LostTech.TensorFlow : API Documentation

Type GradientTape

Namespace tensorflow

Parent PythonObjectContainer

Interfaces IGradientTape, IContextManager<T>

Record operations for automatic differentiation.

Operations are recorded if they are executed within this context manager and at least one of their inputs is being "watched".

Trainable variables (created by tf.Variable or `tf.compat.v1.get_variable`, where `trainable=True` is default in both cases) are automatically watched. Tensors can be manually watched by invoking the `watch` method on this context manager.

For example, consider the function `y = x * x`. The gradient at `x = 3.0` can be computed as: GradientTapes can be nested to compute higher-order derivatives. For example, By default, the resources held by a GradientTape are released as soon as GradientTape.gradient() method is called. To compute multiple gradients over the same computation, create a persistent gradient tape. This allows multiple calls to the gradient() method as resources are released when the tape object is garbage collected. By default GradientTape will automatically watch any trainable variables that are accessed inside the context. If you want fine grained control over which variables are watched you can disable automatic tracking by passing `watch_accessed_variables=False` to the tape constructor: Note that when using models you should ensure that your variables exist when using `watch_accessed_variables=False`. Otherwise it's quite easy to make your first iteration not have any gradients: Note that only tensors with real or complex dtypes are differentiable.
Show Example 
x = tf.constant(3.0)
            with tf.GradientTape() as g:
              g.watch(x)
              y = x * x
            dy_dx = g.gradient(y, x) # Will compute to 6.0

Methods

Properties

Public instance methods

Tensor batch_jacobian(IGraphNodeBase target, IGraphNodeBase source, ImplicitContainer<T> unconnected_gradients, Nullable<int> parallel_iterations, bool experimental_use_pfor)

Computes and stacks per-example jacobians.

See [wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant) for the definition of a Jacobian. This function is essentially an efficient implementation of the following:

`tf.stack([self.jacobian(y[i], x[i]) for i in range(x.shape[0])])`.

Note that compared to `GradientTape.jacobian` which computes gradient of each output value w.r.t each input value, this function is useful when `target[i,...]` is independent of `source[j,...]` for `j != i`. This assumption allows more efficient computation as compared to `GradientTape.jacobian`. The output, as well as intermediate activations, are lower dimensional and avoid a bunch of redundant zeros which would result in the jacobian computation given the independence assumption.

Example usage:
Parameters
IGraphNodeBase target
A tensor with rank 2 or higher and with shape [b, y1,..., y_n]. `target[i,...]` should only depend on `source[i,...]`.
IGraphNodeBase source
A tensor with rank 2 or higher and with shape [b, x1,..., x_m].
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Nullable<int> parallel_iterations
A knob to control how many iterations are dispatched in parallel. This knob can be used to control the total memory usage.
bool experimental_use_pfor
If true, uses pfor for computing the Jacobian. Else uses a tf.while_loop.
Returns
Tensor
A tensor `t` with shape [b, y_1,..., y_n, x1,..., x_m] where `t[i,...]` is the jacobian of `target[i,...]` w.r.t. `source[i,...]`, i.e. stacked per-example jacobians.
Show Example 
with tf.GradientTape() as g:
              x = tf.constant([[1., 2.], [3., 4.]], dtype=tf.float32)
              g.watch(x)
              y = x * x
            batch_jacobian = g.batch_jacobian(y, x)
            # batch_jacobian is [[[2,  0], [0,  4]], [[6,  0], [0,  8]]]

object batch_jacobian_dyn(object target, object source, ImplicitContainer<T> unconnected_gradients, object parallel_iterations, ImplicitContainer<T> experimental_use_pfor)

Computes and stacks per-example jacobians.

See [wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant) for the definition of a Jacobian. This function is essentially an efficient implementation of the following:

`tf.stack([self.jacobian(y[i], x[i]) for i in range(x.shape[0])])`.

Note that compared to `GradientTape.jacobian` which computes gradient of each output value w.r.t each input value, this function is useful when `target[i,...]` is independent of `source[j,...]` for `j != i`. This assumption allows more efficient computation as compared to `GradientTape.jacobian`. The output, as well as intermediate activations, are lower dimensional and avoid a bunch of redundant zeros which would result in the jacobian computation given the independence assumption.

Example usage:
Parameters
object target
A tensor with rank 2 or higher and with shape [b, y1,..., y_n]. `target[i,...]` should only depend on `source[i,...]`.
object source
A tensor with rank 2 or higher and with shape [b, x1,..., x_m].
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
object parallel_iterations
A knob to control how many iterations are dispatched in parallel. This knob can be used to control the total memory usage.
ImplicitContainer<T> experimental_use_pfor
If true, uses pfor for computing the Jacobian. Else uses a tf.while_loop.
Returns
object
A tensor `t` with shape [b, y_1,..., y_n, x1,..., x_m] where `t[i,...]` is the jacobian of `target[i,...]` w.r.t. `source[i,...]`, i.e. stacked per-example jacobians.
Show Example 
with tf.GradientTape() as g:
              x = tf.constant([[1., 2.], [3., 4.]], dtype=tf.float32)
              g.watch(x)
              y = x * x
            batch_jacobian = g.batch_jacobian(y, x)
            # batch_jacobian is [[[2,  0], [0,  4]], [[6,  0], [0,  8]]]

object gradient(object target, object sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
object target
a list or nested structure of Tensors or Variables to be differentiated.
object sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient(object target, PythonFunctionContainer sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
object target
a list or nested structure of Tensors or Variables to be differentiated.
PythonFunctionContainer sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient(object target, IEnumerable<object> sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
object target
a list or nested structure of Tensors or Variables to be differentiated.
IEnumerable<object> sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient(PythonFunctionContainer target, object sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
PythonFunctionContainer target
a list or nested structure of Tensors or Variables to be differentiated.
object sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient(PythonFunctionContainer target, IEnumerable<object> sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
PythonFunctionContainer target
a list or nested structure of Tensors or Variables to be differentiated.
IEnumerable<object> sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient(IEnumerable<object> target, IEnumerable<object> sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
IEnumerable<object> target
a list or nested structure of Tensors or Variables to be differentiated.
IEnumerable<object> sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient(PythonFunctionContainer target, PythonFunctionContainer sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
PythonFunctionContainer target
a list or nested structure of Tensors or Variables to be differentiated.
PythonFunctionContainer sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient(IEnumerable<IGraphNodeBase> target, object sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
IEnumerable<IGraphNodeBase> target
a list or nested structure of Tensors or Variables to be differentiated.
object sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient(IEnumerable<IGraphNodeBase> target, PythonFunctionContainer sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
IEnumerable<IGraphNodeBase> target
a list or nested structure of Tensors or Variables to be differentiated.
PythonFunctionContainer sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object gradient_dyn(object target, object sources, object output_gradients, ImplicitContainer<T> unconnected_gradients)

Computes the gradient using operations recorded in context of this tape.
Parameters
object target
a list or nested structure of Tensors or Variables to be differentiated.
object sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
object output_gradients
a list of gradients, one for each element of target. Defaults to None.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Returns
object
a list or nested structure of Tensors (or IndexedSlices, or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`.

object jacobian(IGraphNodeBase target, IEnumerable<IGraphNodeBase> sources, ImplicitContainer<T> unconnected_gradients, Nullable<int> parallel_iterations, bool experimental_use_pfor)

Computes the jacobian using operations recorded in context of this tape.

See [wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant) for the definition of a Jacobian.

Example usage:
Parameters
IGraphNodeBase target
Tensor to be differentiated.
IEnumerable<IGraphNodeBase> sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Nullable<int> parallel_iterations
A knob to control how many iterations are dispatched in parallel. This knob can be used to control the total memory usage.
bool experimental_use_pfor
If true, vectorizes the jacobian computation. Else falls back to a sequential while_loop. Vectorization can sometimes fail or lead to excessive memory usage. This option can be used to disable vectorization in such cases.
Returns
object
A list or nested structure of Tensors (or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`. Note if any gradient is sparse (IndexedSlices), jacobian function currently makes it dense and returns a Tensor instead. This may change in the future.
Show Example 
with tf.GradientTape() as g:
              x  = tf.constant([1.0, 2.0])
              g.watch(x)
              y = x * x
            jacobian = g.jacobian(y, x)
            # jacobian value is [[2., 0.], [0., 4.]]

object jacobian(IGraphNodeBase target, IGraphNodeBase sources, ImplicitContainer<T> unconnected_gradients, Nullable<int> parallel_iterations, bool experimental_use_pfor)

Computes the jacobian using operations recorded in context of this tape.

See [wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant) for the definition of a Jacobian.

Example usage:
Parameters
IGraphNodeBase target
Tensor to be differentiated.
IGraphNodeBase sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
Nullable<int> parallel_iterations
A knob to control how many iterations are dispatched in parallel. This knob can be used to control the total memory usage.
bool experimental_use_pfor
If true, vectorizes the jacobian computation. Else falls back to a sequential while_loop. Vectorization can sometimes fail or lead to excessive memory usage. This option can be used to disable vectorization in such cases.
Returns
object
A list or nested structure of Tensors (or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`. Note if any gradient is sparse (IndexedSlices), jacobian function currently makes it dense and returns a Tensor instead. This may change in the future.
Show Example 
with tf.GradientTape() as g:
              x  = tf.constant([1.0, 2.0])
              g.watch(x)
              y = x * x
            jacobian = g.jacobian(y, x)
            # jacobian value is [[2., 0.], [0., 4.]]

object jacobian_dyn(object target, object sources, ImplicitContainer<T> unconnected_gradients, object parallel_iterations, ImplicitContainer<T> experimental_use_pfor)

Computes the jacobian using operations recorded in context of this tape.

See [wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant) for the definition of a Jacobian.

Example usage:
Parameters
object target
Tensor to be differentiated.
object sources
a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`.
ImplicitContainer<T> unconnected_gradients
a value which can either hold 'none' or 'zero' and alters the value which will be returned if the target and sources are unconnected. The possible values and effects are detailed in 'UnconnectedGradients' and it defaults to 'none'.
object parallel_iterations
A knob to control how many iterations are dispatched in parallel. This knob can be used to control the total memory usage.
ImplicitContainer<T> experimental_use_pfor
If true, vectorizes the jacobian computation. Else falls back to a sequential while_loop. Vectorization can sometimes fail or lead to excessive memory usage. This option can be used to disable vectorization in such cases.
Returns
object
A list or nested structure of Tensors (or None), one for each element in `sources`. Returned structure is the same as the structure of `sources`. Note if any gradient is sparse (IndexedSlices), jacobian function currently makes it dense and returns a Tensor instead. This may change in the future.
Show Example 
with tf.GradientTape() as g:
              x  = tf.constant([1.0, 2.0])
              g.watch(x)
              y = x * x
            jacobian = g.jacobian(y, x)
            # jacobian value is [[2., 0.], [0., 4.]]

void reset()

Resets the timer.

object reset_dyn()

Resets the timer.

IContextManager<T> stop_recording()

Temporarily stops recording operations on this tape.

Operations executed while this context manager is active will not be recorded on the tape. This is useful for reducing the memory used by tracing all computations.

For example:

``` with tf.GradientTape(persistent=True) as t: loss = compute_loss(model) with t.stop_recording(): # The gradient computation below is not traced, saving memory. grads = t.gradient(loss, model.variables) ```

object stop_recording_dyn()

Temporarily stops recording operations on this tape.

Operations executed while this context manager is active will not be recorded on the tape. This is useful for reducing the memory used by tracing all computations.

For example:

``` with tf.GradientTape(persistent=True) as t: loss = compute_loss(model) with t.stop_recording(): # The gradient computation below is not traced, saving memory. grads = t.gradient(loss, model.variables) ```

void watch(IEnumerable<object> tensor)

Ensures that `tensor` is being traced by this tape.
Parameters
IEnumerable<object> tensor
a Tensor or list of Tensors.

void watch(PythonFunctionContainer tensor)

Ensures that `tensor` is being traced by this tape.
Parameters
PythonFunctionContainer tensor
a Tensor or list of Tensors.

object watch_dyn(object tensor)

Ensures that `tensor` is being traced by this tape.
Parameters
object tensor
a Tensor or list of Tensors.

object watched_variables()

Returns variables watched by this tape in order of construction.

object watched_variables_dyn()

Returns variables watched by this tape in order of construction.

Public properties

object PythonObject get;