Python tensorflow.python.ops.standard_ops.multiply() Examples
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code examples of tensorflow.python.ops.standard_ops.multiply().
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Example #1
| Source File: cost.py From super-resolution-videos with The Unlicense | 6 votes |
|
def cosine_similarity(v1, v2):
"""Cosine similarity [-1, 1], `wiki <https://en.wikipedia.org/wiki/Cosine_similarity>`_.
Parameters
-----------
v1, v2 : tensor of [batch_size, n_feature], with the same number of features.
Returns
-----------
a tensor of [batch_size, ]
"""
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
Example #2
| Source File: cost.py From deepsleepnet with Apache License 2.0 | 6 votes |
|
def cosine_similarity(v1, v2):
"""Cosine similarity [-1, 1], `wiki <https://en.wikipedia.org/wiki/Cosine_similarity>`_.
Parameters
-----------
v1, v2 : tensor of [batch_size, n_feature], with the same number of features.
Returns
-----------
a tensor of [batch_size, ]
"""
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
Example #3
| Source File: cost.py From LapSRN-tensorflow with Apache License 2.0 | 6 votes |
|
def cosine_similarity(v1, v2):
"""Cosine similarity [-1, 1], `wiki <https://en.wikipedia.org/wiki/Cosine_similarity>`_.
Parameters
-----------
v1, v2 : tensor of [batch_size, n_feature], with the same number of features.
Returns
-----------
a tensor of [batch_size, ]
"""
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
Example #4
| Source File: model.py From cite with MIT License | 5 votes |
|
def weight_l2_regularizer(initial_weights, scale, scope=None):
"""Returns a function that can be used to apply L2 regularization to weights.
Small values of L2 can help prevent overfitting the training data.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l2(weights)` that applies L2 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l2(weights):
"""Applies l2 regularization to weights."""
with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
weight_diff = initial_weights - weights
return standard_ops.multiply(my_scale, nn.l2_loss(weight_diff), name=name)
return l2
Example #5
| Source File: regularizers.py From keras-lambda with MIT License | 5 votes |
|
def l2_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L2 regularization to weights.
Small values of L2 can help prevent overfitting the training data.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l2(weights)` that applies L2 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l2(weights):
"""Applies l2 regularization to weights."""
with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)
return l2
Example #6
| Source File: regularizers.py From keras-lambda with MIT License | 5 votes |
|
def l1_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L1 regularization to weights.
L1 regularization encourages sparsity.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l1(weights)` that apply L1 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l1(weights, name=None):
"""Applies L1 regularization to weights."""
with ops.name_scope(scope, 'l1_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(
my_scale,
standard_ops.reduce_sum(standard_ops.abs(weights)),
name=name)
return l1
Example #7
| Source File: module_utils.py From AttentionCluster with Apache License 2.0 | 5 votes |
|
def orthogonal_regularizer(scale, scope=None):
""" Return a function that computes orthogonal regularization.
:param scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
:param scope: An optional scope name.
:return: A function with signature `orthogonal_sum(weights)` that applies orthogonal regularization.
"""
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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def orthogonal_sum(weights):
""" Applies orthogonal regularization to weights. """
with ops.name_scope(scope, 'orthogonal_regularizer', [weights]) as name:
tensor_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
norm_weights = tf.nn.l2_normalize(weights, axis=1)
anchor_weights_t = tf.transpose(norm_weights)
det_reg = tf.matmul(anchor_weights_t, norm_weights)
identity = tf.eye(tf.shape(det_reg)[0])
det_reg = tf.subtract(det_reg, identity)
det_reg = tf.reduce_sum(tf.abs(det_reg))
# Print sum value before scaling
det_reg = tf.Print(det_reg, [det_reg], "Orthogonal sum for \"{}\" :".format(name))
return standard_ops.multiply(tensor_scale, det_reg, name=name)
return orthogonal_sum
Example #8
| Source File: regularizers.py From tensornets with MIT License | 5 votes |
|
def l1_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L1 regularization to weights.
L1 regularization encourages sparsity.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l1(weights)` that apply L1 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l1(weights, name=None):
"""Applies L1 regularization to weights."""
with ops.name_scope(scope, 'l1_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(
my_scale,
standard_ops.reduce_sum(standard_ops.abs(weights)),
name=name)
return l1
Example #9
| Source File: regularizers.py From tf-slim with Apache License 2.0 | 5 votes |
|
def l2_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L2 regularization to weights.
Small values of L2 can help prevent overfitting the training data.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l2(weights)` that applies L2 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l2(weights):
"""Applies l2 regularization to weights."""
with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)
return l2
Example #10
| Source File: regularizers.py From tf-slim with Apache License 2.0 | 5 votes |
|
def l1_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L1 regularization to weights.
L1 regularization encourages sparsity.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l1(weights)` that apply L1 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l1(weights, name=None):
"""Applies L1 regularization to weights."""
with ops.name_scope(scope, 'l1_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(
my_scale,
standard_ops.reduce_sum(standard_ops.abs(weights)),
name=name)
return l1
Example #11
| Source File: regularizers.py From tensornets with MIT License | 5 votes |
|
def l2_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L2 regularization to weights.
Small values of L2 can help prevent overfitting the training data.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l2(weights)` that applies L2 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l2(weights):
"""Applies l2 regularization to weights."""
with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)
return l2
Example #12
| Source File: regularizers.py From lambda-packs with MIT License | 5 votes |
|
def l1_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L1 regularization to weights.
L1 regularization encourages sparsity.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l1(weights)` that apply L1 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l1(weights, name=None):
"""Applies L1 regularization to weights."""
with ops.name_scope(scope, 'l1_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(
my_scale,
standard_ops.reduce_sum(standard_ops.abs(weights)),
name=name)
return l1
Example #13
| Source File: regularizers.py From lambda-packs with MIT License | 5 votes |
|
def l2_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L2 regularization to weights.
Small values of L2 can help prevent overfitting the training data.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l2(weights)` that applies L2 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l2(weights):
"""Applies l2 regularization to weights."""
with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)
return l2
Example #14
| Source File: regularizers.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def l2_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L2 regularization to weights.
Small values of L2 can help prevent overfitting the training data.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l2(weights)` that applies L2 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l2(weights):
"""Applies l2 regularization to weights."""
with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)
return l2
Example #15
| Source File: regularizers.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def l1_regularizer(scale, scope=None):
"""Returns a function that can be used to apply L1 regularization to weights.
L1 regularization encourages sparsity.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
scope: An optional scope name.
Returns:
A function with signature `l1(weights)` that apply L1 regularization.
Raises:
ValueError: If scale is negative 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 == 0.:
logging.info('Scale of 0 disables regularizer.')
return lambda _: None
def l1(weights, name=None):
"""Applies L1 regularization to weights."""
with ops.name_scope(scope, 'l1_regularizer', [weights]) as name:
my_scale = ops.convert_to_tensor(scale,
dtype=weights.dtype.base_dtype,
name='scale')
return standard_ops.multiply(
my_scale,
standard_ops.reduce_sum(standard_ops.abs(weights)),
name=name)
return l1
Example #16
| Source File: cost.py From deepsleepnet with Apache License 2.0 | 4 votes |
|
def maxnorm_o_regularizer(scale, scope):
"""Max-norm output regularization removes the neurons of current layer.\n
Returns a function that can be used to apply max-norm regularization to each column of weight matrix.\n
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: An optional scope name.
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
Example #17
| Source File: cost.py From super-resolution-videos with The Unlicense | 4 votes |
|
def maxnorm_o_regularizer(scale, scope):
"""Max-norm output regularization removes the neurons of current layer.\n
Returns a function that can be used to apply max-norm regularization to each column of weight matrix.\n
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: An optional scope name.
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
Example #18
| Source File: cost.py From super-resolution-videos with The Unlicense | 4 votes |
|
def maxnorm_regularizer(scale=1.0, scope=None):
"""Max-norm regularization returns a function that can be used
to apply max-norm regularization to weights.
About max-norm: `wiki <https://en.wikipedia.org/wiki/Matrix_norm#Max_norm>`_.\n
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: An optional scope name.
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
Example #19
| Source File: cost.py From super-resolution-videos with The Unlicense | 4 votes |
|
def lo_regularizer(scale, scope=None):
"""lo regularization removes the neurons of current layer, `o` represents `outputs`\n
Returns a function that can be used to apply group lo regularization to weights.\n
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: An optional scope name for TF12+.
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
Example #20
| Source File: cost.py From super-resolution-videos with The Unlicense | 4 votes |
|
def li_regularizer(scale, scope=None):
"""li regularization removes the neurons of previous layer, `i` represents `inputs`.\n
Returns a function that can be used to apply group li regularization to weights.\n
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: An optional scope name for TF12+.
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, name=None):
"""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
Example #21
| Source File: cost.py From LapSRN-tensorflow with Apache License 2.0 | 4 votes |
|
def maxnorm_o_regularizer(scale, scope):
"""Max-norm output regularization removes the neurons of current layer.\n
Returns a function that can be used to apply max-norm regularization to each column of weight matrix.\n
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: An optional scope name.
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
Example #22
| Source File: cost.py From deepsleepnet with Apache License 2.0 | 4 votes |
|
def maxnorm_regularizer(scale=1.0, scope=None):
"""Max-norm regularization returns a function that can be used
to apply max-norm regularization to weights.
About max-norm: `wiki <https://en.wikipedia.org/wiki/Matrix_norm#Max_norm>`_.\n
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: An optional scope name.
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
Example #23
| Source File: cost.py From deepsleepnet with Apache License 2.0 | 4 votes |
|
def lo_regularizer(scale, scope=None):
"""lo regularization removes the neurons of current layer, `o` represents `outputs`\n
Returns a function that can be used to apply group lo regularization to weights.\n
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: An optional scope name for TF12+.
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
Example #24
| Source File: cost.py From deepsleepnet with Apache License 2.0 | 4 votes |
|
def li_regularizer(scale, scope=None):
"""li regularization removes the neurons of previous layer, `i` represents `inputs`.\n
Returns a function that can be used to apply group li regularization to weights.\n
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: An optional scope name for TF12+.
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, name=None):
"""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
Example #25
| Source File: cost.py From LapSRN-tensorflow with Apache License 2.0 | 4 votes |
|
def li_regularizer(scale, scope=None):
"""li regularization removes the neurons of previous layer, `i` represents `inputs`.\n
Returns a function that can be used to apply group li regularization to weights.\n
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: An optional scope name for TF12+.
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, name=None):
"""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
Example #26
| Source File: cost.py From LapSRN-tensorflow with Apache License 2.0 | 4 votes |
|
def lo_regularizer(scale, scope=None):
"""lo regularization removes the neurons of current layer, `o` represents `outputs`\n
Returns a function that can be used to apply group lo regularization to weights.\n
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: An optional scope name for TF12+.
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
Example #27
| Source File: cost.py From LapSRN-tensorflow with Apache License 2.0 | 4 votes |
|
def maxnorm_regularizer(scale=1.0, scope=None):
"""Max-norm regularization returns a function that can be used
to apply max-norm regularization to weights.
About max-norm: `wiki <https://en.wikipedia.org/wiki/Matrix_norm#Max_norm>`_.\n
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: An optional scope name.
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
