tensorflow.python.eager.backprop 源代码

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Code for backpropagation using the tape utilities."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import functools
import operator
import sys

import six

from tensorflow.python import pywrap_tfe
from tensorflow.python import _pywrap_utils
from tensorflow.python.eager import backprop_util
from tensorflow.python.eager import context
from tensorflow.python.eager import execute
from tensorflow.python.eager import imperative_grad
from tensorflow.python.eager import tape
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_util
from tensorflow.python.ops import default_gradient
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import nest
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util import tf_inspect
from tensorflow.python.util.tf_export import tf_export

# Note that we need to lazy load the following two modules to avoid creating
# circular dependencies.
# TODO(b/119775953): fix the circular dependencies.
"pfor_ops", globals(),
"tensorflow.python.ops.parallel_for.control_flow_ops")

function = LazyLoader("function", globals(),
"tensorflow.python.eager.function")

_op_attr_type_cache = {}

def op_attr_type(op_type, attr_name):
try:
return _op_attr_type_cache[(op_type, attr_name)]
except KeyError:
context.ensure_initialized()
h = context.context()._handle  # pylint: disable=protected-access
attr_type = pywrap_tfe.TFE_OpNameGetAttrType(h, op_type, attr_name)
_op_attr_type_cache[(op_type, attr_name)] = attr_type
return attr_type

def make_attr(attr_type, value):
# pybind11 enums do not return the raw value like SWIG enums do. They are
# useful when comparing amongst each other but not direct integers as we are
# doing in most tests.
# TODO(amitpatankar): After all SWIG transitions, convert the enum comparisons
# from integer value to class.
if attr_type == int(pywrap_tfe.TF_ATTR_TYPE):
return dtypes.as_dtype(value)
elif attr_type == [int(pywrap_tfe.TF_ATTR_TYPE)]:
return [dtypes.as_dtype(v) for v in value]
elif attr_type == int(pywrap_tfe.TF_ATTR_SHAPE):
return tensor_shape.as_shape(value).as_proto()
elif attr_type == [int(pywrap_tfe.TF_ATTR_SHAPE)]:
return [tensor_shape.as_shape(v).as_proto() for v in value]
elif isinstance(value, str):
return value.encode()
return value

class _MockOp(object):
"""Pretends to be a tf.Operation for the gradient functions."""

def __init__(self, attrs, inputs, outputs, typ, skip_input_indices):
self.attrs = attrs
self.inputs = inputs
self.outputs = outputs
self.type = typ
self.skip_input_indices = skip_input_indices

def get_attr(self, attr):
typ = op_attr_type(self.type, attr)
for i in range(0, len(self.attrs), 2):
if self.attrs[i] == attr:
return make_attr(typ, self.attrs[i + 1])
raise KeyError(attr)

def _get_control_flow_context(self):
raise NotImplementedError(
"tf.GradientTape.gradients() does not support graph control flow "
"operations like tf.cond or tf.while at this time. Use tf.gradients() "
"instead. If you need this feature, please file a feature request at "
"https://github.com/tensorflow/tensorflow/issues/new"
)

def _gradient_function(op_name, attr_tuple, num_inputs, inputs, outputs,
"""Calls the gradient function of the op.

Args:
op_name: the name of the op to be differentiated.
attr_tuple: the attrs, as a tuple.
num_inputs: the number of inputs to the op.
inputs: inputs to the original operation.
outputs: outputs to the original operation.
out_grads: gradients of the operation wrt its outputs.
skip_input_indices: a tuple that is passed to the gradient function,
indicating which inputs to skip calculating the gradient for
forward_pass_name_scope: the namescope of the op in the forward pass.

Returns:
The gradients with respect to the inputs of the function, as a list.
"""
mock_op = _MockOp(attr_tuple, inputs, outputs, op_name, skip_input_indices)
if grad_fn is None:
return [None] * num_inputs

# This does not work with v1 TensorArrays.
if ops.executing_eagerly_outside_functions(
) or control_flow_util.EnableControlFlowV2(ops.get_default_graph()):
if forward_pass_name_scope:
gradient_name_scope = "gradient_tape/" + forward_pass_name_scope + "/"
else:
else:

return not pywrap_tfe.TFE_Py_TapeSetIsEmpty()

def _record_gradient(op_name, inputs, attrs, results):
return pywrap_tfe.TFE_Py_RecordGradient(op_name, inputs, attrs, results,
ops.get_name_scope())

"""Returns a function which differentiates f with respect to variables.

The wrapped function returns the value and the gradient of f when called with
the same arguments. The gradient is with respect to all trainable TFE
variables accessed by f.

This function is useful when the exact set of variables to differentiate with
is not known ahead of time.

Example:

python
dense_layer = tf.compat.v1.layers.Dense(1)
def loss(x, y):
return tf.reduce_sum(tf.square(dense_layer(x) - y))

# Obtain the gradient function.

# Invoke the gradient function with concrete values of x and y.
x = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
y = tf.constant([[10.0], [20.0]])
print('Value of loss: %s' % value)

# Apply the gradients to Variables.


Args:
f: function to be differentiated. If f returns a scalar, this scalar will
be differentiated. If f returns a tensor or list of tensors, by default
a scalar will be computed by adding all their values to produce a single
scalar.

Returns:
A function which, when called, returns a tuple pair.
Its first element is the value to which the function evaluates.
Its second element is list of (gradient, variable) pairs.

Raises:
ValueError: if f returns None.
"""
# TODO(cais): Remove calls to tf.constant() once the gradients functions
# accept lists and np.ndarrays.

"""Computes the gradient of the wrapped function."""
this_tape = tape.push_new_tape()
try:
end_node = f(*args, **kwds)
if end_node is None:
raise ValueError("Cannot differentiate a function that returns None; "
"did you forget to return a value from {}?".format(
f.__name__))
finally:
tape.pop_tape(this_tape)
# Note: variables are returned in construction order. This ensures unique
# order across executions.
variables = this_tape.watched_variables()
if not variables:
raise ValueError("No trainable variables were accessed while the "
"function was being computed.")

sources = [v.handle for v in variables]
sources)
return end_node, list(zip(grad, variables))

"""Returns a function which differentiates f with respect to variables.

The wrapped function returns the gradient of f when called with the same
arguments. The gradient is with respect to all trainable TFE variables
accessed by f.

This function is useful when the exact set of variables to differentiate with
is not known ahead of time.

Example:

python
dense_layer = tf.compat.v1.layers.Dense(1)
def loss(x, y):
return tf.reduce_sum(tf.square(dense_layer(x) - y))

# Obtain the gradient function.

# Invoke the gradient function with concrete values of x and y.
x = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
y = tf.constant([[10.0], [20.0]])

# Apply the gradients to Variables.


Args:
f: function to be differentiated. If f returns a scalar, this scalar will
be differentiated. If f returns a tensor or list of tensors, by default
a scalar will be computed by adding all their values to produce a single
scalar.

Returns:
A function which, when called, returns a list of (gradient, variable) pairs.
"""
# TODO(cais): Remove calls to tf.constant() once the gradients functions
# accept lists and np.ndarrays.

"""Computes the gradient of the wrapped function."""

def _get_arg_spec(f, params, param_args):
"""The positions of the parameters of f to be differentiated in param_args."""
try:
args = tf_inspect.getfullargspec(f).args
except TypeError as e:
# TypeError can happen when f is a callable object.
if params is None:
return range(len(param_args))
elif all(isinstance(x, int) for x in params):
return params
raise ValueError("Either callable provided is not a function or could not "
"inspect its arguments by name: %s. Original error: %s"
% (f, e))
if params is None:
if not args:
return range(len(param_args))
if args[0] == "self":
return range(len(args) - 1)
else:
return range(len(args))
elif all(isinstance(x, six.string_types) for x in params):
return [args.index(n) for n in params]
elif all(isinstance(x, int) for x in params):
return params
else:
raise ValueError(
"params must be all strings or all integers; got %s." % params)

"""Returns a function which differentiates f with respect to params.

Example:
python
# f(x, y) = (x ^ 3) * y - x * (y ^ 2)
# Therefore, the 1st order derivatives are:
#   df / dx = 3 * (x ^ 2) * y - y ^ 2
#   df / dy = x ^ 3 - 2 * x * y
# The 2nd order derivatives with respect to x is:
#   d^2 f / (dx)^2 = 6 * x * y
def f(x, y):
return x * x * x * y - x * y * y

# Obtain a function that returns 1st order gradients.

x = 2.0
y = 3.0

# Invoke the 1st order gradient function.
assert x_grad.numpy() == 3 * (2 ** 2) * 3 - 3 ** 2
assert y_grad.numpy() == (2 ** 3) - 2 * 2 * 3

# Obtain a function that returns the 2nd order gradient with respect to x.

# Invoke the 2nd order gradient function.
assert x_gradgrad.numpy() == 6 * 2 * 3

# To obtain a callable that returns the gradient(s) of f with respect to a
# subset of its inputs, use the params keyword argument with
# gradients_function().

assert y_grad.numpy() == (2 ** 3) - 2 * 2 * 3


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

Args:
f: function to be differentiated. If f returns a scalar, this scalar will
be differentiated. If f returns a tensor or list of tensors, by default
a scalar will be computed by adding all their values to produce a single
scalar. If desired, the tensors can be elementwise multiplied by the
tensors passed as the dy keyword argument to the returned gradient
function.
params: list of parameter names of f or list of integers indexing the
parameters with respect to which we'll differentiate. Passing None
differentiates with respect to all parameters.

Returns:
function which, when called, returns the value of f and the gradient
of f with respect to all of params. The function takes an extra optional
keyword argument dy. Setting it allows computation of vector jacobian
products for vectors other than the vector of ones.

Raises:
ValueError: if the params are not all strings or all integers.
"""

def decorated(*args, **kwds):
"""Computes the gradient of the decorated function."""

return decorated

def _ensure_unique_tensor_objects(parameter_positions, args):
"""Make each of the parameter_positions in args a unique ops.Tensor object.

Ensure that each parameter is treated independently.
For example:

def f(x, y): return x * y
one = tf.constant(1.)

g(one, one) should return [1., 1.]
(even though the two arguments are the same Tensor object).

Args:
parameter_positions: List of indices into args defining the arguments to
differentiate against.
args: A list of arguments to the function to be differentiated.

Returns:
args, possibly edited in-place.
"""
s = set()
for (i, t) in enumerate(args):
if i in parameter_positions:
tid = ops.tensor_id(t)
if tid in s:
args[i] = gen_array_ops.identity(args[i])
else:
return args

"""Returns a function that computes f and its derivative w.r.t. params.

Example:
python
# f(x, y) = (x ^ 3) * y - x * (y ^ 2)
# Therefore, the 1st order derivatives are:
#   df / dx = 3 * (x ^ 2) * y - y ^ 2
#   df / dy = x ^ 3 - 2 * x * y
def f(x, y):
return x * x * x * y - x * y * y

# Obtain a function that returns the function value and the 1st order

x = 2.0
y = 3.0

# Invoke the value-and-gradients function.
assert f_val.numpy() == (2 ** 3) * 3 - 2 * (3 ** 2)
assert x_grad.numpy() == 3 * (2 ** 2) * 3 - 3 ** 2
assert y_grad.numpy() == (2 ** 3) - 2 * 2 * 3

# To obtain a callable that returns the value of f and the gradient(s) of
# f with respect to a subset of its inputs, use the params keyword
# argument with value_and_gradients_function().

assert f_val.numpy() == (2 ** 3) * 3 - 2 * (3 ** 2)
assert y_grad.numpy() == (2 ** 3) - 2 * 2 * 3


Args:
f: function to be differentiated. If f returns a scalar, this scalar will
be differentiated. If f returns a tensor or list of tensors, by default
a scalar will be computed by adding all their values to produce a single
scalar. If desired, the tensors can be elementwise multiplied by the
tensors passed as the dy keyword argument to the returned gradient
function.
params: list of parameter names of f or list of integers indexing the
parameters with respect to which we'll differentiate. Passing None
differentiates with respect to all parameters.

Returns:
function which, when called, returns the value of f and the gradient
of f with respect to all of params. The function takes an extra optional
keyword argument "dy". Setting it allows computation of vector jacobian
products for vectors other than the vector of ones.

Raises:
ValueError: if the params are not all strings or all integers.
"""

def decorated(*args, **kwds):
"""Computes the value and gradient of the decorated function."""
dy = kwds.pop("dy", None)
if kwds:
raise ValueError("Functions to be differentiated cannot "
val, vjp = make_vjp(f, params)(*args, **kwds)
return val, vjp(dy=dy)

return decorated

def make_vjp(f, params=None, persistent=True):
"""Returns a function that computes f and its vjp w.r.t.

params.

The term "vjp" here is an abbreviation for vector-jacobian product.

Args:
f: the function to be differentiated.
params: the parameters (numbers or names) to differentiate with respect to.
A value of None will differentiate with respect to all parameters.
persistent: Boolean controlling whether the VJP function can be re-used.
Must be True or False.

Returns:
A function, which when called, returns a tuple (value, vjp), where:
- value is the result of calling f.
- vjp is a function, which takes a vector as an argument and
returns the product of that vector with the Jacobian of f.
Providing no argument to vjp is equivalent to providing a
vector of ones.

For example,
python
def f(x):
return x * x

wrapped_fn = tfe.make_vjp(f)
result, vjp = wrapped_fn(tf.constant(3.0))
# result is 9.0
vjp()  # the vjp function rturns 6.0

Raises:
ValueError: if f returns None.
"""

def decorated(*args, **kwds):
"""Computes the value and gradient of the decorated function."""
parameter_positions = _get_arg_spec(f, params, args)
assert not kwds, "The gradient function can't take keyword arguments."
this_tape = tape.push_new_tape(persistent=persistent)
try:
sources = []
args = [
ops.convert_to_tensor(arg) if i in parameter_positions else arg
for i, arg in enumerate(args)
]
args = _ensure_unique_tensor_objects(parameter_positions, args)
for i in parameter_positions:
sources.append(args[i])
tape.watch(this_tape, args[i])
result = f(*args)
if result is None:
raise ValueError("Cannot differentiate a function that returns None; "
"did you forget to return a value from {}?".format(
f.__name__))
flat_result = nest.flatten(result)
flat_result = [gen_array_ops.identity(x) for x in flat_result]
result = nest.pack_sequence_as(result, flat_result)
finally:
tape.pop_tape(this_tape)
def vjp(dy=None):
if dy is not None:
dy = [ops.convert_to_tensor(x) for x in nest.flatten(dy)]
this_tape, nest.flatten(result), sources, output_gradients=dy)

return result, vjp

return decorated

else:
g.indices),
g.dense_shape)

"""Aggregates gradients containing IndexedSlicess."""
if len(grads) < 1:
return None
elif len(grads) == 1:
else:
grads = [g for g in grads if g is not None]
# If any gradient is a Tensor, sum them up and return a dense tensor
# object.
if any(isinstance(g, ops.Tensor) for g in grads):

# The following _as_indexed_slices_list casts ids of IndexedSlices into
# int64. It is to make sure the inputs of concat all have same the data
# type.

grads = [flatten_nested_indexed_slices(x) for x in grads]
# Form IndexedSlices out of the concatenated values and indices.
array_ops.concat([x.values for x in grads], axis=0),
array_ops.concat([x.indices for x in grads], axis=0),

"""Aggregate gradients from multiple sources.

Args:
gradients: A list of 'Tensor' or 'IndexedSlices' gradients.

Returns:
If 'gradients' only has 'Tensor', returns an aggregated 'Tensor'.
Otherwise returns an aggregated 'IndexedSlices'.
"""

if len(gradients) == 1:
if all(isinstance(g, ops.Tensor) for g in gradients):
else:
assert all(isinstance(g, (ops.Tensor, ops.IndexedSlices))
for g in gradients)

"""The number of elements in the grad tensor."""
shape_tuple = grad._shape_tuple()  # pylint: disable=protected-access
shape_tuple = grad.values._shape_tuple()  # pylint: disable=protected-access
else:
raise ValueError("grad not a Tensor or IndexedSlices.")
if shape_tuple is None or None in shape_tuple:
return 0
return functools.reduce(operator.mul, shape_tuple, 1)

def _fast_fill(value, shape, dtype):
return array_ops.fill(
constant_op.constant(shape, dtype=dtypes.int32),
constant_op.constant(value, dtype=dtype))

def _zeros(shape, dtype):
"""Helper to return (possibly cached) zero tensors in eager mode."""
# Note: variants will use _zeros_like
if dtype == dtypes.string or dtype == dtypes.resource:
return None

ctx = context.context()
if not ctx.executing_eagerly():
return array_ops.zeros(shape, dtype)

device = ctx.device_name

if tensor_util.is_tensor(shape):
shape_key = shape.ref()
else:
shape_key = shape
cache_key = shape_key, dtype, device
cached = ctx.zeros_cache().get(cache_key)
if cached is None:
if dtypes.as_dtype(dtype).is_bool:
value = False
else:
value = 0
cached = _fast_fill(value, shape, dtype)
ctx.zeros_cache().put(cache_key, cached)
return cached

def _ones(shape, dtype):
as_dtype = dtypes.as_dtype(dtype)
if as_dtype == dtypes.string:
return None

if not context.context().executing_eagerly():
return array_ops.ones(shape, dtype)

if as_dtype.is_bool:
value = True
else:
value = 1

if shape == ():  # pylint: disable=g-explicit-bool-comparison
return constant_op.constant(value, dtype=dtype)
return _fast_fill(value, shape, dtype)

num_elements_fn=_num_elements,
zeros_fn=_zeros,
ones_fn=_ones,
graph_shape_fn=gen_array_ops.shape)
pywrap_tfe.TFE_Py_RegisterVSpace(_default_vspace)

def _handle_or_self(x):
"""If x is ResourceVariable, return its handle, else x."""
if resource_variable_ops.is_resource_variable(x):
x = x.handle
return x

"""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:

python
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,

python
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
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:

python
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:

python
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:

python
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 Nones since a's variables
# are not being watched.


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

def __init__(self, persistent=False, watch_accessed_variables=True):
"""Creates a new GradientTape.

Args:
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
"""
self._tape = None
self._persistent = persistent
self._watch_accessed_variables = watch_accessed_variables
self._watched_variables = ()
self._recording = False
self._created_eagerly = context.executing_eagerly()
if self._created_eagerly:
context.ensure_initialized()
context.context().start_step()

def __enter__(self):
"""Enters a context inside which operations are recorded on this tape."""
self._push_tape()
return self

def __exit__(self, typ, value, traceback):
"""Exits the recording context, no further operations are traced."""
if self._recording:
self._pop_tape()

def _push_tape(self):
"""Pushes a new tape onto the tape stack."""
if self._recording:
raise ValueError("Tape is still recording, This can happen if you try to "
"re-enter an already-active tape.")
if self._tape is None:
self._tape = tape.push_new_tape(
persistent=self._persistent,
watch_accessed_variables=self._watch_accessed_variables)
else:
tape.push_tape(self._tape)
self._recording = True

def _pop_tape(self):
if not self._recording:
raise ValueError("Tape is not recording.")
tape.pop_tape(self._tape)
self._recording = False

def __del__(self):
if self._created_eagerly:
try:
context.context().end_step()
except AttributeError:
pass
except TypeError:
pass

[文档]  def watch(self, tensor):
"""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.
"""
for t in nest.flatten(tensor):
if not (_pywrap_utils.IsTensor(t) or _pywrap_utils.IsVariable(t)):
raise ValueError("Passed in object of type {}, not tf.Tensor".format(
type(t)))
if not backprop_util.IsTrainable(t):
logging.log_first_n(
logging.WARN, "The dtype of the watched tensor must be "
"floating (e.g. tf.float32), got %r", 5, t.dtype)
if hasattr(t, "handle"):
# There are many variable-like objects, all of them currently have
# handle attribute that points to a tensor. If this changes, internals
# of watch_variable need to change as well.
tape.watch_variable(self._tape, t)
else:
tape.watch(self._tape, t)

[文档]  @tf_contextlib.contextmanager
def stop_recording(self):
"""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.
"""
if self._tape is None:
raise RuntimeError(
"Trying to stop recording a tape which is not recording.")
self._pop_tape()
try:
yield
finally:
self._push_tape()

[文档]  def reset(self):
"""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()

"""
self._pop_tape()
self._tape = None
self._push_tape()

[文档]  def watched_variables(self):
"""Returns variables watched by this tape in order of construction."""
if self._tape is not None:
self._watched_variables = self._tape.watched_variables()
return self._watched_variables

target,
sources,
"""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.
"""
if self._tape is None:
raise RuntimeError("GradientTape.gradient can only be called once on "
"non-persistent tapes.")
if self._recording:
if not self._persistent:
self._pop_tape()
else:
logging.log_first_n(
logging.WARN, "Calling GradientTape.gradient on a persistent "
"tape inside its context is significantly less "
"efficient than calling it outside the context (it "
"causes the gradient ops to be recorded on the "
"tape, leading to increased CPU and memory usage). "
"context if you actually want to trace the "
"gradient in order to compute higher order "
"derivatives.", 1)

flat_targets = []
for t in nest.flatten(target):
if not backprop_util.IsTrainable(t):
logging.vlog(
logging.WARN, "The dtype of the target tensor must be "
"floating (e.g. tf.float32) when calling GradientTape.gradient, "
"got %r", t.dtype)
if resource_variable_ops.is_resource_variable(t):
with self:
t = ops.convert_to_tensor(t)
flat_targets.append(t)

flat_sources = nest.flatten(sources)
flat_sources_raw = flat_sources
flat_sources = [_handle_or_self(x) for x in flat_sources]
for t in flat_sources_raw:
if not backprop_util.IsTrainable(t):
logging.vlog(
logging.WARN, "The dtype of the source tensor must be "
"floating (e.g. tf.float32) when calling GradientTape.gradient, "
"got %r", t.dtype)

if output_gradients is not None:
output_gradients = [None if x is None else ops.convert_to_tensor(x)
for x in nest.flatten(output_gradients)]

self._tape,
flat_targets,
flat_sources,
sources_raw=flat_sources_raw,

if not self._persistent:
# Keep track of watched variables before setting tape to None
self._watched_variables = self._tape.watched_variables()
self._tape = None

[文档]  def jacobian(self,
target,
sources,
parallel_iterations=None,
experimental_use_pfor=True):
"""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:

python
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.
"""
flat_sources = nest.flatten(sources)
target_static_shape = target.shape
target_shape = array_ops.shape(target)
# Note that we push and pop the tape here and below. This is needed since we
# need gradients through the enclosed operations.
self._push_tape()
target = array_ops.reshape(target, [-1])
self._pop_tape()

def loop_fn(i):
self._push_tape()
y = array_ops.gather(target, i)
self._pop_tape()

try:
target_size = int(target.shape[0])
except TypeError:
target_size = array_ops.shape(target)[0]

if experimental_use_pfor:
try:
output = pfor_ops.pfor(loop_fn, target_size,
parallel_iterations=parallel_iterations)
except ValueError as err:
six.reraise(
ValueError,
ValueError(
str(err) + "\nEncountered an exception while vectorizing the "
"jacobian computation. Vectorization can be disabled by setting"
" experimental_use_pfor to False."),
sys.exc_info()[2])
else:
if context.executing_eagerly() and not self._persistent:
raise RuntimeError(
"GradientTape must be created with persistent=True"
" to compute the jacobian with eager execution enabled and with "
" experimental_use_pfor set to False.")
output = pfor_ops.for_loop(
loop_fn, [target.dtype] * len(flat_sources), target_size,
parallel_iterations=parallel_iterations)

for i, out in enumerate(output):
if out is not None:
new_shape = array_ops.concat(
[target_shape, array_ops.shape(out)[1:]], axis=0)
out = array_ops.reshape(out, new_shape)
if context.executing_eagerly():
out.set_shape(target_static_shape.concatenate(flat_sources[i].shape))
output[i] = out

return nest.pack_sequence_as(sources, output)

[文档]  def batch_jacobian(self,
target,
source,
parallel_iterations=None,
experimental_use_pfor=True):
"""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:

python
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.
"""
target_shape = target.shape
if target_shape.rank is None:
dim = tensor_shape.Dimension(None)
else:
dim = target_shape.dims[0]
if not (target_shape.with_rank_at_least(2) and
source.shape.with_rank_at_least(2) and
dim.is_compatible_with(source.shape[0])):
raise ValueError(
"Need first dimension of target shape (%s) and "
"source shape (%s) to match." % (target.shape, source.shape))
if target_shape.is_fully_defined():
batch_size = int(target_shape[0])
target_row_size = target_shape.num_elements() // batch_size
else:
target_shape = array_ops.shape(target)
batch_size = target_shape[0]
target_row_size = array_ops.size(target) // batch_size
source_shape = array_ops.shape(source)
# Flatten target to 2-D.
# Note that we push and pop the tape here and below. This is needed since we
# need gradients through the enclosed operations.
self._push_tape()
with ops.control_dependencies(
[check_ops.assert_equal(batch_size, source_shape[0])]):
target = array_ops.reshape(target, [batch_size, target_row_size])
self._pop_tape()

def loop_fn(i):
self._push_tape()
y = array_ops.gather(target, i, axis=1)
self._pop_tape()

if experimental_use_pfor:
try:
output = pfor_ops.pfor(loop_fn, target_row_size,
parallel_iterations=parallel_iterations)
except ValueError as err:
six.reraise(
ValueError,
ValueError(
str(err) + "\nEncountered an exception while vectorizing the "
"batch_jacobian computation. Vectorization can be disabled by "
"setting experimental_use_pfor to False."),
sys.exc_info()[2])
else:
if context.executing_eagerly() and not self._persistent:
raise RuntimeError(
"GradientTape must be created with persistent=True"
" to compute the batch_jacobian with eager execution enabled and "
" with experimental_use_pfor set to False.")
output = pfor_ops.for_loop(loop_fn, target.dtype, target_row_size,
parallel_iterations=parallel_iterations)
new_shape = array_ops.concat([target_shape, source_shape[1:]], axis=0)
if output is None:
return array_ops.zeros(new_shape)
else:
output = array_ops.reshape(output,
[target_row_size, batch_size, -1])
output = array_ops.transpose(output, [1, 0, 2])
return array_ops.reshape(output, new_shape)
`