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,
num,
inner=TANH_INNER,
outer=TANH_OUTER,
**kwargs):
"""
Construct a MultiConvolutionalFeatureMap.
All parameters are as in ConvolutionalFeatureMap; num is the
number of planes this MCFM has (and the length of the list
that fprop, bprop and grad expect for the "inputs" argument).
"""
# Filter size times the number of filters, plus a bias
filter_elems = np.prod(fsize)
nparams = filter_elems * num + 1
outsize = ConvolutionalPlane.outsize_from_imsize_and_fsize(
imsize, fsize)
super(MultiConvolutionalFeatureMap, self).__init__(outsize,
True,
nparams=nparams,
**kwargs)
self.planes = []
assert num > 0
for index in xrange(num):
param_range = slice(1 + (filter_elems * index),
1 + (filter_elems * (index + 1)))
thisparam = self.params[param_range]
thisgrad = self._grad[param_range]
thisplane = ConvolutionalPlane(fsize,
imsize,
params=thisparam,
grad=thisgrad,
bias=False)
self.planes.append(thisplane)
self._out_array = np.empty(outsize)
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_convolutional_plane_params_gradient_no_bias():
module = ConvolutionalPlane((5, 5), (20, 20), bias=False)
module.initialize()
inputs = random.normal(size=(20, 20))
params = random.normal(size=len(module.params))
check_parameter_gradient(module, inputs, params)
