# Copyright 2015 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
# ==============================================================================
"""Operations for clipping (gradient, weight) tensors to min/max values."""
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import indexed_slices
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import gen_nn_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util import dispatch
from tensorflow.python.util.compat import collections_abc
from tensorflow.python.util.tf_export import tf_export
[文档]@tf_export("clip_by_value")
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def clip_by_value(t, clip_value_min, clip_value_max,
name=None):
"""Clips tensor values to a specified min and max.
Given a tensor `t`, this operation returns a tensor of the same type and
shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`.
Any values less than `clip_value_min` are set to `clip_value_min`. Any values
greater than `clip_value_max` are set to `clip_value_max`.
Note: `clip_value_min` needs to be smaller or equal to `clip_value_max` for
correct results.
For example:
Basic usage passes a scalar as the min and max value.
>>> t = tf.constant([[-10., -1., 0.], [0., 2., 10.]])
>>> t2 = tf.clip_by_value(t, clip_value_min=-1, clip_value_max=1)
>>> t2.numpy()
array([[-1., -1., 0.],
[ 0., 1., 1.]], dtype=float32)
The min and max can be the same size as `t`, or broadcastable to that size.
>>> t = tf.constant([[-1, 0., 10.], [-1, 0, 10]])
>>> clip_min = [[2],[1]]
>>> t3 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100)
>>> t3.numpy()
array([[ 2., 2., 10.],
[ 1., 1., 10.]], dtype=float32)
Broadcasting fails, intentionally, if you would expand the dimensions of `t`
>>> t = tf.constant([[-1, 0., 10.], [-1, 0, 10]])
>>> clip_min = [[[2, 1]]] # Has a third axis
>>> t4 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100)
Traceback (most recent call last):
...
InvalidArgumentError: Incompatible shapes: [2,3] vs. [1,1,2]
It throws a `TypeError` if you try to clip an `int` to a `float` value
(`tf.cast` the input to `float` first).
>>> t = tf.constant([[1, 2], [3, 4]], dtype=tf.int32)
>>> t5 = tf.clip_by_value(t, clip_value_min=-3.1, clip_value_max=3.1)
Traceback (most recent call last):
...
TypeError: Cannot convert ...
Args:
t: A `Tensor` or `IndexedSlices`.
clip_value_min: The minimum value to clip to. A scalar `Tensor` or one that
is broadcastable to the shape of `t`.
clip_value_max: The maximum value to clip to. A scalar `Tensor` or one that
is broadcastable to the shape of `t`.
name: A name for the operation (optional).
Returns:
A clipped `Tensor` or `IndexedSlices`.
Raises:
`tf.errors.InvalidArgumentError`: If the clip tensors would trigger array
broadcasting that would make the returned tensor larger than the input.
TypeError: If dtype of the input is `int32` and dtype of
the `clip_value_min` or `clip_value_max` is `float32`
"""
with ops.name_scope(name, "clip_by_value",
[t, clip_value_min, clip_value_max]) as name:
values = ops.convert_to_tensor(
t.values if isinstance(t, indexed_slices.IndexedSlices) else t,
name="t")
# Go through list of tensors, for each value in each tensor clip
t_min = math_ops.minimum(values, clip_value_max)
# Assert that the shape is compatible with the initial shape,
# to prevent unintentional broadcasting.
values.shape.assert_is_compatible_with(t_min.shape)
t_max = math_ops.maximum(t_min, clip_value_min, name=name)
values.shape.assert_is_compatible_with(t_max.shape)
if isinstance(t, indexed_slices.IndexedSlices):
t_max = indexed_slices.IndexedSlices(t_max, t.indices, t.dense_shape)
return t_max
# TODO(scottzhu): switch to use new implementation in 2 weeks.
# return gen_math_ops.clip_by_value(
# t, clip_value_min, clip_value_max, name=name)
# TODO(scottzhu): switch to use new implementation in 2 weeks.
# @ops.RegisterGradient("ClipByValue")
def _clip_by_value_grad(op, grad):
"""Returns grad of clip_by_value."""
x = op.inputs[0]
y = op.inputs[1]
z = op.inputs[2]
gdtype = grad.dtype
sx = array_ops.shape(x)
sy = array_ops.shape(y)
sz = array_ops.shape(z)
gradshape = array_ops.shape(grad)
zeros = array_ops.zeros(gradshape, gdtype)
xymask = math_ops.less(x, y)
xzmask = math_ops.greater(x, z)
rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
rx, rz = gen_array_ops.broadcast_gradient_args(sx, sz)
xgrad = array_ops.where(math_ops.logical_or(xymask, xzmask), zeros, grad)
ygrad = array_ops.where(xymask, grad, zeros)
zgrad = array_ops.where(xzmask, grad, zeros)
gx = array_ops.reshape(math_ops.reduce_sum(xgrad, rx), sx)
gy = array_ops.reshape(math_ops.reduce_sum(ygrad, ry), sy)
gz = array_ops.reshape(math_ops.reduce_sum(zgrad, rz), sz)
return (gx, gy, gz)
[文档]@tf_export("clip_by_norm")
@dispatch.add_dispatch_support
def clip_by_norm(t, clip_norm, axes=None, name=None):
"""Clips tensor values to a maximum L2-norm.
Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
normalizes `t` so that its L2-norm is less than or equal to `clip_norm`,
along the dimensions given in `axes`. Specifically, in the default case
where all dimensions are used for calculation, if the L2-norm of `t` is
already less than or equal to `clip_norm`, then `t` is not modified. If
the L2-norm is greater than `clip_norm`, then this operation returns a
tensor of the same type and shape as `t` with its values set to:
`t * clip_norm / l2norm(t)`
In this case, the L2-norm of the output tensor is `clip_norm`.
As another example, if `t` is a matrix and `axes == [1]`, then each row
of the output will have L2-norm less than or equal to `clip_norm`. If
`axes == [0]` instead, each column of the output will be clipped.
Code example:
>>> some_nums = tf.constant([[1, 2, 3, 4, 5]], dtype=tf.float32)
>>> tf.clip_by_norm(some_nums, 2.0).numpy()
array([[0.26967996, 0.5393599 , 0.80903983, 1.0787199 , 1.3483998 ]],
dtype=float32)
This operation is typically used to clip gradients before applying them with
an optimizer. Most gradient data is a collection of different shaped tensors
for different parts of the model. Thus, this is a common usage:
```
# Get your gradients after training
loss_value, grads = grad(model, features, labels)
# Apply some clipping
grads = [tf.clip_by_norm(g, norm)
for g in grads]
# Continue on with training
optimizer.apply_gradients(grads)
```
Args:
t: A `Tensor` or `IndexedSlices`. This must be a floating point type.
clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value, also
floating point
axes: A 1-D (vector) `Tensor` of type int32 containing the dimensions
to use for computing the L2-norm. If `None` (the default), uses all
dimensions.
name: A name for the operation (optional).
Returns:
A clipped `Tensor` or `IndexedSlices`.
Raises:
ValueError: If the clip_norm tensor is not a 0-D scalar tensor.
TypeError: If dtype of the input is not a floating point or
complex type.
"""
with ops.name_scope(name, "clip_by_norm", [t, clip_norm]) as name:
values = ops.convert_to_tensor(
t.values if isinstance(t, indexed_slices.IndexedSlices) else t,
name="t")
# Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
l2sum = math_ops.reduce_sum(values * values, axes, keepdims=True)
pred = l2sum > 0
# Two-tap tf.where trick to bypass NaN gradients
l2sum_safe = array_ops.where(pred, l2sum, array_ops.ones_like(l2sum))
l2norm = array_ops.where(pred, math_ops.sqrt(l2sum_safe), l2sum)
intermediate = values * clip_norm
# Assert that the shape is compatible with the initial shape,
# to prevent unintentional broadcasting.
values.shape.assert_is_compatible_with(intermediate.shape)
values_clip = array_ops.identity(
intermediate / math_ops.maximum(l2norm, clip_norm), name=name)
if isinstance(t, indexed_slices.IndexedSlices):
return indexed_slices.IndexedSlices(values_clip, t.indices, t.dense_shape)
return values_clip
@tf_export("linalg.global_norm", v1=["linalg.global_norm", "global_norm"])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints("global_norm")
def global_norm(t_list, name=None):
"""Computes the global norm of multiple tensors.
Given a tuple or list of tensors `t_list`, this operation returns the
global norm of the elements in all tensors in `t_list`. The global norm is
computed as:
`global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))`
Any entries in `t_list` that are of type None are ignored.
Args:
t_list: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
name: A name for the operation (optional).
Returns:
A 0-D (scalar) `Tensor` of type `float`.
Raises:
TypeError: If `t_list` is not a sequence.
"""
if (not isinstance(t_list, collections_abc.Sequence) or
isinstance(t_list, str)):
raise TypeError("`t_list` should be a sequence of tensors. Received "
f"{type(t_list)}.")
t_list = list(t_list)
with ops.name_scope(name, "global_norm", t_list) as name:
values = [
ops.convert_to_tensor(
t.values if isinstance(t, indexed_slices.IndexedSlices) else t,
name="t_%d" % i) if t is not None else t
for i, t in enumerate(t_list)
]
half_squared_norms = []
for v in values:
if v is not None:
with ops.colocate_with(v):
half_squared_norms.append(gen_nn_ops.l2_loss(v))
half_squared_norm = math_ops.reduce_sum(array_ops.stack(half_squared_norms))
norm = math_ops.sqrt(
half_squared_norm *
constant_op.constant(2.0, dtype=half_squared_norm.dtype),
name="global_norm")
return norm
[文档]@tf_export("clip_by_global_norm")
@dispatch.add_dispatch_support
def clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None):
"""Clips values of multiple tensors by the ratio of the sum of their norms.
Given a tuple or list of tensors `t_list`, and a clipping ratio `clip_norm`,
this operation returns a list of clipped tensors `list_clipped`
and the global norm (`global_norm`) of all tensors in `t_list`. Optionally,
if you've already computed the global norm for `t_list`, you can specify
the global norm with `use_norm`.
To perform the clipping, the values `t_list[i]` are set to:
t_list[i] * clip_norm / max(global_norm, clip_norm)
where:
global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))
If `clip_norm > global_norm` then the entries in `t_list` remain as they are,
otherwise they're all shrunk by the global ratio.
If `global_norm == infinity` then the entries in `t_list` are all set to `NaN`
to signal that an error occurred.
Any of the entries of `t_list` that are of type `None` are ignored.
This is the correct way to perform gradient clipping (Pascanu et al., 2012).
However, it is slower than `clip_by_norm()` because all the parameters must be
ready before the clipping operation can be performed.
Args:
t_list: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
clip_norm: A 0-D (scalar) `Tensor` > 0. The clipping ratio.
use_norm: A 0-D (scalar) `Tensor` of type `float` (optional). The global
norm to use. If not provided, `global_norm()` is used to compute the norm.
name: A name for the operation (optional).
Returns:
list_clipped: A list of `Tensors` of the same type as `list_t`.
global_norm: A 0-D (scalar) `Tensor` representing the global norm.
Raises:
TypeError: If `t_list` is not a sequence.
References:
On the difficulty of training Recurrent Neural Networks:
[Pascanu et al., 2012](http://proceedings.mlr.press/v28/pascanu13.html)
([pdf](http://proceedings.mlr.press/v28/pascanu13.pdf))
"""
if (not isinstance(t_list, collections_abc.Sequence) or
isinstance(t_list, str)):
raise TypeError("`t_list` should be a sequence of tensors. Received "
f"{type(t_list)}.")
t_list = list(t_list)
if use_norm is None:
use_norm = global_norm(t_list, name)
with ops.name_scope(name, "clip_by_global_norm",
t_list + [clip_norm]) as name:
# Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
scale_for_finite = clip_norm * math_ops.minimum(
1.0 / use_norm,
constant_op.constant(1.0, dtype=use_norm.dtype) / clip_norm)
# If use_norm is any finite number, this is a no-op. For inf/-inf/NaN,
# this will make scale NaN.
scale = scale_for_finite + (use_norm - use_norm)
values = [
ops.convert_to_tensor(
t.values if isinstance(t, indexed_slices.IndexedSlices) else t,
name="t_%d" % i) if t is not None else t
for i, t in enumerate(t_list)
]
values_clipped = []
for i, v in enumerate(values):
if v is None:
values_clipped.append(None)
else:
with ops.colocate_with(v):
values_clipped.append(
array_ops.identity(v * scale, name="%s_%d" % (name, i)))
list_clipped = [
indexed_slices.IndexedSlices(c_v, t.indices, t.dense_shape)
if isinstance(t, indexed_slices.IndexedSlices) else c_v
for (c_v, t) in zip(values_clipped, t_list)
]
return list_clipped, use_norm
@deprecation.deprecated(
date=None,
instructions="clip_by_average_norm is deprecated in TensorFlow 2.0. Please "
"use clip_by_norm(t, clip_norm * tf.cast(tf.size(t), tf.float32), name) "
"instead.")
@tf_export(v1=["clip_by_average_norm"])
@dispatch.add_dispatch_support
def clip_by_average_norm(t, clip_norm, name=None):
"""Clips tensor values to a maximum average L2-norm.
Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
normalizes `t` so that its average L2-norm is less than or equal to
`clip_norm`. Specifically, if the average L2-norm is already less than or
equal to `clip_norm`, then `t` is not modified. If the average L2-norm is
greater than `clip_norm`, then this operation returns a tensor of the same
type and shape as `t` with its values set to:
`t * clip_norm / l2norm_avg(t)`
In this case, the average L2-norm of the output tensor is `clip_norm`.
This operation is typically used to clip gradients before applying them with
an optimizer.
Args:
t: A `Tensor`.
clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
name: A name for the operation (optional).
Returns:
A clipped `Tensor`.
"""
with ops.name_scope(name, "clip_by_average_norm", [t, clip_norm]) as name:
t = ops.convert_to_tensor(t, name="t")
# Calculate L2-norm per element, clip elements by ratio of clip_norm to
# L2-norm per element
n_element = math_ops.cast(array_ops.size(t), dtypes.float32)
l2norm_inv = math_ops.rsqrt(
math_ops.reduce_sum(t * t, math_ops.range(array_ops.rank(t))))
tclip = array_ops.identity(
t * clip_norm * math_ops.minimum(
l2norm_inv * n_element, constant_op.constant(1.0) / clip_norm),
name=name)
return tclip