Update generated Python Op docs.

Change: 141357337

tensorflow/g3doc/api_docs/python/array_ops.md

@@ -728,8 +728,6 @@ tf.strided_slice(input, [1, 1, 0], [2, -1, 3], [1, -1, 1]) ==>[[[4, 4, 4],
 
 DEPRECATED: use split_v; split_v rename to split happening soon.
 
-Splits a tensor into `num_split` tensors along one dimension.
-
 Splits `value` along dimension `axis` into `num_or_size_splits` smaller
 tensors. Requires that `num_or_size_splits` evenly divide `value.shape[axis]`.
 
@@ -769,7 +767,7 @@ tf.unpack(t, axis=axis)
 
 ##### Returns:
 
-  `num_split` `Tensor` objects resulting from splitting `value`.
+  `num_or_size_splits` `Tensor` objects resulting from splitting `value`.
 
 
 - - -
@@ -778,13 +776,13 @@ tf.unpack(t, axis=axis)
 
 Splits a tensor into sub tensors.
 
-If num_or_size_splits is a scalar, `num_split`, then
+If `num_or_size_splits` is a scalar, `num_split`, then splits `value` along
-splits `value` along dimension `axis` into `num_split` smaller tensors.
+dimension `axis` into `num_split` smaller tensors.
-Requires that `num_split` evenly divide `value.shape[split_dim]`.
+Requires that `num_split` evenly divides `value.shape[axis]`.
 
-If num_or_size_splits is a tensor, then
+If `num_or_size_splits` is a tensor, `size_splits`, then splits `value` into
-splits `value` into len(size_splits) pieces each the same size as the input
+`len(size_splits)` pieces. The shape of the `i`-th piece has the same size as
-except along dimension split_dim where the size is size_splits[i].
+the `value` except along dimension `axis` where the size is `size_splits[i]`.
 
 For example:
 
@@ -807,12 +805,12 @@ tf.shape(split0) ==> [5, 10]
 *  <b>`num_or_size_splits`</b>: Either an integer indicating the number of splits along
     split_dim or a 1-D Tensor containing the sizes of each output tensor
     along split_dim. If an integer then it must evenly divide
-    value.shape[split_dim]; otherwise the sum of sizes along the split
+    `value.shape[axis]`; otherwise the sum of sizes along the split
-    dimension must match that of the input.
+    dimension must match that of the `value`.
 *  <b>`axis`</b>: A 0-D `int32` `Tensor`. The dimension along which to split.
     Must be in the range `[0, rank(value))`. Defaults to 0.
 *  <b>`num`</b>: Optional, used to specify the number of outputs when it cannot be
-       inferred from the shape of size_splits.
+    inferred from the shape of `size_splits`.
 *  <b>`name`</b>: A name for the operation (optional).
 
 ##### Returns:

tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.linalg.LinearOperatorDiag.md

@@ -1,6 +1,6 @@
 `LinearOperator` acting like a [batch] square diagonal matrix.
 
-This operator acts like a [batch] matrix `A` with shape
+This operator acts like a [batch] diagonal matrix `A` with shape
 `[B1,...,Bb, N, N]` for some `b >= 0`.  The first `b` indices index a
 batch member.  For every batch index `(i1,...,ib)`, `A[i1,...,ib, : :]` is
 an `N x N` matrix.  This matrix `A` is not materialized, but for
@@ -39,7 +39,7 @@ x = operator.solve(y)
 ==> operator.apply(x) = y
 ```
 
-### Shape compatibility
+#### Shape compatibility
 
 This operator acts on [batch] matrix with compatible shape.
 `x` is a batch matrix with compatible shape for `apply` and `solve` if
@@ -50,22 +50,22 @@ x.shape =   [C1,...,Cc] + [N, R],
 and [C1,...,Cc] broadcasts with [B1,...,Bb] to [D1,...,Dd]
 ```
 
-### Performance
+#### Performance
 
 Suppose `operator` is a `LinearOperatorDiag` of shape `[N, N]`,
 and `x.shape = [N, R]`.  Then
 
-* `operator.apply(x)` involves `N*R` multiplications.
+* `operator.apply(x)` involves `N * R` multiplications.
-* `operator.solve(x)` involves `N` divisions and `N*R` multiplications.
+* `operator.solve(x)` involves `N` divisions and `N * R` multiplications.
 * `operator.determinant()` involves a size `N` `reduce_prod`.
 
 If instead `operator` and `x` have shape `[B1,...,Bb, N, N]` and
 `[B1,...,Bb, N, R]`, every operation increases in complexity by `B1*...*Bb`.
 
-### Matrix property hints
+#### Matrix property hints
 
 This `LinearOperator` is initialized with boolean flags of the form `is_X`,
-for `X = non_singular, self_adjoint` etc...
+for `X = non_singular, self_adjoint, positive_definite`.
 These have the following meaning
 * If `is_X == True`, callers should expect the operator to have the
   property `X`.  This is a promise that should be fulfilled, but is *not* a
@@ -234,8 +234,7 @@ If this operator acts like the batch matrix `A` with
 
 ##### Returns:
 
-  Python integer if vector space dimension can be determined statically,
+  `Dimension` object.
-    otherwise `None`.
 
 
 - - -
@@ -328,8 +327,7 @@ If this operator acts like the batch matrix `A` with
 
 ##### Returns:
 
-  Python integer if vector space dimension can be determined statically,
+  `Dimension` object.
-    otherwise `None`.
 
 
 - - -

tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.linalg.LinearOperatorComposition.md

@@ -0,0 +1,486 @@
+Composes one or more `LinearOperators`.
+
+This operator composes one or more linear operators `[op1,...,opJ]`,
+building a new `LinearOperator` with action defined by:
+
+```
+op_composed(x) := op1(op2(...(opJ(x)...))
+```
+
+If `opj` acts like [batch] matrix `Aj`, then `op_composed` acts like the
+[batch] matrix formed with the multiplication `A1 A2...AJ`.
+
+If `opj` has shape `batch_shape_j + [M_j, N_j]`, then we must have
+`N_j = M_{j+1}`, in which case the composed operator has shape equal to
+`broadcast_batch_shape + [M_1, N_J]`, where `broadcast_batch_shape` is the
+mutual broadcast of `batch_shape_j`, `j = 1,...,J`, assuming the intermediate
+batch shapes broadcast.  Even if the composed shape is well defined, the
+composed operator's methods may fail due to lack of broadcasting ability in
+the defining operators' methods.
+
+```python
+# Create a 2 x 2 linear operator composed of two 2 x 2 operators.
+operator_1 = LinearOperatorMatrix([[1., 2.], [3., 4.]])
+operator_2 = LinearOperatorMatrix([[1., 0.], [0., 1.]])
+operator = LinearOperatorComposition([operator_1, operator_2])
+
+operator.to_dense()
+==> [[1., 2.]
+     [3., 4.]]
+
+operator.shape
+==> [2, 2]
+
+operator.log_determinant()
+==> scalar Tensor
+
+x = ... Shape [2, 4] Tensor
+operator.apply(x)
+==> Shape [2, 4] Tensor
+
+# Create a [2, 3] batch of 4 x 5 linear operators.
+matrix_45 = tf.random_normal(shape=[2, 3, 4, 5])
+operator_45 = LinearOperatorMatrix(matrix)
+
+# Create a [2, 3] batch of 5 x 6 linear operators.
+matrix_56 = tf.random_normal(shape=[2, 3, 5, 6])
+operator_56 = LinearOperatorMatrix(matrix_56)
+
+# Compose to create a [2, 3] batch of 4 x 6 operators.
+opeartor_46 = LinearOperatorComposition([operator_45, operator_56])
+
+# Create a shape [2, 3, 6, 2] vector.
+x = tf.random_normal(shape=[2, 3, 6, 2])
+operator.apply(x)
+==> Shape [2, 3, 4, 2] Tensor
+```
+
+#### Performance
+
+The performance of `LinearOperatorComposition` on any operation is equal to
+the sum of the individual operators' operations.
+
+
+#### Matrix property hints
+
+This `LinearOperator` is initialized with boolean flags of the form `is_X`,
+for `X = non_singular, self_adjoint, positive_definite`.
+These have the following meaning
+* If `is_X == True`, callers should expect the operator to have the
+  property `X`.  This is a promise that should be fulfilled, but is *not* a
+  runtime assert.  For example, finite floating point precision may result
+  in these promises being violated.
+* If `is_X == False`, callers should expect the operator to not have `X`.
+* If `is_X == None` (the default), callers should have no expectation either
+  way.
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.__init__(operators, is_non_singular=None, is_self_adjoint=None, is_positive_definite=None, name=None)` {#LinearOperatorComposition.__init__}
+
+Initialize a `LinearOperatorComposition`.
+
+`LinearOperatorComposition` is initialized with a list of operators
+`[op_1,...,op_J]`.  For the `apply` method to be well defined, the
+composition `op_i.apply(op_{i+1}(x))` must be defined.  Other methods have
+similar constraints.
+
+##### Args:
+
+
+*  <b>`operators`</b>: Iterable of `LinearOperator` objects, each with
+    the same `dtype` and composible shape.
+*  <b>`is_non_singular`</b>: Expect that this operator is non-singular.
+*  <b>`is_self_adjoint`</b>: Expect that this operator is equal to its hermitian
+    transpose.
+*  <b>`is_positive_definite`</b>: Expect that this operator is positive definite,
+    meaning the real part of all eigenvalues is positive.  We do not require
+    the operator to be self-adjoint to be positive-definite.  See:
+*  <b>`https`</b>: //en.wikipedia.org/wiki/Positive-definite_matrix
+        #Extension_for_non_symmetric_matrices
+*  <b>`name`</b>: A name for this `LinearOperator`.  Default is the individual
+    operators names joined with `_o_`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If all operators do not have the same `dtype`.
+*  <b>`ValueError`</b>: If `operators` is empty.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.add_to_tensor(x, name='add_to_tensor')` {#LinearOperatorComposition.add_to_tensor}
+
+Add matrix represented by this operator to `x`.  Equivalent to `A + x`.
+
+##### Args:
+
+
+*  <b>`x`</b>: `Tensor` with same `dtype` and shape broadcastable to `self.shape`.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  A `Tensor` with broadcast shape and same `dtype` as `self`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.apply(x, adjoint=False, name='apply')` {#LinearOperatorComposition.apply}
+
+Transform `x` with left multiplication:  `x --> Ax`.
+
+##### Args:
+
+
+*  <b>`x`</b>: `Tensor` with compatible shape and same `dtype` as `self`.
+    See class docstring for definition of compatibility.
+*  <b>`adjoint`</b>: Python `bool`.  If `True`, left multiply by the adjoint.
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  A `Tensor` with shape `[..., M, R]` and same `dtype` as `self`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.assert_non_singular(name='assert_non_singular')` {#LinearOperatorComposition.assert_non_singular}
+
+Returns an `Op` that asserts this operator is non singular.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.assert_positive_definite(name='assert_positive_definite')` {#LinearOperatorComposition.assert_positive_definite}
+
+Returns an `Op` that asserts this operator is positive definite.
+
+Here, positive definite means the real part of all eigenvalues is positive.
+We do not require the operator to be self-adjoint.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  An `Op` that asserts this operator is positive definite.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.assert_self_adjoint(name='assert_self_adjoint')` {#LinearOperatorComposition.assert_self_adjoint}
+
+Returns an `Op` that asserts this operator is self-adjoint.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.batch_shape` {#LinearOperatorComposition.batch_shape}
+
+`TensorShape` of batch dimensions of this `LinearOperator`.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns
+`TensorShape([B1,...,Bb])`, equivalent to `A.get_shape()[:-2]`
+
+##### Returns:
+
+  `TensorShape`, statically determined, may be undefined.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.batch_shape_dynamic(name='batch_shape_dynamic')` {#LinearOperatorComposition.batch_shape_dynamic}
+
+Shape of batch dimensions of this operator, determined at runtime.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns a `Tensor` holding
+`[B1,...,Bb]`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `int32` `Tensor`
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.determinant(name='det')` {#LinearOperatorComposition.determinant}
+
+Determinant for every batch member.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `Tensor` with shape `self.batch_shape` and same `dtype` as `self`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.domain_dimension` {#LinearOperatorComposition.domain_dimension}
+
+Dimension (in the sense of vector spaces) of the domain of this operator.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `N`.
+
+##### Returns:
+
+  `Dimension` object.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.domain_dimension_dynamic(name='domain_dimension_dynamic')` {#LinearOperatorComposition.domain_dimension_dynamic}
+
+Dimension (in the sense of vector spaces) of the domain of this operator.
+
+Determined at runtime.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `N`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op`.
+
+##### Returns:
+
+  `int32` `Tensor`
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.dtype` {#LinearOperatorComposition.dtype}
+
+The `DType` of `Tensor`s handled by this `LinearOperator`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.graph_parents` {#LinearOperatorComposition.graph_parents}
+
+List of graph dependencies of this `LinearOperator`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.is_non_singular` {#LinearOperatorComposition.is_non_singular}
+
+
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.is_positive_definite` {#LinearOperatorComposition.is_positive_definite}
+
+
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.is_self_adjoint` {#LinearOperatorComposition.is_self_adjoint}
+
+
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.log_abs_determinant(name='log_abs_det')` {#LinearOperatorComposition.log_abs_determinant}
+
+Log absolute value of determinant for every batch member.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `Tensor` with shape `self.batch_shape` and same `dtype` as `self`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.name` {#LinearOperatorComposition.name}
+
+Name prepended to all ops created by this `LinearOperator`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.operators` {#LinearOperatorComposition.operators}
+
+
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.range_dimension` {#LinearOperatorComposition.range_dimension}
+
+Dimension (in the sense of vector spaces) of the range of this operator.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `M`.
+
+##### Returns:
+
+  `Dimension` object.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.range_dimension_dynamic(name='range_dimension_dynamic')` {#LinearOperatorComposition.range_dimension_dynamic}
+
+Dimension (in the sense of vector spaces) of the range of this operator.
+
+Determined at runtime.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `M`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op`.
+
+##### Returns:
+
+  `int32` `Tensor`
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.shape` {#LinearOperatorComposition.shape}
+
+`TensorShape` of this `LinearOperator`.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns
+`TensorShape([B1,...,Bb, M, N])`, equivalent to `A.get_shape()`.
+
+##### Returns:
+
+  `TensorShape`, statically determined, may be undefined.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.shape_dynamic(name='shape_dynamic')` {#LinearOperatorComposition.shape_dynamic}
+
+Shape of this `LinearOperator`, determined at runtime.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns a `Tensor` holding
+`[B1,...,Bb, M, N]`, equivalent to `tf.shape(A)`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `int32` `Tensor`
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.solve(rhs, adjoint=False, name='solve')` {#LinearOperatorComposition.solve}
+
+Solve `R` (batch) systems of equations exactly: `A X = rhs`.
+
+Examples:
+
+```python
+# Create an operator acting like a 10 x 2 x 2 matrix.
+operator = LinearOperator(...)
+operator.shape # = 10 x 2 x 2
+
+# Solve one linear system (R = 1) for every member of the length 10 batch.
+RHS = ... # shape 10 x 2 x 1
+X = operator.solve(RHS)  # shape 10 x 2 x 1
+
+# Solve five linear systems (R = 5) for every member of the length 10 batch.
+RHS = ... # shape 10 x 2 x 5
+X = operator.solve(RHS)
+X[3, :, 2]  # Solution to the linear system A[3, :, :] X = RHS[3, :, 2]
+```
+
+##### Args:
+
+
+*  <b>`rhs`</b>: `Tensor` with same `dtype` as this operator and compatible shape.
+    See class docstring for definition of compatibility.
+*  <b>`adjoint`</b>: Python `bool`.  If `True`, solve the system involving the adjoint
+    of this `LinearOperator`.
+*  <b>`name`</b>: A name scope to use for ops added by this method.
+
+##### Returns:
+
+  `Tensor` with shape `[...,N, R]` and same `dtype` as `rhs`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If self.is_non_singular is False.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.tensor_rank` {#LinearOperatorComposition.tensor_rank}
+
+Rank (in the sense of tensors) of matrix corresponding to this operator.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `b + 2`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  Python integer, or None if the tensor rank is undefined.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.tensor_rank_dynamic(name='tensor_rank_dynamic')` {#LinearOperatorComposition.tensor_rank_dynamic}
+
+Rank (in the sense of tensors) of matrix corresponding to this operator.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `b + 2`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `int32` `Tensor`, determined at runtime.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorComposition.to_dense(name='to_dense')` {#LinearOperatorComposition.to_dense}
+
+Return a dense (batch) matrix representing this operator.
+
+

tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.split_v.md

@@ -2,13 +2,13 @@
 
 Splits a tensor into sub tensors.
 
-If num_or_size_splits is a scalar, `num_split`, then
+If `num_or_size_splits` is a scalar, `num_split`, then splits `value` along
-splits `value` along dimension `axis` into `num_split` smaller tensors.
+dimension `axis` into `num_split` smaller tensors.
-Requires that `num_split` evenly divide `value.shape[split_dim]`.
+Requires that `num_split` evenly divides `value.shape[axis]`.
 
-If num_or_size_splits is a tensor, then
+If `num_or_size_splits` is a tensor, `size_splits`, then splits `value` into
-splits `value` into len(size_splits) pieces each the same size as the input
+`len(size_splits)` pieces. The shape of the `i`-th piece has the same size as
-except along dimension split_dim where the size is size_splits[i].
+the `value` except along dimension `axis` where the size is `size_splits[i]`.
 
 For example:
 
@@ -31,12 +31,12 @@ tf.shape(split0) ==> [5, 10]
 *  <b>`num_or_size_splits`</b>: Either an integer indicating the number of splits along
     split_dim or a 1-D Tensor containing the sizes of each output tensor
     along split_dim. If an integer then it must evenly divide
-    value.shape[split_dim]; otherwise the sum of sizes along the split
+    `value.shape[axis]`; otherwise the sum of sizes along the split
-    dimension must match that of the input.
+    dimension must match that of the `value`.
 *  <b>`axis`</b>: A 0-D `int32` `Tensor`. The dimension along which to split.
     Must be in the range `[0, rank(value))`. Defaults to 0.
 *  <b>`num`</b>: Optional, used to specify the number of outputs when it cannot be
-       inferred from the shape of size_splits.
+    inferred from the shape of `size_splits`.
 *  <b>`name`</b>: A name for the operation (optional).
 
 ##### Returns:

tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.split.md

@@ -2,8 +2,6 @@
 
 DEPRECATED: use split_v; split_v rename to split happening soon.
 
-Splits a tensor into `num_split` tensors along one dimension.
-
 Splits `value` along dimension `axis` into `num_or_size_splits` smaller
 tensors. Requires that `num_or_size_splits` evenly divide `value.shape[axis]`.
 
@@ -43,5 +41,5 @@ tf.unpack(t, axis=axis)
 
 ##### Returns:
 
-  `num_split` `Tensor` objects resulting from splitting `value`.
+  `num_or_size_splits` `Tensor` objects resulting from splitting `value`.
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.linalg.LinearOperatorMatrix.md

@@ -0,0 +1,469 @@
+`LinearOperator` that wraps a [batch] matrix.
+
+This operator wraps a [batch] matrix `A` (which is a `Tensor`) with shape
+`[B1,...,Bb, M, N]` for some `b >= 0`.  The first `b` indices index a
+batch member.  For every batch index `(i1,...,ib)`, `A[i1,...,ib, : :]` is
+an `M x N` matrix.
+
+```python
+# Create a 2 x 2 linear operator.
+matrix = [[1., 2.], [3., 4.]]
+operator = LinearOperatorMatrix(matrix)
+
+operator.to_dense()
+==> [[1., 2.]
+     [3., 4.]]
+
+operator.shape
+==> [2, 2]
+
+operator.log_determinant()
+==> scalar Tensor
+
+x = ... Shape [2, 4] Tensor
+operator.apply(x)
+==> Shape [2, 4] Tensor
+
+# Create a [2, 3] batch of 4 x 4 linear operators.
+matrix = tf.random_normal(shape=[2, 3, 4, 4])
+operator = LinearOperatorMatrix(matrix)
+```
+
+#### Shape compatibility
+
+This operator acts on [batch] matrix with compatible shape.
+`x` is a batch matrix with compatible shape for `apply` and `solve` if
+
+```
+operator.shape = [B1,...,Bb] + [M, N],  with b >= 0
+x.shape =        [B1,...,Bb] + [N, R],  with R >= 0.
+```
+
+#### Performance
+
+`LinearOperatorMatrix` has exactly the same performance as would be achieved
+by using standard `TensorFlow` matrix ops.  Intelligent choices are made
+based on the following initialization hints.
+
+* If `dtype` is real, and `is_self_adjoint` and `is_positive_definite`, a
+  Cholesky factorization is used for the determinant and solve.
+
+In all cases, suppose `operator` is a `LinearOperatorMatrix` of shape
+`[M, N]`, and `x.shape = [N, R]`.  Then
+
+* `operator.apply(x)` is `O(M * N * R)`.
+* If `M=N`, `operator.solve(x)` is `O(N^3 * R)`.
+* If `M=N`, `operator.determinant()` is `O(N^3)`.
+
+If instead `operator` and `x` have shape `[B1,...,Bb, M, N]` and
+`[B1,...,Bb, N, R]`, every operation increases in complexity by `B1*...*Bb`.
+
+#### Matrix property hints
+
+This `LinearOperator` is initialized with boolean flags of the form `is_X`,
+for `X = non_singular, self_adjoint, positive_definite`.
+These have the following meaning
+* If `is_X == True`, callers should expect the operator to have the
+  property `X`.  This is a promise that should be fulfilled, but is *not* a
+  runtime assert.  For example, finite floating point precision may result
+  in these promises being violated.
+* If `is_X == False`, callers should expect the operator to not have `X`.
+* If `is_X == None` (the default), callers should have no expectation either
+  way.
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.__init__(matrix, is_non_singular=None, is_self_adjoint=None, is_positive_definite=None, name='LinearOperatorMatrix')` {#LinearOperatorMatrix.__init__}
+
+Initialize a `LinearOperatorMatrix`.
+
+##### Args:
+
+
+*  <b>`matrix`</b>: Shape `[B1,...,Bb, M, N]` with `b >= 0`, `M, N >= 0`.
+    Allowed dtypes: `float32`, `float64`, `complex64`, `complex128`.
+*  <b>`is_non_singular`</b>: Expect that this operator is non-singular.
+*  <b>`is_self_adjoint`</b>: Expect that this operator is equal to its hermitian
+    transpose.
+*  <b>`is_positive_definite`</b>: Expect that this operator is positive definite,
+    meaning the real part of all eigenvalues is positive.  We do not require
+    the operator to be self-adjoint to be positive-definite.  See:
+*  <b>`https`</b>: //en.wikipedia.org/wiki/Positive-definite_matrix
+        #Extension_for_non_symmetric_matrices
+*  <b>`name`</b>: A name for this `LinearOperator`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `diag.dtype` is not an allowed type.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.add_to_tensor(x, name='add_to_tensor')` {#LinearOperatorMatrix.add_to_tensor}
+
+Add matrix represented by this operator to `x`.  Equivalent to `A + x`.
+
+##### Args:
+
+
+*  <b>`x`</b>: `Tensor` with same `dtype` and shape broadcastable to `self.shape`.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  A `Tensor` with broadcast shape and same `dtype` as `self`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.apply(x, adjoint=False, name='apply')` {#LinearOperatorMatrix.apply}
+
+Transform `x` with left multiplication:  `x --> Ax`.
+
+##### Args:
+
+
+*  <b>`x`</b>: `Tensor` with compatible shape and same `dtype` as `self`.
+    See class docstring for definition of compatibility.
+*  <b>`adjoint`</b>: Python `bool`.  If `True`, left multiply by the adjoint.
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  A `Tensor` with shape `[..., M, R]` and same `dtype` as `self`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.assert_non_singular(name='assert_non_singular')` {#LinearOperatorMatrix.assert_non_singular}
+
+Returns an `Op` that asserts this operator is non singular.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.assert_positive_definite(name='assert_positive_definite')` {#LinearOperatorMatrix.assert_positive_definite}
+
+Returns an `Op` that asserts this operator is positive definite.
+
+Here, positive definite means the real part of all eigenvalues is positive.
+We do not require the operator to be self-adjoint.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  An `Op` that asserts this operator is positive definite.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.assert_self_adjoint(name='assert_self_adjoint')` {#LinearOperatorMatrix.assert_self_adjoint}
+
+Returns an `Op` that asserts this operator is self-adjoint.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.batch_shape` {#LinearOperatorMatrix.batch_shape}
+
+`TensorShape` of batch dimensions of this `LinearOperator`.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns
+`TensorShape([B1,...,Bb])`, equivalent to `A.get_shape()[:-2]`
+
+##### Returns:
+
+  `TensorShape`, statically determined, may be undefined.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.batch_shape_dynamic(name='batch_shape_dynamic')` {#LinearOperatorMatrix.batch_shape_dynamic}
+
+Shape of batch dimensions of this operator, determined at runtime.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns a `Tensor` holding
+`[B1,...,Bb]`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `int32` `Tensor`
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.determinant(name='det')` {#LinearOperatorMatrix.determinant}
+
+Determinant for every batch member.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `Tensor` with shape `self.batch_shape` and same `dtype` as `self`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.domain_dimension` {#LinearOperatorMatrix.domain_dimension}
+
+Dimension (in the sense of vector spaces) of the domain of this operator.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `N`.
+
+##### Returns:
+
+  `Dimension` object.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.domain_dimension_dynamic(name='domain_dimension_dynamic')` {#LinearOperatorMatrix.domain_dimension_dynamic}
+
+Dimension (in the sense of vector spaces) of the domain of this operator.
+
+Determined at runtime.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `N`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op`.
+
+##### Returns:
+
+  `int32` `Tensor`
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.dtype` {#LinearOperatorMatrix.dtype}
+
+The `DType` of `Tensor`s handled by this `LinearOperator`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.graph_parents` {#LinearOperatorMatrix.graph_parents}
+
+List of graph dependencies of this `LinearOperator`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.is_non_singular` {#LinearOperatorMatrix.is_non_singular}
+
+
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.is_positive_definite` {#LinearOperatorMatrix.is_positive_definite}
+
+
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.is_self_adjoint` {#LinearOperatorMatrix.is_self_adjoint}
+
+
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.log_abs_determinant(name='log_abs_det')` {#LinearOperatorMatrix.log_abs_determinant}
+
+Log absolute value of determinant for every batch member.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `Tensor` with shape `self.batch_shape` and same `dtype` as `self`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.name` {#LinearOperatorMatrix.name}
+
+Name prepended to all ops created by this `LinearOperator`.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.range_dimension` {#LinearOperatorMatrix.range_dimension}
+
+Dimension (in the sense of vector spaces) of the range of this operator.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `M`.
+
+##### Returns:
+
+  `Dimension` object.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.range_dimension_dynamic(name='range_dimension_dynamic')` {#LinearOperatorMatrix.range_dimension_dynamic}
+
+Dimension (in the sense of vector spaces) of the range of this operator.
+
+Determined at runtime.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `M`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op`.
+
+##### Returns:
+
+  `int32` `Tensor`
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.shape` {#LinearOperatorMatrix.shape}
+
+`TensorShape` of this `LinearOperator`.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns
+`TensorShape([B1,...,Bb, M, N])`, equivalent to `A.get_shape()`.
+
+##### Returns:
+
+  `TensorShape`, statically determined, may be undefined.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.shape_dynamic(name='shape_dynamic')` {#LinearOperatorMatrix.shape_dynamic}
+
+Shape of this `LinearOperator`, determined at runtime.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns a `Tensor` holding
+`[B1,...,Bb, M, N]`, equivalent to `tf.shape(A)`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `int32` `Tensor`
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.solve(rhs, adjoint=False, name='solve')` {#LinearOperatorMatrix.solve}
+
+Solve `R` (batch) systems of equations exactly: `A X = rhs`.
+
+Examples:
+
+```python
+# Create an operator acting like a 10 x 2 x 2 matrix.
+operator = LinearOperator(...)
+operator.shape # = 10 x 2 x 2
+
+# Solve one linear system (R = 1) for every member of the length 10 batch.
+RHS = ... # shape 10 x 2 x 1
+X = operator.solve(RHS)  # shape 10 x 2 x 1
+
+# Solve five linear systems (R = 5) for every member of the length 10 batch.
+RHS = ... # shape 10 x 2 x 5
+X = operator.solve(RHS)
+X[3, :, 2]  # Solution to the linear system A[3, :, :] X = RHS[3, :, 2]
+```
+
+##### Args:
+
+
+*  <b>`rhs`</b>: `Tensor` with same `dtype` as this operator and compatible shape.
+    See class docstring for definition of compatibility.
+*  <b>`adjoint`</b>: Python `bool`.  If `True`, solve the system involving the adjoint
+    of this `LinearOperator`.
+*  <b>`name`</b>: A name scope to use for ops added by this method.
+
+##### Returns:
+
+  `Tensor` with shape `[...,N, R]` and same `dtype` as `rhs`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If self.is_non_singular is False.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.tensor_rank` {#LinearOperatorMatrix.tensor_rank}
+
+Rank (in the sense of tensors) of matrix corresponding to this operator.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `b + 2`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  Python integer, or None if the tensor rank is undefined.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.tensor_rank_dynamic(name='tensor_rank_dynamic')` {#LinearOperatorMatrix.tensor_rank_dynamic}
+
+Rank (in the sense of tensors) of matrix corresponding to this operator.
+
+If this operator acts like the batch matrix `A` with
+`A.shape = [B1,...,Bb, M, N]`, then this returns `b + 2`.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for this `Op.
+
+##### Returns:
+
+  `int32` `Tensor`, determined at runtime.
+
+
+- - -
+
+#### `tf.contrib.linalg.LinearOperatorMatrix.to_dense(name='to_dense')` {#LinearOperatorMatrix.to_dense}
+
+Return a dense (batch) matrix representing this operator.
+
+

tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.linalg.LinearOperatorTriL.md

@@ -1,6 +1,6 @@
 `LinearOperator` acting like a [batch] square lower triangular matrix.
 
-This operator acts like a [batch] matrix `A` with shape
+This operator acts like a [batch] lower triangular matrix `A` with shape
 `[B1,...,Bb, N, N]` for some `b >= 0`.  The first `b` indices index a
 batch member.  For every batch index `(i1,...,ib)`, `A[i1,...,ib, : :]` is
 an `N x N` matrix.
@@ -31,16 +31,9 @@ operator.apply(x)
 # Create a [2, 3] batch of 4 x 4 linear operators.
 tril = tf.random_normal(shape=[2, 3, 4, 4])
 operator = LinearOperatorTriL(tril)
-
-# Create a shape [2, 1, 4, 2] vector.  Note that this shape is compatible
-# since the batch dimensions, [2, 1], are brodcast to
-# operator.batch_shape = [2, 3].
-y = tf.random_normal(shape=[2, 1, 4, 2])
-x = operator.solve(y)
-==> operator.apply(x) = y
 ```
 
-### Shape compatibility
+#### Shape compatibility
 
 This operator acts on [batch] matrix with compatible shape.
 `x` is a batch matrix with compatible shape for `apply` and `solve` if
@@ -50,7 +43,7 @@ operator.shape = [B1,...,Bb] + [N, N],  with b >= 0
 x.shape =        [B1,...,Bb] + [N, R],  with R >= 0.
 ```
 
-### Performance
+#### Performance
 
 Suppose `operator` is a `LinearOperatorTriL` of shape `[N, N]`,
 and `x.shape = [N, R]`.  Then
@@ -62,10 +55,10 @@ and `x.shape = [N, R]`.  Then
 If instead `operator` and `x` have shape `[B1,...,Bb, N, N]` and
 `[B1,...,Bb, N, R]`, every operation increases in complexity by `B1*...*Bb`.
 
-### Matrix property hints
+#### Matrix property hints
 
 This `LinearOperator` is initialized with boolean flags of the form `is_X`,
-for `X = non_singular, self_adjoint` etc...
+for `X = non_singular, self_adjoint, positive_definite`.
 These have the following meaning
 * If `is_X == True`, callers should expect the operator to have the
   property `X`.  This is a promise that should be fulfilled, but is *not* a
@@ -237,8 +230,7 @@ If this operator acts like the batch matrix `A` with
 
 ##### Returns:
 
-  Python integer if vector space dimension can be determined statically,
+  `Dimension` object.
-    otherwise `None`.
 
 
 - - -
@@ -331,8 +323,7 @@ If this operator acts like the batch matrix `A` with
 
 ##### Returns:
 
-  Python integer if vector space dimension can be determined statically,
+  `Dimension` object.
-    otherwise `None`.
 
 
 - - -

tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.linalg.LinearOperator.md

@@ -7,7 +7,7 @@ Subclasses of `LinearOperator` provide a access to common methods on a
 * Operators that take advantage of special structure, while providing a
   consistent API to users.
 
-### Subclassing
+#### Subclassing
 
 To enable a public method, subclasses should implement the leading-underscore
 version of the method.  The argument signature should be identical except for
@@ -15,7 +15,7 @@ the omission of `name="..."`.  For example, to enable
 `apply(x, adjoint=False, name="apply")` a subclass should implement
 `_apply(x, adjoint=False)`.
 
-### Performance contract
+#### Performance contract
 
 Subclasses should implement a method only if it can be done with a reasonable
 performance increase over generic dense operations, either in time, parallel
@@ -27,7 +27,7 @@ Class docstrings should contain an explanation of computational complexity.
 Since this is a high-performance library, attention should be paid to detail,
 and explanations can include constants as well as Big-O notation.
 
-### Shape compatibility
+#### Shape compatibility
 
 `LinearOperator` sub classes should operate on a [batch] matrix with
 compatible shape.  Class docstrings should define what is meant by compatible
@@ -49,7 +49,7 @@ operator.shape = [B1,...,Bb] + [M, N],  b >= 0,
 rhs.shape =   [B1,...,Bb] + [M, R]
 ```
 
-### Example docstring for subclasses.
+#### Example docstring for subclasses.
 
 This operator acts like a (batch) matrix `A` with shape
 `[B1,...,Bb, M, N]` for some `b >= 0`.  The first `b` indices index a
@@ -76,19 +76,19 @@ operator.apply(x)
 ==> Shape [2, 4, 5] Tensor
 ```
 
-### Shape compatibility
+#### Shape compatibility
 
 This operator acts on batch matrices with compatible shape.
 FILL IN WHAT IS MEANT BY COMPATIBLE SHAPE
 
-### Performance
+#### Performance
 
 FILL THIS IN
 
-### Matrix property hints
+#### Matrix property hints
 
 This `LinearOperator` is initialized with boolean flags of the form `is_X`,
-for `X = non_singular, self_adjoint` etc...
+for `X = non_singular, self_adjoint, positive_definite`.
 These have the following meaning
 * If `is_X == True`, callers should expect the operator to have the
   property `X`.  This is a promise that should be fulfilled, but is *not* a
@@ -260,8 +260,7 @@ If this operator acts like the batch matrix `A` with
 
 ##### Returns:
 
-  Python integer if vector space dimension can be determined statically,
+  `Dimension` object.
-    otherwise `None`.
 
 
 - - -
@@ -354,8 +353,7 @@ If this operator acts like the batch matrix `A` with
 
 ##### Returns:
 
-  Python integer if vector space dimension can be determined statically,
+  `Dimension` object.
-    otherwise `None`.
 
 
 - - -

tensorflow/g3doc/api_docs/python/index.md

@@ -1017,7 +1017,9 @@
 
 * **[Linear Algebra (contrib)](../../api_docs/python/contrib.linalg.md)**:
   * [`LinearOperator`](../../api_docs/python/contrib.linalg.md#LinearOperator)
+  * [`LinearOperatorComposition`](../../api_docs/python/contrib.linalg.md#LinearOperatorComposition)
   * [`LinearOperatorDiag`](../../api_docs/python/contrib.linalg.md#LinearOperatorDiag)
+  * [`LinearOperatorMatrix`](../../api_docs/python/contrib.linalg.md#LinearOperatorMatrix)
   * [`LinearOperatorTriL`](../../api_docs/python/contrib.linalg.md#LinearOperatorTriL)
 
 * **[Losses (contrib)](../../api_docs/python/contrib.losses.md)**: