Surface control_flow_ops.case to public. Update docs. Add unit tests.
Change: 115496194
This commit is contained in:
Eugene Brevdo
TensorFlower Gardener
parent
497606904b
commit
2861cc1d23
tensorflow
g3doc/api_docs/python
array_ops.mdcontrib.layers.mdcontrol_flow_ops.mdframework.mdimage.mdindex.mdmath_ops.mdstate_ops.mdtrain.md
python
@ -1285,6 +1285,120 @@ boolean_mask(tensor, mask) ==> [[1, 2], [5, 6]]
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```
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- - -
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### `tf.one_hot(indices, depth, on_value, off_value, axis=None, name=None)` {#one_hot}
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Returns a one-hot tensor.
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The locations represented by indices in `indices` take value `on_value`,
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while all other locations take value `off_value`.
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If the input `indices` is rank `N`, the output will have rank `N+1`,
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The new axis is created at dimension `axis` (default: the new axis is
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appended at the end).
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If `indices` is a scalar the output shape will be a vector of length `depth`.
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If `indices` is a vector of length `features`, the output shape will be:
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```
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features x depth if axis == -1
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depth x features if axis == 0
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```
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If `indices` is a matrix (batch) with shape `[batch, features]`,
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the output shape will be:
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```
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batch x features x depth if axis == -1
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batch x depth x features if axis == 1
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depth x batch x features if axis == 0
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```
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Examples
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=========
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Suppose that
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```
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indices = [0, 2, -1, 1]
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depth = 3
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on_value = 5.0
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off_value = 0.0
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axis = -1
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```
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Then output is `[4 x 3]`:
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```output =
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[5.0 0.0 0.0] // one_hot(0)
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[0.0 0.0 5.0] // one_hot(2)
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[0.0 0.0 0.0] // one_hot(-1)
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[0.0 5.0 0.0] // one_hot(1)
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```
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Suppose that
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```
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indices = [0, 2, -1, 1]
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depth = 3
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on_value = 0.0
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off_value = 3.0
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axis = 0
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```
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Then output is `[3 x 4]`:
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```output =
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[0.0 3.0 3.0 3.0]
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[3.0 3.0 3.0 0.0]
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[3.0 3.0 3.0 3.0]
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[3.0 0.0 3.0 3.0]
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// ^ one_hot(0)
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// ^ one_hot(2)
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// ^ one_hot(-1)
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// ^ one_hot(1)
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```
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Suppose that
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```
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indices = [[0, 2], [1, -1]]
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depth = 3
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on_value = 1.0
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off_value = 0.0
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axis = -1
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```
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Then output is `[2 x 2 x 3]`:
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```output =
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[
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[1.0, 0.0, 0.0] // one_hot(0)
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[0.0, 0.0, 1.0] // one_hot(2)
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][
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[0.0, 1.0, 0.0] // one_hot(1)
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[0.0, 0.0, 0.0] // one_hot(-1)
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]```
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##### Args:
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* <b>`indices`</b>: A `Tensor` of type `int64`. A tensor of indices.
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* <b>`depth`</b>: A `Tensor` of type `int32`.
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A scalar defining the depth of the one hot dimension.
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* <b>`on_value`</b>: A `Tensor`.
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A scalar defining the value to fill in output when `indices[j] = i`.
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* <b>`off_value`</b>: A `Tensor`. Must have the same type as `on_value`.
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A scalar defining the value to fill in output when `indices[j] != i`.
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* <b>`axis`</b>: An optional `int`. Defaults to `-1`.
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The axis to fill (default: -1, a new inner-most axis).
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* <b>`name`</b>: A name for the operation (optional).
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##### Returns:
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A `Tensor`. Has the same type as `on_value`. The one-hot tensor.
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## Other Functions and Classes
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- - -
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@ -301,7 +301,7 @@ activation.
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- - -
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### `tf.contrib.layers.summarize_tensor(tensor)` {#summarize_tensor}
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### `tf.contrib.layers.summarize_tensor(tensor, tag=None)` {#summarize_tensor}
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Summarize a tensor using a suitable summary type.
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@ -313,6 +313,7 @@ other tensors, `histogram_summary` is used.
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* <b>`tensor`</b>: The tensor to summarize
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* <b>`tag`</b>: The tag to use, if None then use tensor's op's name.
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##### Returns:
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@ -377,3 +378,31 @@ be `dtypes.float32` or `dtypes.float64`. If neither `tensors` nor
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float.
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- - -
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### `tf.contrib.layers.assert_scalar_int(tensor)` {#assert_scalar_int}
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Assert `tensor` is 0-D, of type `tf.int32` or `tf.int64`.
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##### Args:
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* <b>`tensor`</b>: Tensor to test.
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##### Returns:
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`tensor`, for chaining.
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##### Raises:
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* <b>`ValueError`</b>: if `tensor` is not 0-D, of type `tf.int32` or `tf.int64`.
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- - -
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### `tf.contrib.layers.is_numeric_tensor(tensor)` {#is_numeric_tensor}
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@ -182,6 +182,84 @@ the same non-zero number and type of outputs.
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```
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- - -
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### `tf.case(pred_fn_pairs, default, exclusive=False, name='case')` {#case}
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Create a case operation.
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The `pred_fn_pairs` parameter is a dict or list of pairs of size N.
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Each pair contains a boolean scalar tensor and a python callable that
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creates the tensors to be returned if the boolean evaluates to True. `default`
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is a callable generating a list of tensors. All the callables in
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`pred_fn_pairs` as well as `default` should return the same number and types
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of tensors.
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If `exclusive==True`, all predicates are evaluated, and a logging operation
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with an error is returned if more than one of the predicates evaluates to
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True. If `exclusive==False`, execution stops are the first predicate which
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evaluates to True, and the tensors generated by the corresponding function
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are returned immediately. If none of the predicates evaluate to True, this
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operation returns the tensors generated by `default`.
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Example 1:
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Pseudocode:
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```
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if (x < y) return 17;
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else return 23;
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```
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Expressions:
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```
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f1 = lambda: tf.constant(17)
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f2 = lambda: tf.constant(23)
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r = case([(tf.less(x, y), f1)], default=f2)
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```
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Example 2:
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Pseudocode:
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```
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if (x < y && x > z) raise OpError("Only one predicate may evaluate true");
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if (x < y) return 17;
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else if (x > z) return 23;
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else return -1;
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```
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Expressions:
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```
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x = tf.constant(0)
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y = tf.constant(1)
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z = tf.constant(2)
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def f1(): return tf.constant(17)
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def f2(): return tf.constant(23)
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def f3(): return tf.constant(-1)
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r = case({tf.less(x, y): f1, tf.greater(x, z): f2},
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default=f3, exclusive=True)
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```
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##### Args:
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* <b>`pred_fn_pairs`</b>: Dict or list of pairs of a boolean scalar tensor and a
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callable which returns a list of tensors.
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* <b>`default`</b>: A callable that returns a list of tensors.
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* <b>`exclusive`</b>: True iff more than one predicate is allowed to evaluate to True.
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* <b>`name`</b>: A name for this operation (optional).
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##### Returns:
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The tensors returned by the first pair whose predicate evaluated to True, or
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those returned by `default` if none does.
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##### Raises:
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* <b>`TypeError`</b>: If `pred_fn_pairs` is not a list/dictionary.
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* <b>`TypeError`</b>: If `pred_fn_pairs` is a list but does not contain 2-tuples.
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* <b>`TypeError`</b>: If `fns[i]` is not callable for any i, or `default` is not
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callable.
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## Logical Operators
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@ -523,7 +523,7 @@ This method may be called concurrently from multiple threads.
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- - -
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#### `tf.Graph.unique_name(name)` {#Graph.unique_name}
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#### `tf.Graph.unique_name(name, mark_as_used=True)` {#Graph.unique_name}
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Return a unique operation name for `name`.
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@ -537,10 +537,17 @@ Operation names are displayed in error messages reported by the
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TensorFlow runtime, and in various visualization tools such as
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TensorBoard.
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If `mark_as_used` is set to `True`, which is the default, a new
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unique name is created and marked as in use. If it's set to `False`,
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the unique name is returned without actually being marked as used.
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This is useful when the caller simply wants to know what the name
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to be created will be.
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##### Args:
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* <b>`name`</b>: The name for an operation.
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* <b>`mark_as_used`</b>: Whether to mark this name as being used.
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##### Returns:
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@ -398,6 +398,39 @@ dimension.
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- - -
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### `tf.image.central_crop(image, central_fraction)` {#central_crop}
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Crop the central region of the image.
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Remove the outer parts of an image but retain the central region of the image
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along each dimension. If we specify central_fraction = 0.5, this function
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returns the region marked with "X" in the below diagram.
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--------
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| |
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| XXXX |
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| XXXX |
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| | where "X" is the central 50% of the image.
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--------
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##### Args:
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* <b>`image`</b>: 3-D float Tensor of shape [height, width, depth]
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* <b>`central_fraction`</b>: float (0, 1], fraction of size to crop
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##### Raises:
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* <b>`ValueError`</b>: if central_crop_fraction is not within (0, 1].
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##### Returns:
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3-D float Tensor
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- - -
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### `tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width)` {#pad_to_bounding_box}
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@ -92,6 +92,7 @@
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* [`dynamic_stitch`](../../api_docs/python/array_ops.md#dynamic_stitch)
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* [`expand_dims`](../../api_docs/python/array_ops.md#expand_dims)
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* [`gather`](../../api_docs/python/array_ops.md#gather)
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* [`one_hot`](../../api_docs/python/array_ops.md#one_hot)
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* [`pack`](../../api_docs/python/array_ops.md#pack)
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* [`pad`](../../api_docs/python/array_ops.md#pad)
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* [`rank`](../../api_docs/python/array_ops.md#rank)
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@ -193,6 +194,7 @@
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* [`sparse_segment_sum`](../../api_docs/python/math_ops.md#sparse_segment_sum)
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* [`sqrt`](../../api_docs/python/math_ops.md#sqrt)
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* [`square`](../../api_docs/python/math_ops.md#square)
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* [`squared_difference`](../../api_docs/python/math_ops.md#squared_difference)
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* [`sub`](../../api_docs/python/math_ops.md#sub)
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* [`transpose`](../../api_docs/python/math_ops.md#transpose)
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* [`truediv`](../../api_docs/python/math_ops.md#truediv)
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@ -203,6 +205,7 @@
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* **[Control Flow](../../api_docs/python/control_flow_ops.md)**:
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* [`add_check_numerics_ops`](../../api_docs/python/control_flow_ops.md#add_check_numerics_ops)
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* [`Assert`](../../api_docs/python/control_flow_ops.md#Assert)
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* [`case`](../../api_docs/python/control_flow_ops.md#case)
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* [`check_numerics`](../../api_docs/python/control_flow_ops.md#check_numerics)
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* [`cond`](../../api_docs/python/control_flow_ops.md#cond)
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* [`count_up_to`](../../api_docs/python/control_flow_ops.md#count_up_to)
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@ -233,6 +236,7 @@
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* [`adjust_contrast`](../../api_docs/python/image.md#adjust_contrast)
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* [`adjust_hue`](../../api_docs/python/image.md#adjust_hue)
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* [`adjust_saturation`](../../api_docs/python/image.md#adjust_saturation)
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* [`central_crop`](../../api_docs/python/image.md#central_crop)
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* [`convert_image_dtype`](../../api_docs/python/image.md#convert_image_dtype)
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* [`crop_to_bounding_box`](../../api_docs/python/image.md#crop_to_bounding_box)
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* [`decode_jpeg`](../../api_docs/python/image.md#decode_jpeg)
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@ -316,6 +320,7 @@
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* **[Neural Network](../../api_docs/python/nn.md)**:
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* [`avg_pool`](../../api_docs/python/nn.md#avg_pool)
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* [`batch_normalization`](../../api_docs/python/nn.md#batch_normalization)
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* [`bias_add`](../../api_docs/python/nn.md#bias_add)
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* [`compute_accidental_hits`](../../api_docs/python/nn.md#compute_accidental_hits)
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* [`conv2d`](../../api_docs/python/nn.md#conv2d)
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@ -422,8 +427,10 @@
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* **[Layers (contrib)](../../api_docs/python/contrib.layers.md)**:
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* [`assert_same_float_dtype`](../../api_docs/python/contrib.layers.md#assert_same_float_dtype)
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* [`assert_scalar_int`](../../api_docs/python/contrib.layers.md#assert_scalar_int)
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* [`convolution2d`](../../api_docs/python/contrib.layers.md#convolution2d)
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* [`fully_connected`](../../api_docs/python/contrib.layers.md#fully_connected)
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* [`is_numeric_tensor`](../../api_docs/python/contrib.layers.md#is_numeric_tensor)
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* [`l1_regularizer`](../../api_docs/python/contrib.layers.md#l1_regularizer)
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* [`l2_regularizer`](../../api_docs/python/contrib.layers.md#l2_regularizer)
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* [`summarize_activation`](../../api_docs/python/contrib.layers.md#summarize_activation)
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@ -628,6 +628,24 @@ Computes complementary error function of `x` element-wise.
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the return type is `quint8`.
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- - -
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### `tf.squared_difference(x, y, name=None)` {#squared_difference}
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Returns (x - y)(x - y) element-wise.
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##### Args:
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* <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `complex64`, `int64`.
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* <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
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* <b>`name`</b>: A name for the operation (optional).
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##### Returns:
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A `Tensor`. Has the same type as `x`.
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## Matrix Math Functions
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@ -104,7 +104,7 @@ Creating a variable.
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- - -
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#### `tf.Variable.__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, name=None, variable_def=None)` {#Variable.__init__}
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#### `tf.Variable.__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None)` {#Variable.__init__}
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Creates a new variable with value `initial_value`.
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@ -131,6 +131,11 @@ variable to its initial value.
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* <b>`validate_shape`</b>: If `False`, allows the variable to be initialized with a
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value of unknown shape. If `True`, the default, the shape of
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`initial_value` must be known.
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* <b>`caching_device`</b>: Optional device string describing where the Variable
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should be cached for reading. Defaults to the Variable's device.
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If not `None`, caches on another device. Typical use is to cache
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on the device where the Ops using the Variable reside, to deduplicate
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copying through `Switch` and other conditional statements.
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* <b>`name`</b>: Optional name for the variable. Defaults to `'Variable'` and gets
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uniquified automatically.
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* <b>`variable_def`</b>: `VariableDef` protocol buffer. If not `None`, recreates
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@ -389,7 +394,7 @@ The `Operation` of this variable.
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#### `tf.Variable.from_proto(variable_def)` {#Variable.from_proto}
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Returns a `Variable` object created from `variable_def`.
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- - -
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@ -742,7 +747,7 @@ path can be passed directly to a call to `restore()`.
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kept in the same directory as the checkpoint files, is automatically
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managed by the saver to keep track of recent checkpoints. Defaults to
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'checkpoint'.
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* <b>`meta_graph_suffix`</b>: Suffix for MetaGraphDef file. Defaults to 'meta'.
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* <b>`meta_graph_suffix`</b>: Suffix for `MetaGraphDef` file. Defaults to 'meta'.
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##### Returns:
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@ -848,7 +853,7 @@ Writes `MetaGraphDef` to save_path/filename.
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#### `tf.train.Saver.from_proto(saver_def)` {#Saver.from_proto}
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Returns a `Saver` object created from `saver_def`.
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- - -
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@ -873,7 +878,11 @@ Sets the list of old checkpoint filenames and timestamps.
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|
||||
#### `tf.train.Saver.to_proto()` {#Saver.to_proto}
|
||||
|
||||
Returns a `SaverDef` protocol buffer.
|
||||
Converts this `Saver` to a `SaverDef` protocol buffer.
|
||||
|
||||
##### Returns:
|
||||
|
||||
A `SaverDef` protocol buffer.
|
||||
|
||||
|
||||
|
||||
@ -1022,17 +1031,26 @@ Attributes:
|
||||
initializer: default initializer passed to get_variable.
|
||||
regularizer: default regularizer passed to get_variable.
|
||||
reuse: Boolean or None, setting the reuse in get_variable.
|
||||
caching_device: string, callable, or None: the caching device passed to
|
||||
get_variable.
|
||||
name_scope: The name passed to tf.name_scope.
|
||||
- - -
|
||||
|
||||
#### `tf.VariableScope.__init__(reuse, name='', initializer=None, regularizer=None, name_scope='')` {#VariableScope.__init__}
|
||||
#### `tf.VariableScope.__init__(reuse, name='', initializer=None, regularizer=None, caching_device=None, name_scope='')` {#VariableScope.__init__}
|
||||
|
||||
Creates a new VariableScope with the given properties.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.VariableScope.get_variable(var_store, name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None)` {#VariableScope.get_variable}
|
||||
#### `tf.VariableScope.caching_device` {#VariableScope.caching_device}
|
||||
|
||||
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.VariableScope.get_variable(var_store, name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None)` {#VariableScope.get_variable}
|
||||
|
||||
Gets an existing variable with this name or create a new one.
|
||||
|
||||
@ -1072,6 +1090,13 @@ Gets an existing variable with this name or create a new one.
|
||||
Reuse variables in this scope.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.VariableScope.set_caching_device(caching_device)` {#VariableScope.set_caching_device}
|
||||
|
||||
Set caching_device for this scope.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.VariableScope.set_initializer(initializer)` {#VariableScope.set_initializer}
|
||||
@ -1089,7 +1114,7 @@ Set regularizer for this scope.
|
||||
|
||||
- - -
|
||||
|
||||
### `tf.variable_scope(name_or_scope, reuse=None, initializer=None, regularizer=None)` {#variable_scope}
|
||||
### `tf.variable_scope(name_or_scope, reuse=None, initializer=None, regularizer=None, caching_device=None)` {#variable_scope}
|
||||
|
||||
Returns a context for variable scope.
|
||||
|
||||
@ -1157,6 +1182,7 @@ then all its sub-scopes become reusing as well.
|
||||
well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
|
||||
* <b>`initializer`</b>: default initializer for variables within this scope.
|
||||
* <b>`regularizer`</b>: default regularizer for variables within this scope.
|
||||
* <b>`caching_device`</b>: default caching device for variables within this scope.
|
||||
|
||||
##### Returns:
|
||||
|
||||
@ -1172,7 +1198,7 @@ then all its sub-scopes become reusing as well.
|
||||
|
||||
- - -
|
||||
|
||||
### `tf.variable_op_scope(values, name, default_name, initializer=None, regularizer=None)` {#variable_op_scope}
|
||||
### `tf.variable_op_scope(values, name, default_name, initializer=None, regularizer=None, caching_device=None)` {#variable_op_scope}
|
||||
|
||||
Returns a context manager for defining an op that creates variables.
|
||||
|
||||
@ -1208,8 +1234,10 @@ def my_op_with_vars(a, b, name=None):
|
||||
uniquified in the variable scope.
|
||||
* <b>`default_name`</b>: The default name to use if the `name` argument is `None`, this
|
||||
name will be uniquified.
|
||||
* <b>`initializer`</b>: A default initializer to pass to variable scope.
|
||||
* <b>`regularizer`</b>: default regularizer for variables within this scope.
|
||||
* <b>`initializer`</b>: The default initializer to pass to variable scope.
|
||||
* <b>`regularizer`</b>: The default regularizer for variables within this scope.
|
||||
* <b>`caching_device`</b>: The default caching device for variables within this scope.
|
||||
|
||||
|
||||
##### Returns:
|
||||
|
||||
|
||||
@ -410,36 +410,15 @@ current good choice is 1.0 or 0.1.
|
||||
|
||||
Optimizer that implements the FTRL algorithm.
|
||||
|
||||
See this [paper](
|
||||
https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
|
||||
|
||||
- - -
|
||||
|
||||
#### `tf.train.FtrlOptimizer.__init__(learning_rate, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='Ftrl')` {#FtrlOptimizer.__init__}
|
||||
|
||||
Construct a new FTRL optimizer.
|
||||
|
||||
The Ftrl-proximal algorithm, abbreviated for Follow-the-regularized-leader,
|
||||
is described in the paper [Ad Click Prediction: a View from the Trenches](
|
||||
https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
|
||||
|
||||
It can give a good performance vs. sparsity tradeoff.
|
||||
|
||||
Ftrl-proximal uses its own global base learning rate and can behave like
|
||||
Adagrad with `learning_rate_power=-0.5`, or like gradient descent with
|
||||
`learning_rate_power=0.0`.
|
||||
|
||||
The effective learning rate is adjusted per parameter, relative to this
|
||||
base learning rate as:
|
||||
|
||||
```
|
||||
effective_learning_rate_i = (learning_rate /
|
||||
pow(k + summed_squared_gradients_for_i, learning_rate_power));
|
||||
```
|
||||
|
||||
where k is the small constant `initial_accumulator_value`.
|
||||
|
||||
Note that the real regularization coefficient of `|w|^2` for objective
|
||||
function is `1 / lambda_2` if specifying `l2 = lambda_2` as argument when
|
||||
using this function.
|
||||
|
||||
##### Args:
|
||||
|
||||
|
||||
@ -1442,7 +1421,7 @@ depending on whether or not a `Coordinator` was passed to
|
||||
|
||||
#### `tf.train.QueueRunner.from_proto(queue_runner_def)` {#QueueRunner.from_proto}
|
||||
|
||||
|
||||
Returns a `QueueRunner` object created from `queue_runner_def`.
|
||||
|
||||
|
||||
- - -
|
||||
@ -1936,7 +1915,7 @@ global_step: 10
|
||||
##### Args:
|
||||
|
||||
|
||||
* <b>`sess`</b>: A brain `Session` object.
|
||||
* <b>`sess`</b>: A TensorFlow `Session` object.
|
||||
* <b>`global_step_tensor`</b>: `Tensor` or the `name` of the operation that contains
|
||||
the global step.
|
||||
|
||||
@ -2237,9 +2216,12 @@ Generates a checkpoint state proto.
|
||||
|
||||
Recreates a Graph saved in a `MetaGraphDef` proto.
|
||||
|
||||
This function reads from a file containing a `MetaGraphDef` proto,
|
||||
adds all the nodes from the graph_def proto to the current graph,
|
||||
recreates all the collections, and returns a saver from saver_def.
|
||||
This function takes a `MetaGraphDef` protocol buffer as input. If
|
||||
the argument is a file containing a `MetaGraphDef` protocol buffer ,
|
||||
it constructs a protocol buffer from the file content. The function
|
||||
then adds all the nodes from the `graph_def` field to the
|
||||
current graph, recreates all the collections, and returns a saver
|
||||
constructed from the `saver_def` field.
|
||||
|
||||
In combination with `export_meta_graph()`, this function can be used to
|
||||
|
||||
@ -2250,6 +2232,38 @@ In combination with `export_meta_graph()`, this function can be used to
|
||||
|
||||
* Run inference from a saved graph and checkpoints.
|
||||
|
||||
```Python
|
||||
...
|
||||
# Create a saver.
|
||||
saver = tf.train.Saver(...variables...)
|
||||
# Remember the training_op we want to run by adding it to a collection.
|
||||
tf.add_to_collection('train_op', train_op)
|
||||
sess = tf.Session()
|
||||
for step in xrange(1000000):
|
||||
sess.run(train_op)
|
||||
if step % 1000 == 0:
|
||||
# Saves checkpoint, which by default also exports a meta_graph
|
||||
# named 'my-model-global_step.meta'.
|
||||
saver.save(sess, 'my-model', global_step=step)
|
||||
```
|
||||
|
||||
Later we can continue training from this saved `meta_graph` without building
|
||||
the model from scratch.
|
||||
|
||||
```Python
|
||||
with tf.Session() as sess:
|
||||
new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
|
||||
new_saver.restore(sess, 'my-save-dir/my-model-10000')
|
||||
# tf.get_collection() retrurns a list. In this example we only want the
|
||||
# first one.
|
||||
train_op = tf.get_collection('train_op')[0]
|
||||
for step in xrange(1000000):
|
||||
sess.run(train_op)
|
||||
```
|
||||
|
||||
NOTE: Restarting training from saved `meta_graph` only works if the
|
||||
device assignments have not changed.
|
||||
|
||||
##### Args:
|
||||
|
||||
|
||||
|
||||
@ -1232,6 +1232,50 @@ class ControlFlowTest(tf.test.TestCase):
|
||||
self.assertAllClose(4.0, i.eval(feed_dict={d: 1}))
|
||||
self.assertAllClose(2.0 * math.sqrt(2), i.eval(feed_dict={d: 2}))
|
||||
|
||||
def testCase(self):
|
||||
with self.test_session():
|
||||
x = tf.constant(1)
|
||||
y = tf.constant(2)
|
||||
z = tf.constant(3)
|
||||
f1 = lambda: tf.constant(17)
|
||||
f2 = lambda: tf.constant(23)
|
||||
f3 = lambda: tf.constant(-1)
|
||||
|
||||
r1 = tf.case({x < y: f1, x > z: f2}, default=f3, exclusive=True)
|
||||
self.assertAllEqual(r1.eval(), 17)
|
||||
|
||||
r2 = tf.case([(y > z, f1), (y > x, f2)], default=f3)
|
||||
self.assertAllEqual(r2.eval(), 23)
|
||||
|
||||
# Duplicate events can happen, first one is selected
|
||||
r3 = tf.case([(x < y, f1), (x < y, f2)], default=f3)
|
||||
self.assertAllEqual(r3.eval(), 17)
|
||||
|
||||
# Duplicate events cause an error if exclusive = True
|
||||
r4 = tf.case([(x < y, f1), (x < y, f2)], default=f3, exclusive=True)
|
||||
with self.assertRaisesOpError(
|
||||
"More than one condition evaluated as True but exclusive=True."):
|
||||
r4.eval()
|
||||
|
||||
# Check that the default is called if none of the others are
|
||||
r5 = tf.case({x > y: f1}, default=f3)
|
||||
self.assertAllEqual(r5.eval(), -1)
|
||||
|
||||
ran_once = [False, False, False]
|
||||
|
||||
def break_run_twice(ix):
|
||||
def _break():
|
||||
assert not ran_once[ix]
|
||||
ran_once[ix] = True
|
||||
return tf.constant(ix)
|
||||
return _break
|
||||
|
||||
# Should not fail - each conditional gets called exactly once
|
||||
r6 = tf.case([(x < y, break_run_twice(0)), (x > y, break_run_twice(1))],
|
||||
default=break_run_twice(2))
|
||||
|
||||
self.assertAllEqual(r6.eval(), 0)
|
||||
|
||||
def testOneOpCond(self):
|
||||
with self.test_session():
|
||||
v = tf.Variable(0)
|
||||
|
||||
@ -24,6 +24,7 @@ the execution of operations and add conditional dependencies to your graph.
|
||||
@@no_op
|
||||
@@count_up_to
|
||||
@@cond
|
||||
@@case
|
||||
|
||||
## Logical Operators
|
||||
|
||||
@ -82,6 +83,7 @@ from tensorflow.python.ops import constant_op
|
||||
from tensorflow.python.ops import gen_array_ops
|
||||
from tensorflow.python.ops import gen_control_flow_ops
|
||||
from tensorflow.python.ops import gen_data_flow_ops
|
||||
from tensorflow.python.ops import logging_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import tensor_array_ops
|
||||
# pylint: disable=wildcard-import,undefined-variable
|
||||
@ -1974,6 +1976,9 @@ def case(pred_fn_pairs, default, exclusive=False, name="case"):
|
||||
|
||||
Expressions:
|
||||
```
|
||||
x = tf.constant(0)
|
||||
y = tf.constant(1)
|
||||
z = tf.constant(2)
|
||||
def f1(): return tf.constant(17)
|
||||
def f2(): return tf.constant(23)
|
||||
def f3(): return tf.constant(-1)
|
||||
@ -2050,7 +2055,7 @@ def case(pred_fn_pairs, default, exclusive=False, name="case"):
|
||||
# and prev_case_seq will loop from case_sequence[0] to case_sequence[-1]
|
||||
if exclusive:
|
||||
# TODO(ebrevdo): Add Where() for DT_BOOL, replace with Size(Where(preds))
|
||||
preds_c = array_ops.concat(0, preds, name="preds_c")
|
||||
preds_c = array_ops.pack(preds, name="preds_c")
|
||||
num_true_conditions = math_ops.reduce_sum(
|
||||
math_ops.cast(preds_c, dtypes.int32), name="num_true_conds")
|
||||
at_most_one_true_condition = math_ops.less(
|
||||
|
||||
@ -36,6 +36,7 @@ from tensorflow.python.ops.control_flow_ops import group
|
||||
from tensorflow.python.ops.control_flow_ops import no_op
|
||||
from tensorflow.python.ops.control_flow_ops import tuple
|
||||
from tensorflow.python.ops.control_flow_ops import cond
|
||||
from tensorflow.python.ops.control_flow_ops import case
|
||||
from tensorflow.python.ops.data_flow_ops import *
|
||||
from tensorflow.python.ops.gradients import *
|
||||
from tensorflow.python.ops.init_ops import *
|
||||
|
||||
Loading…
Reference in New Issue
Block a user