질문이있다? TensorFlow 포럼 에서 커뮤니티와 연결

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:

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

GradientTapes can be nested to compute higher-order derivatives. For example,

x = tf.constant(3.0)
with tf.GradientTape() as g:
g.watch(x)
with tf.GradientTape() as gg:
gg.watch(x)
y = x * x
dy_dx = gg.gradient(y, x)     # Will compute to 6.0
d2y_dx2 = g.gradient(dy_dx, x)  # Will compute to 2.0

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. For example:

x = tf.constant(3.0)
with tf.GradientTape(persistent=True) as g:
g.watch(x)
y = x * x
z = y * y
dz_dx = g.gradient(z, x)  # 108.0 (4*x^3 at x = 3)
dy_dx = g.gradient(y, x)  # 6.0
del g  # Drop the reference to the tape

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:

with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(variable_a)
y = variable_a ** 2  # Gradients will be available for `variable_a`.
z = variable_b ** 3  # No gradients will be available since `variable_b` is
# not being watched.

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:

a = tf.keras.layers.Dense(32)
b = tf.keras.layers.Dense(32)

with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(a.variables)  # Since `a.build` has not been called at this point
# `a.variables` will return an empty list and the
# tape will not be watching anything.
result = b(a(inputs))
tape.gradient(result, a.variables)  # The result of this computation will be
# a list of `None`s since a's variables
# are not being watched.

Note that only tensors with real or complex dtypes are differentiable.

persistent Boolean controlling whether a persistent gradient tape is created. False by default, which means at most one call can be made to the gradient() method on this object.
watch_accessed_variables Boolean controlling whether the tape will automatically watch any (trainable) variables accessed while the tape is active. Defaults to True meaning gradients can be requested from any result computed in the tape derived from reading a trainable Variable. If False users must explicitly watch any Variables they want to request gradients from.

## Methods

### batch_jacobian

View source

Computes and stacks per-example jacobians.

See wikipedia article 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:

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]]]

Args
target A tensor with rank 2 or higher and with shape [b, y1, ..., y_n]. target[i,...] should only depend on source[i,...].
source A tensor with rank 2 or higher and with shape [b, x1, ..., x_m].
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'.
parallel_iterations A knob to control how many iterations are dispatched in parallel. This knob can be used to control the total memory usage.
experimental_use_pfor If true, uses pfor for computing the Jacobian. Else uses a tf.while_loop.

Returns
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.

Raises
RuntimeError If called on a non-persistent tape with eager execution enabled and without enabling experimental_use_pfor.
ValueError If vectorization of jacobian computation fails or if first dimension of target and source do not match.

View source

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

Args
target a list or nested structure of Tensors or Variables to be differentiated.
sources a list or nested structure of Tensors or Variables. target will be differentiated against elements in sources.
output_gradients a list of gradients, one for each element of target. Defaults to None.
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
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.

Raises
RuntimeError if called inside the context of the tape, or if called more than once on a non-persistent tape.
ValueError if the target is a variable or if unconnected gradients is called with an unknown value.

### jacobian

View source

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

See wikipedia article for the definition of a Jacobian.

#### Example usage:

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.]]

Args
target Tensor to be differentiated.
sources a list or nested structure of Tensors or Variables. target will be differentiated against elements in sources.
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'.
parallel_iterations A knob to control how many iterations are dispatched in parallel. This knob can be used to control the total memory usage.
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
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.

Raises
RuntimeError If called on a non-persistent tape with eager execution enabled and without enabling experimental_use_pfor.
ValueError If vectorization of jacobian computation fails.

### reset

View source

Clears all information stored in this tape.

Equivalent to exiting and reentering the tape context manager with a new tape. For example, the two following code blocks are equivalent:

with tf.GradientTape() as t:
loss = loss_fn()
with tf.GradientTape() as t:
loss += other_loss_fn()
t.gradient(loss, ...)  # Only differentiates other_loss_fn, not loss_fn

# The following is equivalent to the above
with tf.GradientTape() as t:
loss = loss_fn()
t.reset()
loss += other_loss_fn()
t.gradient(loss, ...)  # Only differentiates other_loss_fn, not loss_fn

This is useful if you don't want to exit the context manager for the tape, or can't because the desired reset point is inside a control flow construct:

with tf.GradientTape() as t:
loss = ...
if loss > k:
t.reset()

### stop_recording

View source

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.

#### Yields:

None

Raises
RuntimeError if the tape is not currently recording.

### watch

View source

Ensures that tensor is being traced by this tape.

Args
tensor a Tensor or list of Tensors.

Raises
ValueError if it encounters something that is not a tensor.

### watched_variables

View source

Returns variables watched by this tape in order of construction.

### __enter__

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Enters a context inside which operations are recorded on this tape.

### __exit__

View source

Exits the recording context, no further operations are traced.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"필요한 정보가 없음" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"너무 복잡함/단계 수가 너무 많음" },{ "type": "thumb-down", "id": "outOfDate", "label":"오래됨" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"기타" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"이해하기 쉬움" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"문제가 해결됨" },{ "type": "thumb-up", "id": "otherUp", "label":"기타" }]