureMap, self).__init__()
self.convolution = ConvolutionalPlane(fsize, imsize)
self.nonlinearity = TanhSigmoid(self.convolution.outsize)
def test_sigmoid_initialize_raises_if_no_parameters(self):
foo = TanhSigmoid((5, 5))
foo.initialize()
