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_))