Method _rff_hypothesis_test

caffe2/python/layers_test.py:1680–1693

Runs hypothesis test for Semi Random Features layer. Inputs: rff_output -- output of net after running random fourier features layer X -- input data W -- weight parameter from train_init_net b -- bias parameter

(rff_output, X, W, b, scale)

    def testRandomFourierFeatures(self, batch_size, input_dims, output_dims, bandwidth):

        def _rff_hypothesis_test(rff_output, X, W, b, scale):
            '''
            Runs hypothesis test for Semi Random Features layer.

            Inputs:
                rff_output -- output of net after running random fourier features layer
                X -- input data
                W -- weight parameter from train_init_net
                b -- bias parameter from train_init_net
                scale -- value by which to scale the output vector
            '''
            output = workspace.FetchBlob(rff_output)
            output_ref = scale * np.cos(np.dot(X, np.transpose(W)) + b)
            npt.assert_allclose(output, output_ref, rtol=1e-3, atol=1e-3)

        X = np.random.random((batch_size, input_dims)).astype(np.float32)
        scale = np.sqrt(2.0 / output_dims)