1352 lines
54 KiB
Python
1352 lines
54 KiB
Python
# 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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"""Implementation of Neural Net (NN) functions."""
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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 math
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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 function
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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 candidate_sampling_ops
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from tensorflow.python.ops import embedding_ops
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from tensorflow.python.ops import gen_array_ops # pylint: disable=unused-import
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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.ops import nn_ops
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from tensorflow.python.ops import sparse_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.util.deprecation import deprecated_args
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from tensorflow.python.util.deprecation import deprecated_argument_lookup
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("nn.log_poisson_loss")
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def log_poisson_loss(targets, log_input, compute_full_loss=False, name=None):
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"""Computes log Poisson loss given `log_input`.
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Gives the log-likelihood loss between the prediction and the target under the
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assumption that the target has a Poisson distribution.
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Caveat: By default, this is not the exact loss, but the loss minus a
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constant term [log(z!)]. That has no effect for optimization, but
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does not play well with relative loss comparisons. To compute an
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approximation of the log factorial term, specify
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compute_full_loss=True to enable Stirling's Approximation.
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For brevity, let `c = log(x) = log_input`, `z = targets`. The log Poisson
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loss is
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-log(exp(-x) * (x^z) / z!)
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= -log(exp(-x) * (x^z)) + log(z!)
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~ -log(exp(-x)) - log(x^z) [+ z * log(z) - z + 0.5 * log(2 * pi * z)]
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[ Note the second term is the Stirling's Approximation for log(z!).
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It is invariant to x and does not affect optimization, though
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important for correct relative loss comparisons. It is only
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computed when compute_full_loss == True. ]
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= x - z * log(x) [+ z * log(z) - z + 0.5 * log(2 * pi * z)]
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= exp(c) - z * c [+ z * log(z) - z + 0.5 * log(2 * pi * z)]
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Args:
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targets: A `Tensor` of the same type and shape as `log_input`.
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log_input: A `Tensor` of type `float32` or `float64`.
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compute_full_loss: whether to compute the full loss. If false, a constant
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term is dropped in favor of more efficient optimization.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of the same shape as `log_input` with the componentwise
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logistic losses.
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Raises:
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ValueError: If `log_input` and `targets` do not have the same shape.
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"""
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with ops.name_scope(name, "log_poisson_loss", [log_input, targets]) as name:
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log_input = ops.convert_to_tensor(log_input, name="log_input")
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targets = ops.convert_to_tensor(targets, name="targets")
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try:
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targets.get_shape().merge_with(log_input.get_shape())
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except ValueError:
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raise ValueError(
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"log_input and targets must have the same shape (%s vs %s)" %
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(log_input.get_shape(), targets.get_shape()))
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result = math_ops.exp(log_input) - log_input * targets
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if compute_full_loss:
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# need to create constant tensors here so that their dtypes can be matched
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# to that of the targets.
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point_five = constant_op.constant(0.5, dtype=targets.dtype)
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two_pi = constant_op.constant(2 * math.pi, dtype=targets.dtype)
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stirling_approx = (targets * math_ops.log(targets)) - targets + (
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point_five * math_ops.log(two_pi * targets))
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zeros = array_ops.zeros_like(targets, dtype=targets.dtype)
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ones = array_ops.ones_like(targets, dtype=targets.dtype)
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cond = math_ops.logical_and(targets >= zeros, targets <= ones)
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result += array_ops.where(cond, zeros, stirling_approx)
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return result
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@tf_export("nn.sigmoid_cross_entropy_with_logits")
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def sigmoid_cross_entropy_with_logits( # pylint: disable=invalid-name
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_sentinel=None,
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labels=None,
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logits=None,
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name=None):
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"""Computes sigmoid cross entropy given `logits`.
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Measures the probability error in discrete classification tasks in which each
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class is independent and not mutually exclusive. For instance, one could
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perform multilabel classification where a picture can contain both an elephant
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and a dog at the same time.
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For brevity, let `x = logits`, `z = labels`. The logistic loss is
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z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
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= z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
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= z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
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= z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
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= (1 - z) * x + log(1 + exp(-x))
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= x - x * z + log(1 + exp(-x))
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For x < 0, to avoid overflow in exp(-x), we reformulate the above
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x - x * z + log(1 + exp(-x))
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= log(exp(x)) - x * z + log(1 + exp(-x))
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= - x * z + log(1 + exp(x))
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Hence, to ensure stability and avoid overflow, the implementation uses this
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equivalent formulation
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max(x, 0) - x * z + log(1 + exp(-abs(x)))
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`logits` and `labels` must have the same type and shape.
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Args:
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_sentinel: Used to prevent positional parameters. Internal, do not use.
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labels: A `Tensor` of the same type and shape as `logits`.
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logits: A `Tensor` of type `float32` or `float64`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of the same shape as `logits` with the componentwise
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logistic losses.
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Raises:
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ValueError: If `logits` and `labels` do not have the same shape.
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"""
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# pylint: disable=protected-access
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nn_ops._ensure_xent_args("sigmoid_cross_entropy_with_logits", _sentinel,
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labels, logits)
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# pylint: enable=protected-access
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with ops.name_scope(name, "logistic_loss", [logits, labels]) as name:
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logits = ops.convert_to_tensor(logits, name="logits")
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labels = ops.convert_to_tensor(labels, name="labels")
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try:
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labels.get_shape().merge_with(logits.get_shape())
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except ValueError:
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raise ValueError("logits and labels must have the same shape (%s vs %s)" %
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(logits.get_shape(), labels.get_shape()))
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# The logistic loss formula from above is
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# x - x * z + log(1 + exp(-x))
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# For x < 0, a more numerically stable formula is
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# -x * z + log(1 + exp(x))
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# Note that these two expressions can be combined into the following:
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# max(x, 0) - x * z + log(1 + exp(-abs(x)))
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# To allow computing gradients at zero, we define custom versions of max and
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# abs functions.
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zeros = array_ops.zeros_like(logits, dtype=logits.dtype)
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cond = (logits >= zeros)
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relu_logits = array_ops.where(cond, logits, zeros)
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neg_abs_logits = array_ops.where(cond, -logits, logits)
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return math_ops.add(
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relu_logits - logits * labels,
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math_ops.log1p(math_ops.exp(neg_abs_logits)),
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name=name)
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@tf_export("nn.weighted_cross_entropy_with_logits")
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def weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None):
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"""Computes a weighted cross entropy.
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This is like `sigmoid_cross_entropy_with_logits()` except that `pos_weight`,
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allows one to trade off recall and precision by up- or down-weighting the
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cost of a positive error relative to a negative error.
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The usual cross-entropy cost is defined as:
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targets * -log(sigmoid(logits)) +
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(1 - targets) * -log(1 - sigmoid(logits))
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A value `pos_weights > 1` decreases the false negative count, hence increasing
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the recall.
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Conversely setting `pos_weights < 1` decreases the false positive count and
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increases the precision.
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This can be seen from the fact that `pos_weight` is introduced as a
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multiplicative coefficient for the positive targets term
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in the loss expression:
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targets * -log(sigmoid(logits)) * pos_weight +
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(1 - targets) * -log(1 - sigmoid(logits))
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For brevity, let `x = logits`, `z = targets`, `q = pos_weight`.
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The loss is:
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qz * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
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= qz * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
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= qz * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
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= qz * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
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= (1 - z) * x + (qz + 1 - z) * log(1 + exp(-x))
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= (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(-x))
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Setting `l = (1 + (q - 1) * z)`, to ensure stability and avoid overflow,
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the implementation uses
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(1 - z) * x + l * (log(1 + exp(-abs(x))) + max(-x, 0))
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`logits` and `targets` must have the same type and shape.
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Args:
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targets: A `Tensor` of the same type and shape as `logits`.
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logits: A `Tensor` of type `float32` or `float64`.
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pos_weight: A coefficient to use on the positive examples.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of the same shape as `logits` with the componentwise
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weighted logistic losses.
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Raises:
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ValueError: If `logits` and `targets` do not have the same shape.
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"""
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with ops.name_scope(name, "logistic_loss", [logits, targets]) as name:
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logits = ops.convert_to_tensor(logits, name="logits")
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targets = ops.convert_to_tensor(targets, name="targets")
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try:
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targets.get_shape().merge_with(logits.get_shape())
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except ValueError:
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raise ValueError(
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"logits and targets must have the same shape (%s vs %s)" %
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(logits.get_shape(), targets.get_shape()))
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# The logistic loss formula from above is
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# (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(-x))
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# For x < 0, a more numerically stable formula is
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# (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(x)) - l * x
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# To avoid branching, we use the combined version
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# (1 - z) * x + l * (log(1 + exp(-abs(x))) + max(-x, 0))
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log_weight = 1 + (pos_weight - 1) * targets
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return math_ops.add(
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(1 - targets) * logits,
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log_weight * (math_ops.log1p(math_ops.exp(-math_ops.abs(logits))) +
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nn_ops.relu(-logits)),
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name=name)
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@tf_export("nn.relu_layer")
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def relu_layer(x, weights, biases, name=None):
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"""Computes Relu(x * weight + biases).
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Args:
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x: a 2D tensor. Dimensions typically: batch, in_units
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weights: a 2D tensor. Dimensions typically: in_units, out_units
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biases: a 1D tensor. Dimensions: out_units
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name: A name for the operation (optional). If not specified
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"nn_relu_layer" is used.
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Returns:
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A 2-D Tensor computing relu(matmul(x, weights) + biases).
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Dimensions typically: batch, out_units.
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"""
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with ops.name_scope(name, "relu_layer", [x, weights, biases]) as name:
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x = ops.convert_to_tensor(x, name="x")
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weights = ops.convert_to_tensor(weights, name="weights")
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biases = ops.convert_to_tensor(biases, name="biases")
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xw_plus_b = nn_ops.bias_add(math_ops.matmul(x, weights), biases)
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return nn_ops.relu(xw_plus_b, name=name)
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def _swish_shape(op):
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"""Shape helper function for swish and _swish_grad function below."""
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return [op.inputs[0].shape]
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@function.Defun(shape_func=_swish_shape, func_name="swish_grad", noinline=True)
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def _swish_grad(features, grad):
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"""Gradient of Swish function defined below."""
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sigmoid_features = math_ops.sigmoid(features)
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activation_grad = (
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sigmoid_features * (1.0 + features * (1.0 - sigmoid_features)))
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return grad * activation_grad
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# Naively, x * tf.nn.sigmoid(x) requires keeping both x and sigmoid(x) around
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# for backprop, effectively doubling the tensor's memory consumption. We use a
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# @Defun decorator with noinline=True so that sigmoid(features) is re-computed
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# during backprop, and we can free the sigmoid(features) expression immediately
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# after use during the forward pass.
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@tf_export("nn.swish")
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@function.Defun(
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grad_func=_swish_grad,
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shape_func=_swish_shape,
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func_name="swish",
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noinline=True)
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def swish(features):
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# pylint: disable=g-doc-args
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"""Computes the Swish activation function: `x * sigmoid(x)`.
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Source: "Searching for Activation Functions" (Ramachandran et al. 2017)
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https://arxiv.org/abs/1710.05941
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Args:
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features: A `Tensor` representing preactivation values.
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name: A name for the operation (optional).
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Returns:
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The activation value.
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"""
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# pylint: enable=g-doc-args
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features = ops.convert_to_tensor(features, name="features")
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return features * math_ops.sigmoid(features)
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@tf_export("nn.l2_normalize")
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@deprecated_args(None, "dim is deprecated, use axis instead", "dim")
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def l2_normalize(x, axis=None, epsilon=1e-12, name=None, dim=None):
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"""Normalizes along dimension `axis` using an L2 norm.
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For a 1-D tensor with `axis = 0`, computes
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output = x / sqrt(max(sum(x**2), epsilon))
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For `x` with more dimensions, independently normalizes each 1-D slice along
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dimension `axis`.
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Args:
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x: A `Tensor`.
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axis: Dimension along which to normalize. A scalar or a vector of
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integers.
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epsilon: A lower bound value for the norm. Will use `sqrt(epsilon)` as the
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divisor if `norm < sqrt(epsilon)`.
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name: A name for this operation (optional).
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dim: Deprecated alias for axis.
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Returns:
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A `Tensor` with the same shape as `x`.
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"""
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with ops.name_scope(name, "l2_normalize", [x]) as name:
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axis = deprecated_argument_lookup("axis", axis, "dim", dim)
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x = ops.convert_to_tensor(x, name="x")
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square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keepdims=True)
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x_inv_norm = math_ops.rsqrt(math_ops.maximum(square_sum, epsilon))
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return math_ops.multiply(x, x_inv_norm, name=name)
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@tf_export("nn.zero_fraction")
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def zero_fraction(value, name=None):
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"""Returns the fraction of zeros in `value`.
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If `value` is empty, the result is `nan`.
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This is useful in summaries to measure and report sparsity. For example,
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```python
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z = tf.nn.relu(...)
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summ = tf.summary.scalar('sparsity', tf.nn.zero_fraction(z))
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```
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Args:
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value: A tensor of numeric type.
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name: A name for the operation (optional).
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Returns:
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The fraction of zeros in `value`, with type `float32`.
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"""
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with ops.name_scope(name, "zero_fraction", [value]):
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value = ops.convert_to_tensor(value, name="value")
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zero = constant_op.constant(0, dtype=value.dtype, name="zero")
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return math_ops.reduce_mean(
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math_ops.cast(math_ops.equal(value, zero), dtypes.float32))
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# pylint: disable=redefined-builtin
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@tf_export("nn.depthwise_conv2d")
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def depthwise_conv2d(input,
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filter,
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strides,
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padding,
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rate=None,
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name=None,
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data_format=None):
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"""Depthwise 2-D convolution.
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Given a 4D input tensor ('NHWC' or 'NCHW' data formats)
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and a filter tensor of shape
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`[filter_height, filter_width, in_channels, channel_multiplier]`
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containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d`
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applies a different filter to each input channel (expanding from 1 channel
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to `channel_multiplier` channels for each), then concatenates the results
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together. The output has `in_channels * channel_multiplier` channels.
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In detail,
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output[b, i, j, k * channel_multiplier + q] = sum_{di, dj}
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filter[di, dj, k, q] * input[b, strides[1] * i + rate[0] * di,
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strides[2] * j + rate[1] * dj, k]
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Must have `strides[0] = strides[3] = 1`. For the most common case of the
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same horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
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If any value in `rate` is greater than 1, we perform atrous depthwise
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convolution, in which case all values in the `strides` tensor must be equal
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to 1.
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Args:
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input: 4-D with shape according to `data_format`.
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filter: 4-D with shape
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`[filter_height, filter_width, in_channels, channel_multiplier]`.
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strides: 1-D of size 4. The stride of the sliding window for each
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dimension of `input`.
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padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
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See the @{tf.nn.convolution$comment here}
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rate: 1-D of size 2. The dilation rate in which we sample input values
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across the `height` and `width` dimensions in atrous convolution. If it is
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greater than 1, then all values of strides must be 1.
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name: A name for this operation (optional).
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data_format: The data format for input. Either "NHWC" (default) or "NCHW".
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Returns:
|
|
A 4-D `Tensor` with shape according to `data_format`. E.g., for
|
|
"NHWC" format, shape is
|
|
`[batch, out_height, out_width, in_channels * channel_multiplier].`
|
|
"""
|
|
with ops.name_scope(name, "depthwise", [input, filter]) as name:
|
|
input = ops.convert_to_tensor(input, name="tensor_in")
|
|
filter = ops.convert_to_tensor(filter, name="filter_in")
|
|
if rate is None:
|
|
rate = [1, 1]
|
|
|
|
def op(input_converted, _, padding):
|
|
return nn_ops.depthwise_conv2d_native(
|
|
input=input_converted,
|
|
filter=filter,
|
|
strides=strides,
|
|
padding=padding,
|
|
data_format=data_format,
|
|
name=name)
|
|
|
|
return nn_ops.with_space_to_batch(
|
|
input=input,
|
|
filter_shape=array_ops.shape(filter),
|
|
dilation_rate=rate,
|
|
padding=padding,
|
|
data_format=data_format,
|
|
op=op)
|
|
|
|
|
|
# pylint: enable=redefined-builtin
|
|
|
|
|
|
# pylint: disable=redefined-builtin,line-too-long
|
|
@tf_export("nn.separable_conv2d")
|
|
def separable_conv2d(input,
|
|
depthwise_filter,
|
|
pointwise_filter,
|
|
strides,
|
|
padding,
|
|
rate=None,
|
|
name=None,
|
|
data_format=None):
|
|
"""2-D convolution with separable filters.
|
|
|
|
Performs a depthwise convolution that acts separately on channels followed by
|
|
a pointwise convolution that mixes channels. Note that this is separability
|
|
between dimensions `[1, 2]` and `3`, not spatial separability between
|
|
dimensions `1` and `2`.
|
|
|
|
In detail,
|
|
|
|
output[b, i, j, k] = sum_{di, dj, q, r}
|
|
input[b, strides[1] * i + di, strides[2] * j + dj, q] *
|
|
depthwise_filter[di, dj, q, r] *
|
|
pointwise_filter[0, 0, q * channel_multiplier + r, k]
|
|
|
|
`strides` controls the strides for the depthwise convolution only, since
|
|
the pointwise convolution has implicit strides of `[1, 1, 1, 1]`. Must have
|
|
`strides[0] = strides[3] = 1`. For the most common case of the same
|
|
horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
|
|
If any value in `rate` is greater than 1, we perform atrous depthwise
|
|
convolution, in which case all values in the `strides` tensor must be equal
|
|
to 1.
|
|
|
|
Args:
|
|
input: 4-D `Tensor` with shape according to `data_format`.
|
|
depthwise_filter: 4-D `Tensor` with shape
|
|
`[filter_height, filter_width, in_channels, channel_multiplier]`.
|
|
Contains `in_channels` convolutional filters of depth 1.
|
|
pointwise_filter: 4-D `Tensor` with shape
|
|
`[1, 1, channel_multiplier * in_channels, out_channels]`. Pointwise
|
|
filter to mix channels after `depthwise_filter` has convolved spatially.
|
|
strides: 1-D of size 4. The strides for the depthwise convolution for
|
|
each dimension of `input`.
|
|
padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
|
|
See the @{tf.nn.convolution$comment here}
|
|
rate: 1-D of size 2. The dilation rate in which we sample input values
|
|
across the `height` and `width` dimensions in atrous convolution. If it is
|
|
greater than 1, then all values of strides must be 1.
|
|
name: A name for this operation (optional).
|
|
data_format: The data format for input. Either "NHWC" (default) or "NCHW".
|
|
|
|
Returns:
|
|
A 4-D `Tensor` with shape according to 'data_format'. For
|
|
example, with data_format="NHWC", shape is [batch, out_height,
|
|
out_width, out_channels].
|
|
"""
|
|
with ops.name_scope(name, "separable_conv2d",
|
|
[input, depthwise_filter, pointwise_filter]) as name:
|
|
input = ops.convert_to_tensor(input, name="tensor_in")
|
|
depthwise_filter = ops.convert_to_tensor(
|
|
depthwise_filter, name="depthwise_filter")
|
|
pointwise_filter = ops.convert_to_tensor(
|
|
pointwise_filter, name="pointwise_filter")
|
|
|
|
pointwise_filter_shape = pointwise_filter.get_shape().with_rank(4)
|
|
pointwise_filter_shape[0].assert_is_compatible_with(1)
|
|
pointwise_filter_shape[1].assert_is_compatible_with(1)
|
|
|
|
if rate is None:
|
|
rate = [1, 1]
|
|
|
|
# The layout of the ops in the graph are expected to be as follows:
|
|
# depthwise_conv2d // Conv2D op corresponding to native deptwise conv.
|
|
# separable_conv2d // Conv2D op corresponding to the pointwise conv.
|
|
|
|
def op(input_converted, _, padding):
|
|
return nn_ops.depthwise_conv2d_native(
|
|
input=input_converted,
|
|
filter=depthwise_filter,
|
|
strides=strides,
|
|
padding=padding,
|
|
data_format=data_format,
|
|
name="depthwise")
|
|
|
|
depthwise = nn_ops.with_space_to_batch(
|
|
input=input,
|
|
filter_shape=array_ops.shape(depthwise_filter),
|
|
dilation_rate=rate,
|
|
padding=padding,
|
|
data_format=data_format,
|
|
op=op)
|
|
|
|
return nn_ops.conv2d(
|
|
depthwise,
|
|
pointwise_filter, [1, 1, 1, 1],
|
|
padding="VALID",
|
|
data_format=data_format,
|
|
name=name)
|
|
|
|
|
|
# pylint: enable=redefined-builtin,line-too-long
|
|
|
|
|
|
@tf_export("nn.sufficient_statistics")
|
|
def sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None):
|
|
"""Calculate the sufficient statistics for the mean and variance of `x`.
|
|
|
|
These sufficient statistics are computed using the one pass algorithm on
|
|
an input that's optionally shifted. See:
|
|
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data
|
|
|
|
Args:
|
|
x: A `Tensor`.
|
|
axes: Array of ints. Axes along which to compute mean and variance.
|
|
shift: A `Tensor` containing the value by which to shift the data for
|
|
numerical stability, or `None` if no shift is to be performed. A shift
|
|
close to the true mean provides the most numerically stable results.
|
|
keep_dims: produce statistics with the same dimensionality as the input.
|
|
name: Name used to scope the operations that compute the sufficient stats.
|
|
|
|
Returns:
|
|
Four `Tensor` objects of the same type as `x`:
|
|
|
|
* the count (number of elements to average over).
|
|
* the (possibly shifted) sum of the elements in the array.
|
|
* the (possibly shifted) sum of squares of the elements in the array.
|
|
* the shift by which the mean must be corrected or None if `shift` is None.
|
|
"""
|
|
axes = list(set(axes))
|
|
with ops.name_scope(name, "sufficient_statistics", [x, shift]):
|
|
x = ops.convert_to_tensor(x, name="x")
|
|
x_shape = x.get_shape()
|
|
if all(x_shape[d].value is not None for d in axes):
|
|
counts = 1
|
|
for d in axes:
|
|
counts *= x_shape[d].value
|
|
counts = constant_op.constant(counts, dtype=x.dtype)
|
|
else: # shape needs to be inferred at runtime.
|
|
x_dims = array_ops.gather(
|
|
math_ops.cast(array_ops.shape(x), x.dtype), axes)
|
|
counts = math_ops.reduce_prod(x_dims, name="count")
|
|
if shift is not None:
|
|
shift = ops.convert_to_tensor(shift, name="shift")
|
|
m_ss = math_ops.subtract(x, shift)
|
|
v_ss = math_ops.squared_difference(x, shift)
|
|
else: # no shift.
|
|
m_ss = x
|
|
v_ss = math_ops.square(x)
|
|
m_ss = math_ops.reduce_sum(m_ss, axes, keepdims=keep_dims, name="mean_ss")
|
|
v_ss = math_ops.reduce_sum(v_ss, axes, keepdims=keep_dims, name="var_ss")
|
|
return counts, m_ss, v_ss, shift
|
|
|
|
|
|
@tf_export("nn.normalize_moments")
|
|
def normalize_moments(counts, mean_ss, variance_ss, shift, name=None):
|
|
"""Calculate the mean and variance of based on the sufficient statistics.
|
|
|
|
Args:
|
|
counts: A `Tensor` containing the total count of the data (one value).
|
|
mean_ss: A `Tensor` containing the mean sufficient statistics: the (possibly
|
|
shifted) sum of the elements to average over.
|
|
variance_ss: A `Tensor` containing the variance sufficient statistics: the
|
|
(possibly shifted) squared sum of the data to compute the variance over.
|
|
shift: A `Tensor` containing the value by which the data is shifted for
|
|
numerical stability, or `None` if no shift was performed.
|
|
name: Name used to scope the operations that compute the moments.
|
|
|
|
Returns:
|
|
Two `Tensor` objects: `mean` and `variance`.
|
|
"""
|
|
with ops.name_scope(name, "normalize", [counts, mean_ss, variance_ss, shift]):
|
|
divisor = math_ops.reciprocal(counts, name="divisor")
|
|
if shift is not None:
|
|
shifted_mean = math_ops.multiply(mean_ss, divisor, name="shifted_mean")
|
|
mean = math_ops.add(shifted_mean, shift, name="mean")
|
|
else: # no shift.
|
|
shifted_mean = math_ops.multiply(mean_ss, divisor, name="mean")
|
|
mean = shifted_mean
|
|
variance = math_ops.subtract(
|
|
math_ops.multiply(variance_ss, divisor),
|
|
math_ops.square(shifted_mean),
|
|
name="variance")
|
|
return (mean, variance)
|
|
|
|
|
|
@tf_export("nn.moments")
|
|
def moments(
|
|
x,
|
|
axes,
|
|
shift=None, # pylint: disable=unused-argument
|
|
name=None,
|
|
keep_dims=False):
|
|
"""Calculate the mean and variance of `x`.
|
|
|
|
The mean and variance are calculated by aggregating the contents of `x`
|
|
across `axes`. If `x` is 1-D and `axes = [0]` this is just the mean
|
|
and variance of a vector.
|
|
|
|
Note: shift is currently not used; the true mean is computed and used.
|
|
|
|
When using these moments for batch normalization (see
|
|
`tf.nn.batch_normalization`):
|
|
|
|
* for so-called "global normalization", used with convolutional filters with
|
|
shape `[batch, height, width, depth]`, pass `axes=[0, 1, 2]`.
|
|
* for simple batch normalization pass `axes=[0]` (batch only).
|
|
|
|
Args:
|
|
x: A `Tensor`.
|
|
axes: Array of ints. Axes along which to compute mean and
|
|
variance.
|
|
shift: Not used in the current implementation
|
|
name: Name used to scope the operations that compute the moments.
|
|
keep_dims: produce moments with the same dimensionality as the input.
|
|
|
|
Returns:
|
|
Two `Tensor` objects: `mean` and `variance`.
|
|
"""
|
|
with ops.name_scope(name, "moments", [x, axes]):
|
|
# The dynamic range of fp16 is too limited to support the collection of
|
|
# sufficient statistics. As a workaround we simply perform the operations
|
|
# on 32-bit floats before converting the mean and variance back to fp16
|
|
y = math_ops.cast(x, dtypes.float32) if x.dtype == dtypes.float16 else x
|
|
# Compute true mean while keeping the dims for proper broadcasting.
|
|
mean = math_ops.reduce_mean(y, axes, keepdims=True, name="mean")
|
|
# sample variance, not unbiased variance
|
|
# Note: stop_gradient does not change the gradient that gets
|
|
# backpropagated to the mean from the variance calculation,
|
|
# because that gradient is zero
|
|
variance = math_ops.reduce_mean(
|
|
math_ops.squared_difference(y, array_ops.stop_gradient(mean)),
|
|
axes,
|
|
keepdims=True,
|
|
name="variance")
|
|
if not keep_dims:
|
|
mean = array_ops.squeeze(mean, axes)
|
|
variance = array_ops.squeeze(variance, axes)
|
|
if x.dtype == dtypes.float16:
|
|
return (math_ops.cast(mean, dtypes.float16),
|
|
math_ops.cast(variance, dtypes.float16))
|
|
else:
|
|
return (mean, variance)
|
|
|
|
|
|
@tf_export("nn.weighted_moments")
|
|
def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
|
|
"""Returns the frequency-weighted mean and variance of `x`.
|
|
|
|
Args:
|
|
x: A tensor.
|
|
axes: 1-d tensor of int32 values; these are the axes along which
|
|
to compute mean and variance.
|
|
frequency_weights: A tensor of positive weights which can be
|
|
broadcast with x.
|
|
name: Name used to scope the operation.
|
|
keep_dims: Produce moments with the same dimensionality as the input.
|
|
|
|
Returns:
|
|
Two tensors: `weighted_mean` and `weighted_variance`.
|
|
"""
|
|
with ops.name_scope(name, "weighted_moments", [x, frequency_weights, axes]):
|
|
x = ops.convert_to_tensor(x, name="x")
|
|
frequency_weights = ops.convert_to_tensor(
|
|
frequency_weights, name="frequency_weights")
|
|
|
|
# Unlike moments(), this just uses a simpler two-pass method.
|
|
|
|
# See comment in moments() WRT precision; it applies here too.
|
|
needs_cast = x.dtype == dtypes.float16
|
|
if needs_cast:
|
|
x = math_ops.cast(x, dtypes.float32)
|
|
|
|
if frequency_weights.dtype != x.dtype:
|
|
frequency_weights = math_ops.cast(frequency_weights, x.dtype)
|
|
|
|
# Note that we use keep_dims=True for our reductions regardless of the arg;
|
|
# this is so that the results remain broadcast-compatible with the inputs.
|
|
weighted_input_sum = math_ops.reduce_sum(
|
|
frequency_weights * x, axes, name="weighted_input_sum", keepdims=True)
|
|
|
|
# The shape of the weights isn't necessarily the same as x's
|
|
# shape, just broadcast-compatible with it -- so this expression
|
|
# performs broadcasting to give a per-item weight, with the same
|
|
# shape as (freqency_weights * x). This avoids having to reason
|
|
# through all the broadcast logic to compute a correct
|
|
# sum_of_weights.
|
|
broadcasted_weights = frequency_weights + array_ops.zeros_like(x)
|
|
|
|
sum_of_weights = math_ops.reduce_sum(
|
|
broadcasted_weights, axes, name="sum_of_weights", keepdims=True)
|
|
|
|
divisor = math_ops.reciprocal(sum_of_weights, name="inv_weight_sum")
|
|
|
|
weighted_mean = math_ops.multiply(weighted_input_sum, divisor)
|
|
|
|
# Have the weighted mean; now on to variance:
|
|
weighted_distsq = math_ops.reduce_sum(
|
|
frequency_weights * math_ops.squared_difference(x, weighted_mean),
|
|
axes,
|
|
name="weighted_distsq",
|
|
keepdims=True)
|
|
|
|
weighted_variance = math_ops.multiply(weighted_distsq, divisor)
|
|
|
|
if not keep_dims:
|
|
weighted_mean = array_ops.squeeze(weighted_mean, axis=axes)
|
|
weighted_variance = array_ops.squeeze(
|
|
weighted_variance, axis=axes)
|
|
|
|
if needs_cast:
|
|
weighted_mean = math_ops.cast(weighted_mean, dtypes.float16)
|
|
weighted_variance = math_ops.cast(weighted_variance, dtypes.float16)
|
|
|
|
return weighted_mean, weighted_variance
|
|
|
|
|
|
@tf_export("nn.batch_normalization")
|
|
def batch_normalization(x,
|
|
mean,
|
|
variance,
|
|
offset,
|
|
scale,
|
|
variance_epsilon,
|
|
name=None):
|
|
r"""Batch normalization.
|
|
|
|
As described in http://arxiv.org/abs/1502.03167.
|
|
Normalizes a tensor by `mean` and `variance`, and applies (optionally) a
|
|
`scale` \\(\gamma\\) to it, as well as an `offset` \\(\beta\\):
|
|
|
|
\\(\frac{\gamma(x-\mu)}{\sigma}+\beta\\)
|
|
|
|
`mean`, `variance`, `offset` and `scale` are all expected to be of one of two
|
|
shapes:
|
|
|
|
* In all generality, they can have the same number of dimensions as the
|
|
input `x`, with identical sizes as `x` for the dimensions that are not
|
|
normalized over (the 'depth' dimension(s)), and dimension 1 for the
|
|
others which are being normalized over.
|
|
`mean` and `variance` in this case would typically be the outputs of
|
|
`tf.nn.moments(..., keep_dims=True)` during training, or running averages
|
|
thereof during inference.
|
|
* In the common case where the 'depth' dimension is the last dimension in
|
|
the input tensor `x`, they may be one dimensional tensors of the same
|
|
size as the 'depth' dimension.
|
|
This is the case for example for the common `[batch, depth]` layout of
|
|
fully-connected layers, and `[batch, height, width, depth]` for
|
|
convolutions.
|
|
`mean` and `variance` in this case would typically be the outputs of
|
|
`tf.nn.moments(..., keep_dims=False)` during training, or running averages
|
|
thereof during inference.
|
|
|
|
Args:
|
|
x: Input `Tensor` of arbitrary dimensionality.
|
|
mean: A mean `Tensor`.
|
|
variance: A variance `Tensor`.
|
|
offset: An offset `Tensor`, often denoted \\(\beta\\) in equations, or
|
|
None. If present, will be added to the normalized tensor.
|
|
scale: A scale `Tensor`, often denoted \\(\gamma\\) in equations, or
|
|
`None`. If present, the scale is applied to the normalized tensor.
|
|
variance_epsilon: A small float number to avoid dividing by 0.
|
|
name: A name for this operation (optional).
|
|
|
|
Returns:
|
|
the normalized, scaled, offset tensor.
|
|
"""
|
|
with ops.name_scope(name, "batchnorm", [x, mean, variance, scale, offset]):
|
|
inv = math_ops.rsqrt(variance + variance_epsilon)
|
|
if scale is not None:
|
|
inv *= scale
|
|
# Note: tensorflow/contrib/quantize/python/fold_batch_norms.py depends on
|
|
# the precise order of ops that are generated by the expression below.
|
|
return x * math_ops.cast(inv, x.dtype) + math_ops.cast(
|
|
offset - mean * inv if offset is not None else -mean * inv, x.dtype)
|
|
|
|
|
|
@tf_export("nn.fused_batch_norm")
|
|
def fused_batch_norm(
|
|
x,
|
|
scale,
|
|
offset, # pylint: disable=invalid-name
|
|
mean=None,
|
|
variance=None,
|
|
epsilon=0.001,
|
|
data_format="NHWC",
|
|
is_training=True,
|
|
name=None):
|
|
r"""Batch normalization.
|
|
|
|
As described in http://arxiv.org/abs/1502.03167.
|
|
|
|
Args:
|
|
x: Input `Tensor` of 4 dimensions.
|
|
scale: A `Tensor` of 1 dimension for scaling.
|
|
offset: A `Tensor` of 1 dimension for bias.
|
|
mean: A `Tensor` of 1 dimension for population mean used for inference.
|
|
variance: A `Tensor` of 1 dimension for population variance
|
|
used for inference.
|
|
epsilon: A small float number added to the variance of x.
|
|
data_format: The data format for x. Either "NHWC" (default) or "NCHW".
|
|
is_training: A bool value to specify if the operation is used for
|
|
training or inference.
|
|
name: A name for this operation (optional).
|
|
|
|
Returns:
|
|
y: A 4D Tensor for the normalized, scaled, offsetted x.
|
|
batch_mean: A 1D Tensor for the mean of x.
|
|
batch_var: A 1D Tensor for the variance of x.
|
|
|
|
Raises:
|
|
ValueError: If mean or variance is not None when is_training is True.
|
|
"""
|
|
x = ops.convert_to_tensor(x, name="input")
|
|
scale = ops.convert_to_tensor(scale, name="scale")
|
|
offset = ops.convert_to_tensor(offset, name="offset")
|
|
if is_training:
|
|
if (mean is not None) or (variance is not None):
|
|
raise ValueError("Both 'mean' and 'variance' must be None "
|
|
"if is_training is True.")
|
|
if mean is None:
|
|
mean = constant_op.constant([])
|
|
if variance is None:
|
|
variance = constant_op.constant([])
|
|
# Set a minimum epsilon to 1.001e-5, which is a requirement by CUDNN to
|
|
# prevent exception (see cudnn.h).
|
|
min_epsilon = 1.001e-5
|
|
epsilon = epsilon if epsilon > min_epsilon else min_epsilon
|
|
# TODO(reedwm): In a few weeks, switch to using the V2 version exclusively. We
|
|
# currently only use the V2 version for float16 inputs, which is not supported
|
|
# by the V1 version.
|
|
if x.dtype == dtypes.float16 or x.dtype == dtypes.bfloat16:
|
|
fused_batch_norm_func = gen_nn_ops.fused_batch_norm_v2
|
|
else:
|
|
fused_batch_norm_func = gen_nn_ops._fused_batch_norm # pylint: disable=protected-access
|
|
y, batch_mean, batch_var, _, _ = fused_batch_norm_func(
|
|
x,
|
|
scale,
|
|
offset,
|
|
mean,
|
|
variance,
|
|
epsilon=epsilon,
|
|
data_format=data_format,
|
|
is_training=is_training,
|
|
name=name)
|
|
return y, batch_mean, batch_var
|
|
|
|
|
|
@tf_export("nn.batch_norm_with_global_normalization")
|
|
def batch_norm_with_global_normalization(t,
|
|
m,
|
|
v,
|
|
beta,
|
|
gamma,
|
|
variance_epsilon,
|
|
scale_after_normalization,
|
|
name=None):
|
|
"""Batch normalization.
|
|
|
|
This op is deprecated. See `tf.nn.batch_normalization`.
|
|
|
|
Args:
|
|
t: A 4D input Tensor.
|
|
m: A 1D mean Tensor with size matching the last dimension of t.
|
|
This is the first output from tf.nn.moments,
|
|
or a saved moving average thereof.
|
|
v: A 1D variance Tensor with size matching the last dimension of t.
|
|
This is the second output from tf.nn.moments,
|
|
or a saved moving average thereof.
|
|
beta: A 1D beta Tensor with size matching the last dimension of t.
|
|
An offset to be added to the normalized tensor.
|
|
gamma: A 1D gamma Tensor with size matching the last dimension of t.
|
|
If "scale_after_normalization" is true, this tensor will be multiplied
|
|
with the normalized tensor.
|
|
variance_epsilon: A small float number to avoid dividing by 0.
|
|
scale_after_normalization: A bool indicating whether the resulted tensor
|
|
needs to be multiplied with gamma.
|
|
name: A name for this operation (optional).
|
|
|
|
Returns:
|
|
A batch-normalized `t`.
|
|
"""
|
|
return batch_normalization(t, m, v, beta, gamma if scale_after_normalization
|
|
else None, variance_epsilon, name)
|
|
|
|
|
|
def _sum_rows(x):
|
|
"""Returns a vector summing up each row of the matrix x."""
|
|
# _sum_rows(x) is equivalent to math_ops.reduce_sum(x, 1) when x is
|
|
# a matrix. The gradient of _sum_rows(x) is more efficient than
|
|
# reduce_sum(x, 1)'s gradient in today's implementation. Therefore,
|
|
# we use _sum_rows(x) in the nce_loss() computation since the loss
|
|
# is mostly used for training.
|
|
cols = array_ops.shape(x)[1]
|
|
ones_shape = array_ops.stack([cols, 1])
|
|
ones = array_ops.ones(ones_shape, x.dtype)
|
|
return array_ops.reshape(math_ops.matmul(x, ones), [-1])
|
|
|
|
|
|
def _compute_sampled_logits(weights,
|
|
biases,
|
|
labels,
|
|
inputs,
|
|
num_sampled,
|
|
num_classes,
|
|
num_true=1,
|
|
sampled_values=None,
|
|
subtract_log_q=True,
|
|
remove_accidental_hits=False,
|
|
partition_strategy="mod",
|
|
name=None,
|
|
seed=None):
|
|
"""Helper function for nce_loss and sampled_softmax_loss functions.
|
|
|
|
Computes sampled output training logits and labels suitable for implementing
|
|
e.g. noise-contrastive estimation (see nce_loss) or sampled softmax (see
|
|
sampled_softmax_loss).
|
|
|
|
Note: In the case where num_true > 1, we assign to each target class
|
|
the target probability 1 / num_true so that the target probabilities
|
|
sum to 1 per-example.
|
|
|
|
Args:
|
|
weights: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
|
|
objects whose concatenation along dimension 0 has shape
|
|
`[num_classes, dim]`. The (possibly-partitioned) class embeddings.
|
|
biases: A `Tensor` of shape `[num_classes]`. The (possibly-partitioned)
|
|
class biases.
|
|
labels: A `Tensor` of type `int64` and shape `[batch_size,
|
|
num_true]`. The target classes. Note that this format differs from
|
|
the `labels` argument of `nn.softmax_cross_entropy_with_logits_v2`.
|
|
inputs: A `Tensor` of shape `[batch_size, dim]`. The forward
|
|
activations of the input network.
|
|
num_sampled: An `int`. The number of classes to randomly sample per batch.
|
|
num_classes: An `int`. The number of possible classes.
|
|
num_true: An `int`. The number of target classes per training example.
|
|
sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`,
|
|
`sampled_expected_count`) returned by a `*_candidate_sampler` function.
|
|
(if None, we default to `log_uniform_candidate_sampler`)
|
|
subtract_log_q: A `bool`. whether to subtract the log expected count of
|
|
the labels in the sample to get the logits of the true labels.
|
|
Default is True. Turn off for Negative Sampling.
|
|
remove_accidental_hits: A `bool`. whether to remove "accidental hits"
|
|
where a sampled class equals one of the target classes. Default is
|
|
False.
|
|
partition_strategy: A string specifying the partitioning strategy, relevant
|
|
if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
|
|
Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
|
|
name: A name for the operation (optional).
|
|
seed: random seed for candidate sampling. Default to None, which doesn't set
|
|
the op-level random seed for candidate sampling.
|
|
Returns:
|
|
out_logits: `Tensor` object with shape
|
|
`[batch_size, num_true + num_sampled]`, for passing to either
|
|
`nn.sigmoid_cross_entropy_with_logits` (NCE) or
|
|
`nn.softmax_cross_entropy_with_logits_v2` (sampled softmax).
|
|
out_labels: A Tensor object with the same shape as `out_logits`.
|
|
"""
|
|
|
|
if isinstance(weights, variables.PartitionedVariable):
|
|
weights = list(weights)
|
|
if not isinstance(weights, list):
|
|
weights = [weights]
|
|
|
|
with ops.name_scope(name, "compute_sampled_logits",
|
|
weights + [biases, inputs, labels]):
|
|
if labels.dtype != dtypes.int64:
|
|
labels = math_ops.cast(labels, dtypes.int64)
|
|
labels_flat = array_ops.reshape(labels, [-1])
|
|
|
|
# Sample the negative labels.
|
|
# sampled shape: [num_sampled] tensor
|
|
# true_expected_count shape = [batch_size, 1] tensor
|
|
# sampled_expected_count shape = [num_sampled] tensor
|
|
if sampled_values is None:
|
|
sampled_values = candidate_sampling_ops.log_uniform_candidate_sampler(
|
|
true_classes=labels,
|
|
num_true=num_true,
|
|
num_sampled=num_sampled,
|
|
unique=True,
|
|
range_max=num_classes,
|
|
seed=seed)
|
|
# NOTE: pylint cannot tell that 'sampled_values' is a sequence
|
|
# pylint: disable=unpacking-non-sequence
|
|
sampled, true_expected_count, sampled_expected_count = (
|
|
array_ops.stop_gradient(s) for s in sampled_values)
|
|
# pylint: enable=unpacking-non-sequence
|
|
sampled = math_ops.cast(sampled, dtypes.int64)
|
|
|
|
# labels_flat is a [batch_size * num_true] tensor
|
|
# sampled is a [num_sampled] int tensor
|
|
all_ids = array_ops.concat([labels_flat, sampled], 0)
|
|
|
|
# Retrieve the true weights and the logits of the sampled weights.
|
|
|
|
# weights shape is [num_classes, dim]
|
|
all_w = embedding_ops.embedding_lookup(
|
|
weights, all_ids, partition_strategy=partition_strategy)
|
|
|
|
# true_w shape is [batch_size * num_true, dim]
|
|
true_w = array_ops.slice(all_w, [0, 0],
|
|
array_ops.stack(
|
|
[array_ops.shape(labels_flat)[0], -1]))
|
|
|
|
sampled_w = array_ops.slice(
|
|
all_w, array_ops.stack([array_ops.shape(labels_flat)[0], 0]), [-1, -1])
|
|
# inputs has shape [batch_size, dim]
|
|
# sampled_w has shape [num_sampled, dim]
|
|
# Apply X*W', which yields [batch_size, num_sampled]
|
|
sampled_logits = math_ops.matmul(inputs, sampled_w, transpose_b=True)
|
|
|
|
# Retrieve the true and sampled biases, compute the true logits, and
|
|
# add the biases to the true and sampled logits.
|
|
all_b = embedding_ops.embedding_lookup(
|
|
biases, all_ids, partition_strategy=partition_strategy)
|
|
# true_b is a [batch_size * num_true] tensor
|
|
# sampled_b is a [num_sampled] float tensor
|
|
true_b = array_ops.slice(all_b, [0], array_ops.shape(labels_flat))
|
|
sampled_b = array_ops.slice(all_b, array_ops.shape(labels_flat), [-1])
|
|
|
|
# inputs shape is [batch_size, dim]
|
|
# true_w shape is [batch_size * num_true, dim]
|
|
# row_wise_dots is [batch_size, num_true, dim]
|
|
dim = array_ops.shape(true_w)[1:2]
|
|
new_true_w_shape = array_ops.concat([[-1, num_true], dim], 0)
|
|
row_wise_dots = math_ops.multiply(
|
|
array_ops.expand_dims(inputs, 1),
|
|
array_ops.reshape(true_w, new_true_w_shape))
|
|
# We want the row-wise dot plus biases which yields a
|
|
# [batch_size, num_true] tensor of true_logits.
|
|
dots_as_matrix = array_ops.reshape(row_wise_dots,
|
|
array_ops.concat([[-1], dim], 0))
|
|
true_logits = array_ops.reshape(_sum_rows(dots_as_matrix), [-1, num_true])
|
|
true_b = array_ops.reshape(true_b, [-1, num_true])
|
|
true_logits += true_b
|
|
sampled_logits += sampled_b
|
|
|
|
if remove_accidental_hits:
|
|
acc_hits = candidate_sampling_ops.compute_accidental_hits(
|
|
labels, sampled, num_true=num_true)
|
|
acc_indices, acc_ids, acc_weights = acc_hits
|
|
|
|
# This is how SparseToDense expects the indices.
|
|
acc_indices_2d = array_ops.reshape(acc_indices, [-1, 1])
|
|
acc_ids_2d_int32 = array_ops.reshape(
|
|
math_ops.cast(acc_ids, dtypes.int32), [-1, 1])
|
|
sparse_indices = array_ops.concat([acc_indices_2d, acc_ids_2d_int32], 1,
|
|
"sparse_indices")
|
|
# Create sampled_logits_shape = [batch_size, num_sampled]
|
|
sampled_logits_shape = array_ops.concat(
|
|
[array_ops.shape(labels)[:1],
|
|
array_ops.expand_dims(num_sampled, 0)], 0)
|
|
if sampled_logits.dtype != acc_weights.dtype:
|
|
acc_weights = math_ops.cast(acc_weights, sampled_logits.dtype)
|
|
sampled_logits += sparse_ops.sparse_to_dense(
|
|
sparse_indices,
|
|
sampled_logits_shape,
|
|
acc_weights,
|
|
default_value=0.0,
|
|
validate_indices=False)
|
|
|
|
if subtract_log_q:
|
|
# Subtract log of Q(l), prior probability that l appears in sampled.
|
|
true_logits -= math_ops.log(true_expected_count)
|
|
sampled_logits -= math_ops.log(sampled_expected_count)
|
|
|
|
# Construct output logits and labels. The true labels/logits start at col 0.
|
|
out_logits = array_ops.concat([true_logits, sampled_logits], 1)
|
|
|
|
# true_logits is a float tensor, ones_like(true_logits) is a float
|
|
# tensor of ones. We then divide by num_true to ensure the per-example
|
|
# labels sum to 1.0, i.e. form a proper probability distribution.
|
|
out_labels = array_ops.concat([
|
|
array_ops.ones_like(true_logits) / num_true,
|
|
array_ops.zeros_like(sampled_logits)
|
|
], 1)
|
|
|
|
return out_logits, out_labels
|
|
|
|
|
|
@tf_export("nn.nce_loss")
|
|
def nce_loss(weights,
|
|
biases,
|
|
labels,
|
|
inputs,
|
|
num_sampled,
|
|
num_classes,
|
|
num_true=1,
|
|
sampled_values=None,
|
|
remove_accidental_hits=False,
|
|
partition_strategy="mod",
|
|
name="nce_loss"):
|
|
"""Computes and returns the noise-contrastive estimation training loss.
|
|
|
|
See [Noise-contrastive estimation: A new estimation principle for
|
|
unnormalized statistical
|
|
models](http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).
|
|
Also see our [Candidate Sampling Algorithms
|
|
Reference](https://www.tensorflow.org/extras/candidate_sampling.pdf)
|
|
|
|
A common use case is to use this method for training, and calculate the full
|
|
sigmoid loss for evaluation or inference. In this case, you must set
|
|
`partition_strategy="div"` for the two losses to be consistent, as in the
|
|
following example:
|
|
|
|
```python
|
|
if mode == "train":
|
|
loss = tf.nn.nce_loss(
|
|
weights=weights,
|
|
biases=biases,
|
|
labels=labels,
|
|
inputs=inputs,
|
|
...,
|
|
partition_strategy="div")
|
|
elif mode == "eval":
|
|
logits = tf.matmul(inputs, tf.transpose(weights))
|
|
logits = tf.nn.bias_add(logits, biases)
|
|
labels_one_hot = tf.one_hot(labels, n_classes)
|
|
loss = tf.nn.sigmoid_cross_entropy_with_logits(
|
|
labels=labels_one_hot,
|
|
logits=logits)
|
|
loss = tf.reduce_sum(loss, axis=1)
|
|
```
|
|
|
|
Note: By default this uses a log-uniform (Zipfian) distribution for sampling,
|
|
so your labels must be sorted in order of decreasing frequency to achieve
|
|
good results. For more details, see
|
|
@{tf.nn.log_uniform_candidate_sampler}.
|
|
|
|
Note: In the case where `num_true` > 1, we assign to each target class
|
|
the target probability 1 / `num_true` so that the target probabilities
|
|
sum to 1 per-example.
|
|
|
|
Note: It would be useful to allow a variable number of target classes per
|
|
example. We hope to provide this functionality in a future release.
|
|
For now, if you have a variable number of target classes, you can pad them
|
|
out to a constant number by either repeating them or by padding
|
|
with an otherwise unused class.
|
|
|
|
Args:
|
|
weights: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
|
|
objects whose concatenation along dimension 0 has shape
|
|
[num_classes, dim]. The (possibly-partitioned) class embeddings.
|
|
biases: A `Tensor` of shape `[num_classes]`. The class biases.
|
|
labels: A `Tensor` of type `int64` and shape `[batch_size,
|
|
num_true]`. The target classes.
|
|
inputs: A `Tensor` of shape `[batch_size, dim]`. The forward
|
|
activations of the input network.
|
|
num_sampled: An `int`. The number of classes to randomly sample per batch.
|
|
num_classes: An `int`. The number of possible classes.
|
|
num_true: An `int`. The number of target classes per training example.
|
|
sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`,
|
|
`sampled_expected_count`) returned by a `*_candidate_sampler` function.
|
|
(if None, we default to `log_uniform_candidate_sampler`)
|
|
remove_accidental_hits: A `bool`. Whether to remove "accidental hits"
|
|
where a sampled class equals one of the target classes. If set to
|
|
`True`, this is a "Sampled Logistic" loss instead of NCE, and we are
|
|
learning to generate log-odds instead of log probabilities. See
|
|
our [Candidate Sampling Algorithms Reference]
|
|
(https://www.tensorflow.org/extras/candidate_sampling.pdf).
|
|
Default is False.
|
|
partition_strategy: A string specifying the partitioning strategy, relevant
|
|
if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
|
|
Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
|
|
name: A name for the operation (optional).
|
|
|
|
Returns:
|
|
A `batch_size` 1-D tensor of per-example NCE losses.
|
|
"""
|
|
logits, labels = _compute_sampled_logits(
|
|
weights=weights,
|
|
biases=biases,
|
|
labels=labels,
|
|
inputs=inputs,
|
|
num_sampled=num_sampled,
|
|
num_classes=num_classes,
|
|
num_true=num_true,
|
|
sampled_values=sampled_values,
|
|
subtract_log_q=True,
|
|
remove_accidental_hits=remove_accidental_hits,
|
|
partition_strategy=partition_strategy,
|
|
name=name)
|
|
sampled_losses = sigmoid_cross_entropy_with_logits(
|
|
labels=labels, logits=logits, name="sampled_losses")
|
|
# sampled_losses is batch_size x {true_loss, sampled_losses...}
|
|
# We sum out true and sampled losses.
|
|
return _sum_rows(sampled_losses)
|
|
|
|
|
|
@tf_export("nn.sampled_softmax_loss")
|
|
def sampled_softmax_loss(weights,
|
|
biases,
|
|
labels,
|
|
inputs,
|
|
num_sampled,
|
|
num_classes,
|
|
num_true=1,
|
|
sampled_values=None,
|
|
remove_accidental_hits=True,
|
|
partition_strategy="mod",
|
|
name="sampled_softmax_loss",
|
|
seed=None):
|
|
"""Computes and returns the sampled softmax training loss.
|
|
|
|
This is a faster way to train a softmax classifier over a huge number of
|
|
classes.
|
|
|
|
This operation is for training only. It is generally an underestimate of
|
|
the full softmax loss.
|
|
|
|
A common use case is to use this method for training, and calculate the full
|
|
softmax loss for evaluation or inference. In this case, you must set
|
|
`partition_strategy="div"` for the two losses to be consistent, as in the
|
|
following example:
|
|
|
|
```python
|
|
if mode == "train":
|
|
loss = tf.nn.sampled_softmax_loss(
|
|
weights=weights,
|
|
biases=biases,
|
|
labels=labels,
|
|
inputs=inputs,
|
|
...,
|
|
partition_strategy="div")
|
|
elif mode == "eval":
|
|
logits = tf.matmul(inputs, tf.transpose(weights))
|
|
logits = tf.nn.bias_add(logits, biases)
|
|
labels_one_hot = tf.one_hot(labels, n_classes)
|
|
loss = tf.nn.softmax_cross_entropy_with_logits_v2(
|
|
labels=labels_one_hot,
|
|
logits=logits)
|
|
```
|
|
|
|
See our [Candidate Sampling Algorithms Reference]
|
|
(https://www.tensorflow.org/extras/candidate_sampling.pdf)
|
|
|
|
Also see Section 3 of [Jean et al., 2014](http://arxiv.org/abs/1412.2007)
|
|
([pdf](http://arxiv.org/pdf/1412.2007.pdf)) for the math.
|
|
|
|
Args:
|
|
weights: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
|
|
objects whose concatenation along dimension 0 has shape
|
|
[num_classes, dim]. The (possibly-sharded) class embeddings.
|
|
biases: A `Tensor` of shape `[num_classes]`. The class biases.
|
|
labels: A `Tensor` of type `int64` and shape `[batch_size,
|
|
num_true]`. The target classes. Note that this format differs from
|
|
the `labels` argument of `nn.softmax_cross_entropy_with_logits_v2`.
|
|
inputs: A `Tensor` of shape `[batch_size, dim]`. The forward
|
|
activations of the input network.
|
|
num_sampled: An `int`. The number of classes to randomly sample per batch.
|
|
num_classes: An `int`. The number of possible classes.
|
|
num_true: An `int`. The number of target classes per training example.
|
|
sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`,
|
|
`sampled_expected_count`) returned by a `*_candidate_sampler` function.
|
|
(if None, we default to `log_uniform_candidate_sampler`)
|
|
remove_accidental_hits: A `bool`. whether to remove "accidental hits"
|
|
where a sampled class equals one of the target classes. Default is
|
|
True.
|
|
partition_strategy: A string specifying the partitioning strategy, relevant
|
|
if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
|
|
Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
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name: A name for the operation (optional).
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seed: random seed for candidate sampling. Default to None, which doesn't set
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|
the op-level random seed for candidate sampling.
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|
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Returns:
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A `batch_size` 1-D tensor of per-example sampled softmax losses.
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|
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"""
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logits, labels = _compute_sampled_logits(
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weights=weights,
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|
biases=biases,
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|
labels=labels,
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|
inputs=inputs,
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|
num_sampled=num_sampled,
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|
num_classes=num_classes,
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|
num_true=num_true,
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|
sampled_values=sampled_values,
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|
subtract_log_q=True,
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|
remove_accidental_hits=remove_accidental_hits,
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|
partition_strategy=partition_strategy,
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|
name=name,
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|
seed=seed)
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|
labels = array_ops.stop_gradient(labels, name="labels_stop_gradient")
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|
sampled_losses = nn_ops.softmax_cross_entropy_with_logits_v2(
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|
labels=labels, logits=logits)
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|
# sampled_losses is a [batch_size] tensor.
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|
return sampled_losses
|