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()