# -*- coding: utf-8 -*-
import logging
import tensorflow as tf
__all__ = [
'cross_entropy',
'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',
]
[docs]def cross_entropy(output, target, name=None):
"""Softmax cross-entropy operation, returns the TensorFlow expression of cross-entropy for two distributions, it implements
softmax internally. See ``tf.nn.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, ].
name : string
Name of this loss.
Examples
--------
>>> ce = tl.cost.cross_entropy(y_logits, y_target_logits, 'my_loss')
References
-----------
- About cross-entropy: `<https://en.wikipedia.org/wiki/Cross_entropy>`__.
- The code is borrowed from: `<https://en.wikipedia.org/wiki/Cross_entropy>`__.
"""
# try: # old
# return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=output, targets=target))
# except: # TF 1.0
if name is None:
raise Exception("Please give a unique name to tl.cost.cross_entropy for TF1.0+")
return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output, name=name))
[docs]def sigmoid_cross_entropy(output, target, name=None):
"""Sigmoid cross-entropy operation, see ``tf.nn.sigmoid_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, ].
name : string
Name of this loss.
"""
# try: # TF 1.0
return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output, name=name))
# except:
# return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, targets=target))
[docs]def binary_cross_entropy(output, target, epsilon=1e-8, name='bce_loss'):
"""Binary cross entropy operation.
Parameters
----------
output : Tensor
Tensor with type of `float32` or `float64`.
target : Tensor
The target distribution, format the same with `output`.
epsilon : float
A small value to avoid output to be zero.
name : str
An optional name to attach to this function.
References
-----------
- `ericjang-DRAW <https://github.com/ericjang/draw/blob/master/draw.py#L73>`__
"""
# from tensorflow.python.framework import ops
# with ops.op_scope([output, target], name, "bce_loss") as name:
# output = ops.convert_to_tensor(output, name="preds")
# target = ops.convert_to_tensor(targets, name="target")
with tf.name_scope(name):
return tf.reduce_mean(tf.reduce_sum(-(target * tf.log(output + epsilon) + (1. - target) * tf.log(1. - output + epsilon)), axis=1))
# For brevity, let `x = output`, `z = target`. The binary cross entropy loss is
#
# loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))
[docs]def mean_squared_error(output, target, is_mean=False, name="mean_squared_error"):
"""Return the TensorFlow 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`.
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.
- If False, use ``tf.reduce_sum`` (default).
References
------------
- `Wiki Mean Squared Error <https://en.wikipedia.org/wiki/Mean_squared_error>`__
"""
with tf.name_scope(name):
if output.get_shape().ndims == 2: # [batch_size, n_feature]
if is_mean:
mse = tf.reduce_mean(tf.reduce_mean(tf.squared_difference(output, target), 1))
else:
mse = tf.reduce_mean(tf.reduce_sum(tf.squared_difference(output, target), 1))
elif output.get_shape().ndims == 3: # [batch_size, w, h]
if is_mean:
mse = tf.reduce_mean(tf.reduce_mean(tf.squared_difference(output, target), [1, 2]))
else:
mse = tf.reduce_mean(tf.reduce_sum(tf.squared_difference(output, target), [1, 2]))
elif output.get_shape().ndims == 4: # [batch_size, w, h, c]
if is_mean:
mse = tf.reduce_mean(tf.reduce_mean(tf.squared_difference(output, target), [1, 2, 3]))
else:
mse = tf.reduce_mean(tf.reduce_sum(tf.squared_difference(output, target), [1, 2, 3]))
else:
raise Exception("Unknow dimension")
return mse
[docs]def normalized_mean_square_error(output, target):
"""Return the TensorFlow 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`.
"""
with tf.name_scope("mean_squared_error_loss"):
if output.get_shape().ndims == 2: # [batch_size, n_feature]
nmse_a = tf.sqrt(tf.reduce_sum(tf.squared_difference(output, target), axis=1))
nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=1))
elif output.get_shape().ndims == 3: # [batch_size, w, h]
nmse_a = tf.sqrt(tf.reduce_sum(tf.squared_difference(output, target), axis=[1, 2]))
nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=[1, 2]))
elif output.get_shape().ndims == 4: # [batch_size, w, h, c]
nmse_a = tf.sqrt(tf.reduce_sum(tf.squared_difference(output, target), axis=[1, 2, 3]))
nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=[1, 2, 3]))
nmse = tf.reduce_mean(nmse_a / nmse_b)
return nmse
[docs]def absolute_difference_error(output, target, is_mean=False):
"""Return the TensorFlow 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`.
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.
- If False, use ``tf.reduce_sum`` (default).
"""
with tf.name_scope("mean_squared_error_loss"):
if output.get_shape().ndims == 2: # [batch_size, n_feature]
if is_mean:
loss = tf.reduce_mean(tf.reduce_mean(tf.abs(output - target), 1))
else:
loss = tf.reduce_mean(tf.reduce_sum(tf.abs(output - target), 1))
elif output.get_shape().ndims == 3: # [batch_size, w, h]
if is_mean:
loss = tf.reduce_mean(tf.reduce_mean(tf.abs(output - target), [1, 2]))
else:
loss = tf.reduce_mean(tf.reduce_sum(tf.abs(output - target), [1, 2]))
elif output.get_shape().ndims == 4: # [batch_size, w, h, c]
if is_mean:
loss = tf.reduce_mean(tf.reduce_mean(tf.abs(output - target), [1, 2, 3]))
else:
loss = tf.reduce_mean(tf.reduce_sum(tf.abs(output - target), [1, 2, 3]))
else:
raise Exception("Unknow dimension")
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
---------
>>> outputs = tl.act.pixel_wise_softmax(network.outputs)
>>> dice_loss = 1 - tl.cost.dice_coe(outputs, y_)
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")
## old axis=[0,1,2,3]
# dice = 2 * (inse) / (l + r)
# epsilon = 1e-5
# dice = tf.clip_by_value(dice, 0, 1.0-epsilon) # if all empty, dice = 1
## new haodong
dice = (2. * inse + smooth) / (l + r + smooth)
##
dice = tf.reduce_mean(dice)
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``.
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)
## old axis=[0,1,2,3]
# hard_dice = 2 * (inse) / (l + r)
# epsilon = 1e-5
# hard_dice = tf.clip_by_value(hard_dice, 0, 1.0-epsilon)
## new haodong
hard_dice = (2. * inse + smooth) / (l + r + smooth)
##
hard_dice = tf.reduce_mean(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``.
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
## old axis=[0,1,2,3]
# epsilon = 1e-5
# batch_iou = inse / (union + epsilon)
## new haodong
batch_iou = (inse + smooth) / (union + smooth)
iou = tf.reduce_mean(batch_iou)
return iou #, pre, truth, inse, union
# ## test soft/hard dice and iou
# import numpy as np
# y = np.zeros((1,10,10,1))
# # y[0,0:5,0:5]=1.0
# o = np.zeros((1,10,10,1))
# # o[:,:,:,:] = 0 # what we want: dice=0 iou=0 OK
# # o[0,0:2,0:2]=0.3 # what we want: dice larger iou=0 OK
# # o[0,0:2,0:2]=0.6 # what we want: dice larger iou small OK
# # o[0,0:3,0:3]=0.6 # what we want: dice larger iou larger OK
# # o[0,0:3,0:3]=1 # what we want: dice larger iou same OK
# # o[0,0:5,0:5]=1 # what we want: dice=1 iou=1 OK
# # o[0,0:5,0:5]=0.3 # what we want: dice smaller iou=0 OK
# # o[0,0:5,0:5]=1e-2 # what we want: dice≈0 iou=0 OK
# # o[0,8:10,8:10]=1.0 # what we want: dice=0 iou=0 OK
# # o[0,8:10,8:10]=1e-10 # what we want: dice=0 iou=0 OK
# # y[:,:,:,:] = o[:,:,:,:] = 0 # what we want: dice=1 iou=1 OK
# ## why in u-net, dice=1 hard-dice=1 iou=1 exist?? print bug?
#
# d = dice_coe(o, y, 'jaccard', smooth=1.)
# hd = dice_hard_coe(o, y, smooth=1e-5)
# i = iou_coe(o, y, smooth=1e-5)
# sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# # sess.run(tf.local_variables_initializer())
# print(sess.run([d,hd,i]))
# # p, t, i, u = sess.run([pre, truth, inse, union])
# # import pprint
# # pprint.pprint(((y>0.5)*(o>0.5)).astype(int).tolist())
# # pprint.pprint(p.tolist())
# # pprint.pprint(t.tolist())
# # pprint.pprint(i)
# # pprint.pprint(u)
# exit()
[docs]def cross_entropy_seq(logits, target_seqs, batch_size=None): #, batch_size=1, num_steps=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/zsdonghao/tensorlayer/blob/master/example/tutorial_ptb_lstm_state_is_tuple.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 ``tl.cost.cross_entropy_seq_with_mask`` instead.
batch_size : None or int.
Whether to divide the cost by batch size.
- If integer, the return cost will be divided by `batch_size`.
- If None (default), the return cost will not be divided by anything.
Examples
--------
>>> see `PTB example <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_ptb_lstm_state_is_tuple.py>`__.for more details
>>> input_data = tf.placeholder(tf.int32, [batch_size, n_steps])
>>> targets = tf.placeholder(tf.int32, [batch_size, n_steps])
>>> # build the network
>>> print(net.outputs)
... (batch_size * n_steps, n_classes)
>>> cost = tl.cost.cross_entropy_seq(network.outputs, targets)
"""
# try: # TF 1.0
sequence_loss_by_example_fn = tf.contrib.legacy_seq2seq.sequence_loss_by_example
# except:
# sequence_loss_by_example_fn = tf.nn.seq2seq.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
--------
>>> batch_size = 64
>>> vocab_size = 10000
>>> embedding_size = 256
>>> input_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="input")
>>> target_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target")
>>> input_mask = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="mask")
>>> net = tl.layers.EmbeddingInputlayer(
... inputs = input_seqs,
... vocabulary_size = vocab_size,
... embedding_size = embedding_size,
... name = 'seq_embedding')
>>> net = tl.layers.DynamicRNNLayer(net,
... cell_fn = tf.contrib.rnn.BasicLSTMCell,
... n_hidden = embedding_size,
... dropout = (0.7 if is_train else None),
... sequence_length = tl.layers.retrieve_seq_length_op2(input_seqs),
... return_seq_2d = True,
... name = 'dynamicrnn')
>>> print(net.outputs)
... (?, 256)
>>> net = tl.layers.DenseLayer(net, n_units=vocab_size, name="output")
>>> print(net.outputs)
... (?, 10000)
>>> loss = tl.cost.cross_entropy_seq_with_mask(net.outputs, target_seqs, input_mask)
"""
targets = tf.reshape(target_seqs, [-1]) # to one vector
weights = tf.to_float(tf.reshape(input_mask, [-1])) # 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.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets, name=name)) # for TF1.0 and others
# try: ## TF1.0
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")
# except: ## TF0.12
# loss = tf.div(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].
Returns
-------
Tensor
a tensor of shape [batch_size].
References
----------
- `<https://en.wikipedia.org/wiki/Cosine_similarity>`__.
"""
# try: ## TF1.0
cost = 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)))
# except: ## TF0.12
# cost = tf.reduce_sum(tf.mul(v1, v2), reduction_indices=1) / (tf.sqrt(tf.reduce_sum(tf.mul(v1, v1), reduction_indices=1)) * tf.sqrt(tf.reduce_sum(tf.mul(v2, v2), reduction_indices=1)))
return cost
## 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.
"""
import numbers
from tensorflow.python.framework import ops
from tensorflow.python.ops import standard_ops
# from tensorflow.python.platform import tf_logging as logging
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.
"""
import numbers
from tensorflow.python.framework import ops
from tensorflow.python.ops import standard_ops
# from tensorflow.python.platform import tf_logging as logging
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
[docs]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.
"""
import numbers
from tensorflow.python.framework import ops
from tensorflow.python.ops import standard_ops
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.
"""
import numbers
from tensorflow.python.framework import ops
from tensorflow.python.ops import standard_ops
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.
"""
import numbers
from tensorflow.python.framework import ops
from tensorflow.python.ops import standard_ops
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