326 lines
12 KiB
Python
326 lines
12 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Operations for clipping (gradient, weight) tensors to min/max values."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import six
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_array_ops
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from tensorflow.python.ops import gen_nn_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("clip_by_value")
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def clip_by_value(t, clip_value_min, clip_value_max,
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name=None):
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"""Clips tensor values to a specified min and max.
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Given a tensor `t`, this operation returns a tensor of the same type and
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shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`.
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Any values less than `clip_value_min` are set to `clip_value_min`. Any values
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greater than `clip_value_max` are set to `clip_value_max`.
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Args:
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t: A `Tensor`.
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clip_value_min: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
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as `t`. The minimum value to clip by.
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clip_value_max: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
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as `t`. The maximum value to clip by.
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name: A name for the operation (optional).
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Returns:
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A clipped `Tensor`.
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Raises:
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ValueError: if the clip tensors would trigger array broadcasting
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that would make the returned tensor larger than the input.
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"""
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with ops.name_scope(name, "clip_by_value",
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[t, clip_value_min, clip_value_max]) as name:
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t = ops.convert_to_tensor(t, name="t")
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# Go through list of tensors, for each value in each tensor clip
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t_min = math_ops.minimum(t, clip_value_max)
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# Assert that the shape is compatible with the initial shape,
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# to prevent unintentional broadcasting.
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_ = t.shape.merge_with(t_min.shape)
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t_max = math_ops.maximum(t_min, clip_value_min, name=name)
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_ = t.shape.merge_with(t_max.shape)
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return t_max
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# TODO(scottzhu): switch to use new implmentation in 2 weeks.
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# return gen_math_ops.clip_by_value(
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# t, clip_value_min, clip_value_max, name=name)
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# TODO(scottzhu): switch to use new implmentation in 2 weeks.
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# @ops.RegisterGradient("ClipByValue")
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def _clip_by_value_grad(op, grad):
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"""Returns grad of clip_by_value."""
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x = op.inputs[0]
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y = op.inputs[1]
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z = op.inputs[2]
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gdtype = grad.dtype
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sx = array_ops.shape(x)
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sy = array_ops.shape(y)
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sz = array_ops.shape(z)
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gradshape = array_ops.shape(grad)
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zeros = array_ops.zeros(gradshape, gdtype)
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xymask = math_ops.less(x, y)
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xzmask = math_ops.greater(x, z)
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rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
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rx, rz = gen_array_ops.broadcast_gradient_args(sx, sz)
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xgrad = array_ops.where(math_ops.logical_or(xymask, xzmask), zeros, grad)
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ygrad = array_ops.where(xymask, grad, zeros)
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zgrad = array_ops.where(xzmask, grad, zeros)
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gx = array_ops.reshape(math_ops.reduce_sum(xgrad, rx), sx)
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gy = array_ops.reshape(math_ops.reduce_sum(ygrad, ry), sy)
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gz = array_ops.reshape(math_ops.reduce_sum(zgrad, rz), sz)
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return (gx, gy, gz)
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@tf_export("clip_by_norm")
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def clip_by_norm(t, clip_norm, axes=None, name=None):
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"""Clips tensor values to a maximum L2-norm.
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Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
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normalizes `t` so that its L2-norm is less than or equal to `clip_norm`,
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along the dimensions given in `axes`. Specifically, in the default case
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where all dimensions are used for calculation, if the L2-norm of `t` is
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already less than or equal to `clip_norm`, then `t` is not modified. If
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the L2-norm is greater than `clip_norm`, then this operation returns a
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tensor of the same type and shape as `t` with its values set to:
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`t * clip_norm / l2norm(t)`
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In this case, the L2-norm of the output tensor is `clip_norm`.
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As another example, if `t` is a matrix and `axes == [1]`, then each row
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of the output will have L2-norm equal to `clip_norm`. If `axes == [0]`
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instead, each column of the output will be clipped.
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This operation is typically used to clip gradients before applying them with
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an optimizer.
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Args:
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t: A `Tensor`.
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clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
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axes: A 1-D (vector) `Tensor` of type int32 containing the dimensions
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to use for computing the L2-norm. If `None` (the default), uses all
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dimensions.
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name: A name for the operation (optional).
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Returns:
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A clipped `Tensor`.
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"""
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with ops.name_scope(name, "clip_by_norm", [t, clip_norm]) as name:
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t = ops.convert_to_tensor(t, name="t")
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# Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
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l2norm = math_ops.sqrt(math_ops.reduce_sum(t * t, axes, keepdims=True))
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intermediate = t * clip_norm
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# Assert that the shape is compatible with the initial shape,
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# to prevent unintentional broadcasting.
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_ = t.shape.merge_with(intermediate.shape)
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tclip = array_ops.identity(
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intermediate / math_ops.maximum(l2norm, clip_norm), name=name)
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return tclip
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@tf_export("global_norm")
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def global_norm(t_list, name=None):
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"""Computes the global norm of multiple tensors.
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Given a tuple or list of tensors `t_list`, this operation returns the
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global norm of the elements in all tensors in `t_list`. The global norm is
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computed as:
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`global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))`
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Any entries in `t_list` that are of type None are ignored.
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Args:
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t_list: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
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name: A name for the operation (optional).
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Returns:
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A 0-D (scalar) `Tensor` of type `float`.
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Raises:
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TypeError: If `t_list` is not a sequence.
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"""
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if (not isinstance(t_list, collections.Sequence)
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or isinstance(t_list, six.string_types)):
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raise TypeError("t_list should be a sequence")
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t_list = list(t_list)
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with ops.name_scope(name, "global_norm", t_list) as name:
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values = [
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ops.convert_to_tensor(
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t.values if isinstance(t, ops.IndexedSlices) else t,
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name="t_%d" % i)
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if t is not None else t
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for i, t in enumerate(t_list)]
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half_squared_norms = []
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for v in values:
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if v is not None:
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with ops.colocate_with(v):
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half_squared_norms.append(gen_nn_ops.l2_loss(v))
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half_squared_norm = math_ops.reduce_sum(array_ops.stack(half_squared_norms))
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norm = math_ops.sqrt(
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half_squared_norm *
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constant_op.constant(2.0, dtype=half_squared_norm.dtype),
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name="global_norm")
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return norm
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@tf_export("clip_by_global_norm")
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def clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None):
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"""Clips values of multiple tensors by the ratio of the sum of their norms.
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Given a tuple or list of tensors `t_list`, and a clipping ratio `clip_norm`,
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this operation returns a list of clipped tensors `list_clipped`
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and the global norm (`global_norm`) of all tensors in `t_list`. Optionally,
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if you've already computed the global norm for `t_list`, you can specify
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the global norm with `use_norm`.
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To perform the clipping, the values `t_list[i]` are set to:
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t_list[i] * clip_norm / max(global_norm, clip_norm)
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where:
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global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))
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If `clip_norm > global_norm` then the entries in `t_list` remain as they are,
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otherwise they're all shrunk by the global ratio.
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Any of the entries of `t_list` that are of type `None` are ignored.
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This is the correct way to perform gradient clipping (for example, see
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[Pascanu et al., 2012](http://arxiv.org/abs/1211.5063)
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([pdf](http://arxiv.org/pdf/1211.5063.pdf))).
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However, it is slower than `clip_by_norm()` because all the parameters must be
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ready before the clipping operation can be performed.
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Args:
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t_list: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
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clip_norm: A 0-D (scalar) `Tensor` > 0. The clipping ratio.
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use_norm: A 0-D (scalar) `Tensor` of type `float` (optional). The global
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norm to use. If not provided, `global_norm()` is used to compute the norm.
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name: A name for the operation (optional).
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Returns:
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list_clipped: A list of `Tensors` of the same type as `list_t`.
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global_norm: A 0-D (scalar) `Tensor` representing the global norm.
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Raises:
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TypeError: If `t_list` is not a sequence.
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"""
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if (not isinstance(t_list, collections.Sequence)
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or isinstance(t_list, six.string_types)):
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raise TypeError("t_list should be a sequence")
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t_list = list(t_list)
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if use_norm is None:
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use_norm = global_norm(t_list, name)
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with ops.name_scope(name, "clip_by_global_norm",
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t_list + [clip_norm]) as name:
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# Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
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scale = clip_norm * math_ops.minimum(
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1.0 / use_norm,
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constant_op.constant(1.0, dtype=use_norm.dtype) / clip_norm)
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values = [
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ops.convert_to_tensor(
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t.values if isinstance(t, ops.IndexedSlices) else t,
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name="t_%d" % i)
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if t is not None else t
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for i, t in enumerate(t_list)]
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values_clipped = []
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for i, v in enumerate(values):
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if v is None:
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values_clipped.append(None)
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else:
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with ops.colocate_with(v):
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values_clipped.append(
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array_ops.identity(v * scale, name="%s_%d" % (name, i)))
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list_clipped = [
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ops.IndexedSlices(c_v, t.indices, t.dense_shape)
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if isinstance(t, ops.IndexedSlices)
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else c_v
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for (c_v, t) in zip(values_clipped, t_list)]
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return list_clipped, use_norm
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@tf_export("clip_by_average_norm")
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def clip_by_average_norm(t, clip_norm, name=None):
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"""Clips tensor values to a maximum average L2-norm.
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Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
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normalizes `t` so that its average L2-norm is less than or equal to
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`clip_norm`. Specifically, if the average L2-norm is already less than or
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equal to `clip_norm`, then `t` is not modified. If the average L2-norm is
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greater than `clip_norm`, then this operation returns a tensor of the same
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type and shape as `t` with its values set to:
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`t * clip_norm / l2norm_avg(t)`
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In this case, the average L2-norm of the output tensor is `clip_norm`.
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This operation is typically used to clip gradients before applying them with
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an optimizer.
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Args:
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t: A `Tensor`.
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clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
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name: A name for the operation (optional).
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Returns:
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A clipped `Tensor`.
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"""
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with ops.name_scope(name, "clip_by_average_norm", [t, clip_norm]) as name:
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t = ops.convert_to_tensor(t, name="t")
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# Calculate L2-norm per element, clip elements by ratio of clip_norm to
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# L2-norm per element
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n_element = math_ops.cast(array_ops.size(t), dtypes.float32)
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l2norm_inv = math_ops.rsqrt(
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math_ops.reduce_sum(t * t, math_ops.range(array_ops.rank(t))))
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tclip = array_ops.identity(
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t * clip_norm * math_ops.minimum(
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l2norm_inv * n_element, constant_op.constant(1.0) / clip_norm),
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name=name)
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return tclip
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