Method testSemiRandomFeatures

caffe2/python/layers_test.py:1873–2041

(self, batch_size, input_dims, output_dims, s, scale,
                               set_weight_as_global_constant, use_struct_input)

        use_struct_input=st.booleans(),
    )
    def testSemiRandomFeatures(self, batch_size, input_dims, output_dims, s, scale,
                               set_weight_as_global_constant, use_struct_input):

        def _semi_random_hypothesis_test(srf_output, X_full, X_random, rand_w,
                                         rand_b, s):
            '''
            Runs hypothesis test for Semi Random Features layer.

            Inputs:
                srf_output -- output of net after running semi random features layer
                X_full -- full input data
                X_random -- random-output input data
                rand_w -- random-initialized weight parameter from train_init_net
                rand_b -- random-initialized bias parameter from train_init_net
                s -- degree parameter

            '''
            # Get output from net
            net_output = workspace.FetchBlob(srf_output)

            # Fetch learned parameter blobs
            learned_w = workspace.FetchBlob(self.model.layers[0].learned_w)
            learned_b = workspace.FetchBlob(self.model.layers[0].learned_b)

            # Computing output directly
            x_rand = np.matmul(X_random, np.transpose(rand_w)) + rand_b
            x_learn = np.matmul(X_full, np.transpose(learned_w)) + learned_b
            x_pow = np.power(x_rand, s)
            if s > 0:
                h_rand_features = np.piecewise(x_rand,
                                               [x_rand <= 0, x_rand > 0],
                                               [0, 1])
            else:
                h_rand_features = np.piecewise(x_rand,
                                               [x_rand <= 0, x_rand > 0],
                                               [0, lambda x: x / (1 + x)])
            output_ref = np.multiply(np.multiply(x_pow, h_rand_features), x_learn)

            # Comparing net output and computed output
            npt.assert_allclose(net_output, output_ref, rtol=1e-3, atol=1e-3)

        X_full = np.random.normal(size=(batch_size, input_dims)).astype(np.float32)
        if use_struct_input:
            X_random = np.random.normal(size=(batch_size, input_dims)).\
                astype(np.float32)
            input_data = [X_full, X_random]
            input_record = self.new_record(schema.Struct(
                ('full', schema.Scalar(
                    (np.float32, (input_dims,))
                )),
                ('random', schema.Scalar(
                    (np.float32, (input_dims,))
                ))
            ))
        else:
            X_random = X_full
            input_data = [X_full]
            input_record = self.new_record(schema.Scalar(