#! /usr/bin/python
# -*- coding: utf-8 -*-
import numbers
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops, math_ops, nn_ops, standard_ops
from tensorlayerx import logging
__all__ = [
'softmax_cross_entropy_with_logits',
'sigmoid_cross_entropy',
'binary_cross_entropy',
'mean_squared_error',
'normalized_mean_square_error',
'absolute_difference_error',
'dice_coe',
'dice_hard_coe',
'iou_coe',
'cross_entropy_seq',
'cross_entropy_seq_with_mask',
'cosine_similarity',
'li_regularizer',
'lo_regularizer',
'maxnorm_regularizer',
'maxnorm_o_regularizer',
'maxnorm_i_regularizer',
'L1Loss'
]
[docs]def softmax_cross_entropy_with_logits(output, target, reduction='mean'):
"""Softmax cross-entropy operation, returns the TensorLayerX expression of cross-entropy for two distributions,
it implements softmax internally. See ``tf.ops.sparse_softmax_cross_entropy_with_logits``.
Parameters
----------
output : Tensor
A batch of distribution with shape: [batch_size, num of classes].
target : Tensor
A batch of index with shape: [batch_size, ].
reduction : str
The optional values are “mean”, “sum”, and “none”. If “none”, do not perform reduction.
Examples
--------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[4.0, 2.0, 1.0], [0.0, 5.0, 1.0]])
>>> labels = tlx.convert_to_tensor([[1], [2]])
>>> loss = tlx.losses.softmax_cross_entropy_with_logits(logits, labels)
References
-----------
- About cross-entropy: `<https://en.wikipedia.org/wiki/Cross_entropy>`__.
- The code is borrowed from: `<https://en.wikipedia.org/wiki/Cross_entropy>`__.
"""
if reduction == 'mean':
return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output))
elif reduction == 'sum':
return tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output))
elif reduction == 'none':
return tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output)
else:
raise Exception("The reduction values are 'mean', 'sum', and 'none'.")
[docs]def sigmoid_cross_entropy(output, target, reduction='mean'):
"""Sigmoid cross-entropy operation, see ``tf.ops.sigmoid_cross_entropy_with_logits``.
Parameters
----------
output : Tensor
A batch of distribution with shape: [batch_size, num of classes].
target : Tensor
same shape as the input.
reduction : str
The optional values are “mean”, “sum”, and “none”. If “none”, do not perform reduction.
Examples
--------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[4.0, 2.0, 1.0], [0.0, 5.0, 1.0]])
>>> labels = tlx.convert_to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
>>> losses = tlx.losses.sigmoid_cross_entropy(logits, labels)
"""
if reduction == 'mean':
return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output))
elif reduction == 'sum':
return tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output))
elif reduction == 'none':
return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
else:
raise Exception("The reduction values are 'mean', 'sum', and 'none'.")
[docs]def binary_cross_entropy(output, target, reduction='mean'):
"""Binary cross entropy operation.
Parameters
----------
output : Tensor
Tensor with type of `float32` or `float64`.
target : Tensor
The target distribution, format the same with `output`.
reduction : str
The optional values are “mean”, “sum”, and “none”. If “none”, do not perform reduction.
Examples
--------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[0.4, 0.2, 0.8], [1.1, 0.5, 0.3]])
>>> labels = tlx.convert_to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
>>> losses = tlx.losses.binary_cross_entropy(logits, labels)
References
-----------
- `ericjang-DRAW <https://github.com/ericjang/draw/blob/master/draw.py#L73>`__
"""
if False in tf.less_equal(output, [1.0]).numpy() or False in tf.greater_equal(output, [0.0]).numpy():
raise Exception("all elements of input should be between 0 and 1")
epsilon = 3.6e-44
cal_loss = -(target * tf.math.log(output + epsilon) + (1. - target) * tf.math.log(1. - output + epsilon))
if reduction == 'mean':
return tf.reduce_mean(cal_loss)
elif reduction == 'sum':
return tf.reduce_sum(cal_loss)
elif reduction == 'none':
return cal_loss
else:
raise Exception("The reduction values are 'mean', 'sum', and 'none'.")
[docs]def mean_squared_error(output, target, reduction='mean'):
"""Return the TensorLayerX expression of mean-square-error (L2) of two batch of data.
Parameters
----------
output : Tensor
2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, height, width] or [batch_size, height, width, channel].
target : Tensor
The target distribution, format the same with `output`.
reduction : str
The optional values are “mean”, “sum”, and “none”. If “none”, do not perform reduction.
Examples
--------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[0.4, 0.2, 0.8], [1.1, 0.5, 0.3]])
>>> labels = tlx.convert_to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
>>> losses = tlx.losses.mean_squared_error(logits, labels)
References
------------
- `Wiki Mean Squared Error <https://en.wikipedia.org/wiki/Mean_squared_error>`__
"""
if reduction == 'mean':
mse = tf.reduce_mean(tf.math.squared_difference(output, target))
elif reduction == 'sum':
mse = tf.reduce_sum(tf.math.squared_difference(output, target))
elif reduction == 'none':
mse = tf.math.squared_difference(output, target)
else:
raise Exception("The reduction values are 'mean', 'sum', and 'none'.")
return mse
[docs]def normalized_mean_square_error(output, target, reduction='mean'):
"""Return the TensorLayerX expression of normalized mean-square-error of two distributions.
Parameters
----------
output : Tensor
2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, height, width] or [batch_size, height, width, channel].
target : Tensor
The target distribution, format the same with `output`.
reduction : str
The optional values are “mean”, “sum”, and “none”. If “none”, do not perform reduction.
Examples
--------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[0.4, 0.2, 0.8], [1.1, 0.5, 0.3]])
>>> labels = tlx.convert_to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
>>> losses = tlx.losses.normalized_mean_square_error(logits, labels)
"""
nmse_a = tf.sqrt(tf.reduce_sum(tf.math.squared_difference(output, target), axis=-1))
nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=-1))
if reduction == 'mean':
nmse = tf.reduce_mean(nmse_a / nmse_b)
elif reduction == 'sum':
nmse = tf.reduce_sum(nmse_a / nmse_b)
elif reduction == 'none':
nmse = nmse_a / nmse_b
else:
raise Exception("The reduction values are 'mean', 'sum', and 'none'.")
return nmse
[docs]def absolute_difference_error(output, target, reduction='mean'):
"""Return the TensorLayerX expression of absolute difference error (L1) of two batch of data.
Parameters
----------
output : Tensor
2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, height, width] or [batch_size, height, width, channel].
target : Tensor
The target distribution, format the same with `output`.
reduction : str
The optional values are “mean”, “sum”, and “none”. If “none”, do not perform reduction.
Examples
--------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[0.4, 0.2, 0.8], [1.1, 0.5, 0.3]])
>>> labels = tlx.convert_to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
>>> losses = tlx.losses.absolute_difference_error(logits, labels)
"""
if reduction == 'mean':
loss = tf.reduce_mean(tf.abs(output - target))
elif reduction == 'sum':
loss = tf.reduce_sum(tf.abs(output - target))
elif reduction == 'none':
loss = tf.abs(output - target)
else:
raise Exception("The reduction values are 'mean', 'sum', and 'none'.")
return loss
[docs]def dice_coe(output, target, loss_type='jaccard', axis=(1, 2, 3), smooth=1e-5):
"""Soft dice (Sørensen or Jaccard) coefficient for comparing the similarity
of two batch of data, usually be used for binary image segmentation
i.e. labels are binary. The coefficient between 0 to 1, 1 means totally match.
Parameters
-----------
output : Tensor
A distribution with shape: [batch_size, ....], (any dimensions).
target : Tensor
The target distribution, format the same with `output`.
loss_type : str
``jaccard`` or ``sorensen``, default is ``jaccard``.
axis : tuple of int
All dimensions are reduced, default ``[1,2,3]``.
smooth : float
This small value will be added to the numerator and denominator.
- If both output and target are empty, it makes sure dice is 1.
- If either output or target are empty (all pixels are background), dice = ```smooth/(small_value + smooth)``, then if smooth is very small, dice close to 0 (even the image values lower than the threshold), so in this case, higher smooth can have a higher dice.
Examples
---------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[0.4, 0.2, 0.8], [1.1, 0.5, 0.3]])
>>> labels = tlx.convert_to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
>>> dice_loss = tlx.losses.dice_coe(logits, labels, axis=-1)
References
-----------
- `Wiki-Dice <https://en.wikipedia.org/wiki/Sørensen–Dice_coefficient>`__
"""
inse = tf.reduce_sum(output * target, axis=axis)
if loss_type == 'jaccard':
l = tf.reduce_sum(output * output, axis=axis)
r = tf.reduce_sum(target * target, axis=axis)
elif loss_type == 'sorensen':
l = tf.reduce_sum(output, axis=axis)
r = tf.reduce_sum(target, axis=axis)
else:
raise Exception("Unknow loss_type")
dice = (2. * inse + smooth) / (l + r + smooth)
dice = tf.reduce_mean(dice, name='dice_coe')
return dice
[docs]def dice_hard_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5):
"""Non-differentiable Sørensen–Dice coefficient for comparing the similarity
of two batch of data, usually be used for binary image segmentation i.e. labels are binary.
The coefficient between 0 to 1, 1 if totally match.
Parameters
-----------
output : tensor
A distribution with shape: [batch_size, ....], (any dimensions).
target : tensor
The target distribution, format the same with `output`.
threshold : float
The threshold value to be true.
axis : tuple of integer
All dimensions are reduced, default ``(1,2,3)``.
smooth : float
This small value will be added to the numerator and denominator, see ``dice_coe``.
Examples
---------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[0.4, 0.2, 0.8], [1.1, 0.5, 0.3]])
>>> labels = tlx.convert_to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
>>> dice_loss = tlx.losses.dice_hard_coe(logits, labels, axis=-1)
References
-----------
- `Wiki-Dice <https://en.wikipedia.org/wiki/Sørensen–Dice_coefficient>`__
"""
output = tf.cast(output > threshold, dtype=tf.float32)
target = tf.cast(target > threshold, dtype=tf.float32)
inse = tf.reduce_sum(tf.multiply(output, target), axis=axis)
l = tf.reduce_sum(output, axis=axis)
r = tf.reduce_sum(target, axis=axis)
hard_dice = (2. * inse + smooth) / (l + r + smooth)
hard_dice = tf.reduce_mean(hard_dice, name='hard_dice')
return hard_dice
[docs]def iou_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5):
"""Non-differentiable Intersection over Union (IoU) for comparing the
similarity of two batch of data, usually be used for evaluating binary image segmentation.
The coefficient between 0 to 1, and 1 means totally match.
Parameters
-----------
output : tensor
A batch of distribution with shape: [batch_size, ....], (any dimensions).
target : tensor
The target distribution, format the same with `output`.
threshold : float
The threshold value to be true.
axis : tuple of integer
All dimensions are reduced, default ``(1,2,3)``.
smooth : float
This small value will be added to the numerator and denominator, see ``dice_coe``.
Examples
---------
>>> import tensorlayerx as tlx
>>> logits = tlx.convert_to_tensor([[0.4, 0.2, 0.8], [1.1, 0.5, 0.3]])
>>> labels = tlx.convert_to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
>>> dice_loss = tlx.losses.iou_coe(logits, labels, axis=-1)
Notes
------
- IoU cannot be used as training loss, people usually use dice coefficient for training, IoU and hard-dice for evaluating.
"""
pre = tf.cast(output > threshold, dtype=tf.float32)
truth = tf.cast(target > threshold, dtype=tf.float32)
inse = tf.reduce_sum(tf.multiply(pre, truth), axis=axis) # AND
union = tf.reduce_sum(tf.cast(tf.add(pre, truth) >= 1, dtype=tf.float32), axis=axis) # OR
batch_iou = (inse + smooth) / (union + smooth)
iou = tf.reduce_mean(batch_iou, name='iou_coe')
return iou
def sequence_loss_by_example(
logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None
):
"""Weighted cross-entropy loss for a sequence of logits (per example). see original tensorflow code :
<https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py#L1057>
Parameters
----------
logits: List
List of 2D Tensors of shape [batch_size x num_decoder_symbols].
targets: List
List of 1D batch-sized int32 Tensors of the same length as logits.
weights: List
List of 1D batch-sized float-Tensors of the same length as logits.
average_across_timesteps: Boolean
If set, divide the returned losses by the total label weight.
softmax_loss_function: None or Function
Function (labels, logits) -> loss-batch to be used instead of the standard softmax (the default if this is None).
**Note that to avoid confusion, it is required for the function to accept named arguments.**
name: None or str
Optional name for this operation, default: "sequence_loss_by_example".
Returns
-------
1D batch-sized float Tensor: The log-perplexity for each sequence.
Raises
------
ValueError: If len(logits) is different from len(targets) or len(weights).
"""
if len(targets) != len(logits) or len(weights) != len(logits):
raise ValueError(
"Lengths of logits, weights, and targets must be the same "
"%d, %d, %d." % (len(logits), len(weights), len(targets))
)
with ops.name_scope(name, "sequence_loss_by_example", logits + targets + weights):
log_perp_list = []
for logit, target, weight in zip(logits, targets, weights):
if softmax_loss_function is None:
# TODO(irving,ebrevdo): This reshape is needed because
# sequence_loss_by_example is called with scalars sometimes, which
# violates our general scalar strictness policy.
target = array_ops.reshape(target, [-1])
crossent = nn_ops.sparse_softmax_cross_entropy_with_logits(labels=target, logits=logit)
else:
crossent = softmax_loss_function(labels=target, logits=logit)
log_perp_list.append(crossent * weight)
log_perps = math_ops.add_n(log_perp_list)
if average_across_timesteps:
total_size = math_ops.add_n(weights)
total_size += 1e-12 # Just to avoid division by 0 for all-0 weights.
log_perps /= total_size
return log_perps
[docs]def cross_entropy_seq(logits, target_seqs, batch_size=None):
"""Returns the expression of cross-entropy of two sequences, implement
softmax internally. Normally be used for fixed length RNN outputs, see `PTB example <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ptb_lstm.py>`__.
Parameters
----------
logits : Tensor
2D tensor with shape of `[batch_size * n_steps, n_classes]`.
target_seqs : Tensor
The target sequence, 2D tensor `[batch_size, n_steps]`, if the number of step is dynamic, please use ``tlx.losses.cross_entropy_seq_with_mask`` instead.
batch_size : None or int.
Whether to divide the losses by batch size.
- If integer, the return losses will be divided by `batch_size`.
- If None (default), the return losses will not be divided by anything.
Examples
--------
>>> import tensorlayerx as tlx
>>> # see `PTB example <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ptb_lstm.py>`__.for more details
>>> # outputs shape : (batch_size * n_steps, n_classes)
>>> # targets shape : (batch_size, n_steps)
>>> losses = tlx.losses.cross_entropy_seq(outputs, targets)
"""
sequence_loss_by_example_fn = sequence_loss_by_example
loss = sequence_loss_by_example_fn(
[logits], [tf.reshape(target_seqs, [-1])], [tf.ones_like(tf.reshape(target_seqs, [-1]), dtype=tf.float32)]
)
# [tf.ones([batch_size * num_steps])])
cost = tf.reduce_sum(loss) # / batch_size
if batch_size is not None:
cost = cost / batch_size
return cost
[docs]def cross_entropy_seq_with_mask(logits, target_seqs, input_mask, return_details=False, name=None):
"""Returns the expression of cross-entropy of two sequences, implement
softmax internally. Normally be used for Dynamic RNN with Synced sequence input and output.
Parameters
-----------
logits : Tensor
2D tensor with shape of [batch_size * ?, n_classes], `?` means dynamic IDs for each example.
- Can be get from `DynamicRNNLayer` by setting ``return_seq_2d`` to `True`.
target_seqs : Tensor
int of tensor, like word ID. [batch_size, ?], `?` means dynamic IDs for each example.
input_mask : Tensor
The mask to compute loss, it has the same size with `target_seqs`, normally 0 or 1.
return_details : boolean
Whether to return detailed losses.
- If False (default), only returns the loss.
- If True, returns the loss, losses, weights and targets (see source code).
Examples
--------
>>> import tensorlayerx as tlx
>>> import tensorflow as tf
>>> import numpy as np
>>> batch_size = 64
>>> vocab_size = 10000
>>> embedding_size = 256
>>> ni = tlx.nn.Input([batch_size, None], dtype=tf.int64)
>>> net_lits = []
>>> net_list.append(tlx.nn.Embedding(
... vocabulary_size = vocab_size,
... embedding_size = embedding_size,
... name = 'seq_embedding'))
>>> net_list.append(tlx.nn.RNN(
... cell =tf.keras.layers.LSTMCell(units=embedding_size, dropout=0.1),
... return_seq_2d = True,
... name = 'dynamicrnn'))
>>> net_list.append(tlx.nn.Dense(n_units=vocab_size, name="output"))
>>> model = tlx.nn.Sequential(net_list)
>>> input_seqs = np.random.randint(0, 10, size=(batch_size, 10), dtype=np.int64)
>>> target_seqs = np.random.randint(0, 10, size=(batch_size, 10), dtype=np.int64)
>>> input_mask = np.random.randint(0, 2, size=(batch_size, 10), dtype=np.int64)
>>> outputs = model(input_seqs)
>>> loss = tlx.losses.cross_entropy_seq_with_mask(outputs, target_seqs, input_mask)
"""
targets = tf.reshape(target_seqs, [-1]) # to one vector
weights = tf.cast(tf.reshape(input_mask, [-1]), dtype=tf.float32) # to one vector like targets
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets, name=name) * weights
# losses = tf.reduce_mean(tf.ops.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets, name=name)) # for TF1.0 and others
loss = tf.divide(
tf.reduce_sum(losses), # loss from mask. reduce_sum before element-wise mul with mask !!
tf.reduce_sum(weights),
name="seq_loss_with_mask"
)
if return_details:
return loss, losses, weights, targets
else:
return loss
[docs]def cosine_similarity(v1, v2):
"""Cosine similarity [-1, 1].
Parameters
----------
v1, v2 : Tensor
Tensor with the same shape [batch_size, n_feature].
References
----------
- `Wiki <https://en.wikipedia.org/wiki/Cosine_similarity>`__.
"""
return tf.reduce_sum(tf.multiply(v1, v2), 1) / \
(tf.sqrt(tf.reduce_sum(tf.multiply(v1, v1), 1)) *
tf.sqrt(tf.reduce_sum(tf.multiply(v2, v2), 1)))
# Regularization Functions
[docs]def li_regularizer(scale, scope=None):
"""Li regularization removes the neurons of previous layer. The `i` represents `inputs`.
Returns a function that can be used to apply group li regularization to weights.
The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.
Parameters
----------
scale : float
A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: str
An optional scope name for this function.
Returns
--------
A function with signature `li(weights, name=None)` that apply Li regularization.
Raises
------
ValueError : if scale is outside of the range [0.0, 1.0] or if scale is not a float.
"""
if isinstance(scale, numbers.Integral):
raise ValueError('scale cannot be an integer: %s' % scale)
if isinstance(scale, numbers.Real):
if scale < 0.:
raise ValueError('Setting a scale less than 0 on a regularizer: %g' % scale)
if scale >= 1.:
raise ValueError('Setting a scale greater than 1 on a regularizer: %g' % scale)
if scale == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _, name=None: None
def li(weights):
"""Applies li regularization to weights."""
with tf.name_scope('li_regularizer') as scope:
my_scale = ops.convert_to_tensor(scale, dtype=weights.dtype.base_dtype, name='scale')
# if tf.__version__ <= '0.12':
# standard_ops_fn = standard_ops.mul
# else:
standard_ops_fn = standard_ops.multiply
return standard_ops_fn(
my_scale, standard_ops.reduce_sum(standard_ops.sqrt(standard_ops.reduce_sum(tf.square(weights), 1))),
name=scope
)
return li
[docs]def lo_regularizer(scale):
"""Lo regularization removes the neurons of current layer. The `o` represents `outputs`
Returns a function that can be used to apply group lo regularization to weights.
The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.
Parameters
----------
scale : float
A scalar multiplier `Tensor`. 0.0 disables the regularizer.
Returns
-------
A function with signature `lo(weights, name=None)` that apply Lo regularization.
Raises
------
ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
"""
if isinstance(scale, numbers.Integral):
raise ValueError('scale cannot be an integer: %s' % scale)
if isinstance(scale, numbers.Real):
if scale < 0.:
raise ValueError('Setting a scale less than 0 on a regularizer: %g' % scale)
if scale >= 1.:
raise ValueError('Setting a scale greater than 1 on a regularizer: %g' % scale)
if scale == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _, name=None: None
def lo(weights, name='lo_regularizer'):
"""Applies group column regularization to weights."""
with tf.name_scope(name) as scope:
my_scale = ops.convert_to_tensor(scale, dtype=weights.dtype.base_dtype, name='scale')
# if tf.__version__ <= '0.12':
# standard_ops_fn = standard_ops.mul
# else:
standard_ops_fn = standard_ops.multiply
return standard_ops_fn(
my_scale, standard_ops.reduce_sum(standard_ops.sqrt(standard_ops.reduce_sum(tf.square(weights), 0))),
name=scope
)
return lo
def maxnorm_regularizer(scale=1.0):
"""Max-norm regularization returns a function that can be used to apply max-norm regularization to weights.
More about max-norm, see `wiki-max norm <https://en.wikipedia.org/wiki/Matrix_norm#Max_norm>`_.
The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.
Parameters
----------
scale : float
A scalar multiplier `Tensor`. 0.0 disables the regularizer.
Returns
---------
A function with signature `mn(weights, name=None)` that apply Lo regularization.
Raises
--------
ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
"""
if isinstance(scale, numbers.Integral):
raise ValueError('scale cannot be an integer: %s' % scale)
if isinstance(scale, numbers.Real):
if scale < 0.:
raise ValueError('Setting a scale less than 0 on a regularizer: %g' % scale)
# if scale >= 1.:
# raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
# scale)
if scale == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _, name=None: None
def mn(weights, name='max_regularizer'):
"""Applies max-norm regularization to weights."""
with tf.name_scope(name) as scope:
my_scale = ops.convert_to_tensor(scale, dtype=weights.dtype.base_dtype, name='scale')
# if tf.__version__ <= '0.12':
# standard_ops_fn = standard_ops.mul
# else:
standard_ops_fn = standard_ops.multiply
return standard_ops_fn(my_scale, standard_ops.reduce_max(standard_ops.abs(weights)), name=scope)
return mn
[docs]def maxnorm_o_regularizer(scale):
"""Max-norm output regularization removes the neurons of current layer.
Returns a function that can be used to apply max-norm regularization to each column of weight matrix.
The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.
Parameters
----------
scale : float
A scalar multiplier `Tensor`. 0.0 disables the regularizer.
Returns
---------
A function with signature `mn_o(weights, name=None)` that apply Lo regularization.
Raises
---------
ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
"""
if isinstance(scale, numbers.Integral):
raise ValueError('scale cannot be an integer: %s' % scale)
if isinstance(scale, numbers.Real):
if scale < 0.:
raise ValueError('Setting a scale less than 0 on a regularizer: %g' % scale)
# if scale >= 1.:
# raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
# scale)
if scale == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _, name=None: None
def mn_o(weights, name='maxnorm_o_regularizer'):
"""Applies max-norm regularization to weights."""
with tf.name_scope(name) as scope:
my_scale = ops.convert_to_tensor(scale, dtype=weights.dtype.base_dtype, name='scale')
if tf.__version__ <= '0.12':
standard_ops_fn = standard_ops.mul
else:
standard_ops_fn = standard_ops.multiply
return standard_ops_fn(
my_scale, standard_ops.reduce_sum(standard_ops.reduce_max(standard_ops.abs(weights), 0)), name=scope
)
return mn_o
[docs]def maxnorm_i_regularizer(scale):
"""Max-norm input regularization removes the neurons of previous layer.
Returns a function that can be used to apply max-norm regularization to each row of weight matrix.
The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.
Parameters
----------
scale : float
A scalar multiplier `Tensor`. 0.0 disables the regularizer.
Returns
---------
A function with signature `mn_i(weights, name=None)` that apply Lo regularization.
Raises
---------
ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
"""
if isinstance(scale, numbers.Integral):
raise ValueError('scale cannot be an integer: %s' % scale)
if isinstance(scale, numbers.Real):
if scale < 0.:
raise ValueError('Setting a scale less than 0 on a regularizer: %g' % scale)
# if scale >= 1.:
# raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
# scale)
if scale == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _, name=None: None
def mn_i(weights, name='maxnorm_i_regularizer'):
"""Applies max-norm regularization to weights."""
with tf.name_scope(name) as scope:
my_scale = ops.convert_to_tensor(scale, dtype=weights.dtype.base_dtype, name='scale')
if tf.__version__ <= '0.12':
standard_ops_fn = standard_ops.mul
else:
standard_ops_fn = standard_ops.multiply
return standard_ops_fn(
my_scale, standard_ops.reduce_sum(standard_ops.reduce_max(standard_ops.abs(weights), 1)), name=scope
)
return mn_i
def huber_loss(
output, target, is_mean=True, delta=1.0, dynamichuber=False, reverse=False, axis=-1, epsilon=0.00001, name=None
):
"""Huber Loss operation, see ``https://en.wikipedia.org/wiki/Huber_loss`` .
Reverse Huber Loss operation, see ''https://statweb.stanford.edu/~owen/reports/hhu.pdf''.
Dynamic Reverse Huber Loss operation, see ''https://arxiv.org/pdf/1606.00373.pdf''.
Parameters
----------
output : Tensor
A distribution with shape: [batch_size, ....], (any dimensions).
target : Tensor
The target distribution, format the same with `output`.
is_mean : boolean
Whether compute the mean or sum for each example.
- If True, use ``tf.reduce_mean`` to compute the loss between one target and predict data (default).
- If False, use ``tf.reduce_sum``.
delta: float
The point where the huber loss function changes from a quadratic to linear.
dynamichuber: boolean
Whether compute the coefficient c for each batch.
- If True, c is 20% of the maximal per-batch error.
- If False, c is delta.
reverse: boolean
Whether compute the reverse huber loss.
axis : int or list of int
The dimensions to reduce.
epsilon:
Eplison.
name : string
Name of this loss.
"""
if reverse:
if dynamichuber:
huber_c = 0.2 * tf.reduce_max(tf.abs(output - target))
else:
huber_c = delta
if is_mean:
loss = tf.reduce_mean(
tf.where(
tf.less_equal(tf.abs(output - target), huber_c), tf.abs(output - target),
tf.multiply(
tf.pow(output - target, 2.0) + tf.pow(huber_c, 2.0),
tf.math.divide_no_nan(.5, huber_c + epsilon)
)
), name=name
)
else:
loss = tf.reduce_mean(
tf.reduce_sum(
tf.where(
tf.less_equal(tf.abs(output - target), huber_c), tf.abs(output - target),
tf.multiply(
tf.pow(output - target, 2.0) + tf.pow(huber_c, 2.0),
tf.math.divide_no_nan(.5, huber_c + epsilon)
)
), axis
), name=name
)
elif is_mean:
loss = tf.reduce_mean(
tf.where(
tf.less_equal(tf.abs(output - target), delta), 0.5 * tf.pow(output - target, 2),
delta * (tf.abs(output - target) - 0.5 * delta)
), name=name
)
else:
loss = tf.reduce_mean(
tf.reduce_sum(
tf.where(
tf.less_equal(tf.abs(output - target), delta), 0.5 * tf.pow(output - target, 2),
delta * (tf.abs(output - target) - 0.5 * delta)
), axis
), name=name
)
return loss
def L1Loss(input, target, reduction='mean'):
x = tf.abs(input-target)
if reduction == 'mean':
x = tf.reduce_mean(x)
elif reduction == 'sum':
x = tf.reduce_sum(x)
return x