Searched refs:x_r (Results 1 – 5 of 5) sorted by relevance
/external/tensorflow/tensorflow/python/distribute/ |
D | distribute_lib_test.py | 327 x_r = dist.reduce(reduce_util.ReduceOp.MEAN, x, axis=None) 328 self.assertEqual(self.evaluate(x), self.evaluate(x_r)) 335 x_r = dist.reduce(op, x, axis=None) 336 self.assertEqual(self.evaluate(x), self.evaluate(x_r)) 337 x_r = dist.extended.reduce_to(op, x, "/CPU:0") 338 self.assertEqual(self.evaluate(x), self.evaluate(x_r)) 339 x_r, y_r = dist.extended.batch_reduce_to(op, 341 self.assertEqual(self.evaluate(x), self.evaluate(x_r)) 356 x_r = dist.extended.reduce_to(reduce_util.ReduceOp.MEAN, x, "/CPU:0") 357 self.assertEqual(self.evaluate(x), self.evaluate(x_r)) [all …]
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/external/webrtc/modules/audio_processing/agc2/ |
D | compute_interpolated_gain_curve.cc | 53 auto area_under_linear_piece = [](double x_l, double x_r, double m, in ComputeAreaUnderPiecewiseLinearApproximation() 55 return x_r * (m * x_r / 2.0 + q) - x_l * (m * x_l / 2.0 + q); in ComputeAreaUnderPiecewiseLinearApproximation()
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/external/libxaac/decoder/ |
D | ixheaacd_hbe_trans.c | 295 FLOAT32 x_r, x_i, temp; in ixheaacd_norm_qmf_in_buf_4() local 297 x_r = in_buf[0]; in ixheaacd_norm_qmf_in_buf_4() 300 temp = x_r * x_r; in ixheaacd_norm_qmf_in_buf_4() 310 x_r *= mag_scaling_fac; in ixheaacd_norm_qmf_in_buf_4() 313 norm_buf[0] = x_r; in ixheaacd_norm_qmf_in_buf_4() 334 FLOAT32 x_r, x_i, temp; in ixheaacd_norm_qmf_in_buf_2() local 336 x_r = in_buf[0]; in ixheaacd_norm_qmf_in_buf_2() 339 temp = x_r * x_r; in ixheaacd_norm_qmf_in_buf_2() 347 x_r *= mag_scaling_fac; in ixheaacd_norm_qmf_in_buf_2() 350 norm_buf[0] = x_r; in ixheaacd_norm_qmf_in_buf_2()
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/external/tensorflow/tensorflow/python/keras/layers/ |
D | recurrent.py | 1854 x_r = K.dot(inputs_r, self.kernel[:, self.units:self.units * 2]) 1859 x_r = K.bias_add(x_r, input_bias[self.units: self.units * 2]) 1880 r = self.recurrent_activation(x_r + recurrent_r) 1903 x_z, x_r, x_h = array_ops.split(matrix_x, 3, axis=-1) 1918 r = self.recurrent_activation(x_r + recurrent_r)
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D | recurrent_v2.py | 597 x_z, x_r, x_h = array_ops.split(matrix_x, 3, axis=1) 606 r = nn.sigmoid(x_r + recurrent_r)
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