def testMismatchedRanksFails(self): data_1d = self.dtype([0.3, 0.7]) data_2d = self.dtype([[1., .4], [.4, .2]]) data_3d = self.dtype([[[1., .4], [.2, .5]]]) data_1d = make_tensor_hiding_attributes( data_1d, hide_shape=self.use_dynamic_shape) data_2d = make_tensor_hiding_attributes( data_2d, hide_shape=self.use_dynamic_shape) data_3d = make_tensor_hiding_attributes( data_3d, hide_shape=self.use_dynamic_shape) with self.assertRaisesRegexp(Exception, 'similarly batched'): self.evaluate( matvecmul(data_2d, data_3d, validate_args=self.use_dynamic_shape)) with self.assertRaisesRegexp(Exception, 'similarly batched'): self.evaluate( matvecmul(data_2d, data_2d, validate_args=self.use_dynamic_shape)) with self.assertRaisesRegexp(Exception, 'similarly batched'): self.evaluate( matvecmul(data_3d, data_3d, validate_args=self.use_dynamic_shape)) with self.assertRaisesRegexp(Exception, 'similarly batched'): self.evaluate( matvecmul(data_3d, data_1d, validate_args=self.use_dynamic_shape))
def testMismatchedRanksFails(self): data_1d = self.dtype([0.3, 0.7]) data_2d = self.dtype([[1., .4], [.4, .2]]) data_3d = self.dtype([[[1., .4], [.2, .5]]]) data_1d = make_tensor_hiding_attributes(data_1d, hide_shape=self.use_dynamic_shape) data_2d = make_tensor_hiding_attributes(data_2d, hide_shape=self.use_dynamic_shape) data_3d = make_tensor_hiding_attributes(data_3d, hide_shape=self.use_dynamic_shape) with self.assertRaisesRegexp(Exception, 'similarly batched'): self.evaluate(matvecmul(data_2d, data_3d, validate_args=self.use_dynamic_shape)) with self.assertRaisesRegexp(Exception, 'similarly batched'): self.evaluate(matvecmul(data_2d, data_2d, validate_args=self.use_dynamic_shape)) with self.assertRaisesRegexp(Exception, 'similarly batched'): self.evaluate(matvecmul(data_3d, data_3d, validate_args=self.use_dynamic_shape)) with self.assertRaisesRegexp(Exception, 'similarly batched'): self.evaluate(matvecmul(data_3d, data_1d, validate_args=self.use_dynamic_shape))
def calculate_linear_predictor(model_matrix, model_coefficients, offset=None, name=None): """Computes `model_matrix @ model_coefficients + offset`.""" with tf.name_scope(name, 'calculate_linear_predictor', [model_matrix, model_coefficients, offset]): predicted_linear_response = matvecmul(model_matrix, model_coefficients) if offset is not None: predicted_linear_response += offset return predicted_linear_response
def testLiteralsWithMismatchedDtypes(self): a = np.array([[1, 2], [3, 4]], np.float64) b = [1., 1.] expected_result = np.matmul(a, b) result = matvecmul(a, b) result = self.evaluate(result) self.assertAllClose(expected_result, result)
def testLiteralsWithMismatchedDtypes(self): a = np.array([[1, 2], [3, 4]], np.float64) b = [1., 1.] expected_result = np.matmul(a, b) result = matvecmul(a, b) result = self.evaluate(result) self.assertAllClose(expected_result, result)
def testTransposedMultiplicationWorks(self): a = self.dtype([[1., .4, .5], [.4, .2, .25]]) b = self.dtype([0.3, 0.7]) expected_result = np.matmul(np.transpose(a), b) a = make_tensor_hiding_attributes(a, hide_shape=self.use_dynamic_shape) b = make_tensor_hiding_attributes(b, hide_shape=self.use_dynamic_shape) result = matvecmul(a, b, transpose_a=True) result = self.evaluate(result) self.assertAllClose(expected_result, result)
def testTransposedMultiplicationWorks(self): a = self.dtype([[1., .4, .5], [.4, .2, .25]]) b = self.dtype([0.3, 0.7]) expected_result = np.matmul(np.transpose(a), b) a = make_tensor_hiding_attributes(a, hide_shape=self.use_dynamic_shape) b = make_tensor_hiding_attributes(b, hide_shape=self.use_dynamic_shape) result = matvecmul(a, b, transpose_a=True) result = self.evaluate(result) self.assertAllClose(expected_result, result)
def testBatchedMultiplicationWorks(self): a = self.dtype([[[1., .4, .5], [.4, .2, .25]], [[2, .3, .4], [.5, .6, .7]]]) b = self.dtype([[0.3, 0.7, 0.5], [1.0, 2.1, 3.2]]) expected_result = np.stack( [np.matmul(a[0, ...], b[0, ...]), np.matmul(a[1, ...], b[1, ...])]) a = make_tensor_hiding_attributes(a, hide_shape=self.use_dynamic_shape) b = make_tensor_hiding_attributes(b, hide_shape=self.use_dynamic_shape) result = matvecmul(a, b) result = self.evaluate(result) self.assertAllClose(expected_result, result)
def testBatchedMultiplicationWorks(self): a = self.dtype([[[1., .4, .5], [.4, .2, .25]], [[2, .3, .4], [.5, .6, .7]]]) b = self.dtype([[0.3, 0.7, 0.5], [1.0, 2.1, 3.2]]) expected_result = np.stack([np.matmul(a[0, ...], b[0, ...]), np.matmul(a[1, ...], b[1, ...])]) a = make_tensor_hiding_attributes(a, hide_shape=self.use_dynamic_shape) b = make_tensor_hiding_attributes(b, hide_shape=self.use_dynamic_shape) result = matvecmul(a, b) result = self.evaluate(result) self.assertAllClose(expected_result, result)
def _joint_log_prob(model_coefficients_): predicted_linear_response_ = matvecmul(x_, model_coefficients_) return tf.reduce_sum(model.log_prob(y_, predicted_linear_response_))