Parameter#
- class zfit.param.Parameter(name, value, lower=None, upper=None, stepsize=None, floating=True, *, label=None, dtype=None, step_size=None)[source]#
Bases:
ZfitParameterMixin
,TFBaseVariable
,BaseParameter
,SerializableMixin
,ZfitIndependentParameter
Class for fit parameters that has a default state.
Fit Parameter that has a default state (value) and limits (lower, upper).
The name identifies the parameter. Multiple parameters with the same name can exist, however, they cannot be in the same PDF/func/loss as the value would not be uniquely defined.
- Parameters:
name (str) – Name of the parameter. Should be unique within a model/likelihood.
value (ztyping.NumericalScalarType) – Default value of the parameter. Also used as the starting value in minimization.
lower (ztyping.NumericalScalarType | None) – lower limit of the parameter. If the parameter is set to a value below the lower limit, it will raise an error.
upper (ztyping.NumericalScalarType | None) – upper limit of the parameter. If the parameter is set to a value above the upper limit, it will raise an error.
floating (bool) – If the parameter is floating (can change value) or fixed (constant) in the minimization.
label (str | None) – |@doc:param.init.label||@docend:param.init.label|
stepsize (ztyping.NumericalScalarType | None) – Initial step size for minimization. If not set, a default value is used.
- property at_limit: Tensor#
If the value is at the limit (or over it).
- Returns:
Boolean
tf.Tensor
that tells whether the value is at the limits.
- read_value()[source]#
DEPRECATED FUNCTION
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use
value
instead.
- property has_step_size#
DEPRECATED FUNCTION
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use
has_stepsize
instead.
- property stepsize: Tensor#
Step size of the parameter, the estimated order of magnitude of the uncertainty.
This can be crucial to tune for the minimization. A too large
stepsize
can produce NaNs, a too small won’t converge.If the step size is not set, the
DEFAULT_stepsize
is used.- Returns:
The step size
- property step_size: tf.Tensor#
DEPRECATED FUNCTION
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use
stepsize
instead.
- set_value(value)[source]#
Set the
Parameter
tovalue
(temporarily if used in a context manager).This operation won’t, compared to the assign, return the read value but an object that can act as a context manager.
- Parameters:
value (ztyping.NumericalScalarType) – The value the parameter will take on.
- Raises:
ValueError – If the value is not inside the limits (in normal Python/eager mode)
InvalidArgumentError – If the value is not inside the limits (in JIT/traced/graph mode)
- assign(value, use_locking=None, read_value=False)[source]#
Set the
Parameter
tovalue
without any checks.Compared to
set_value
, this method cannot be used with a context manager and won’t raise an error- Parameters:
value – The value the parameter will take on.
- randomize(minval=None, maxval=None, sampler=<built-in method uniform of numpy.random.mtrand.RandomState object>)[source]#
Update the parameter with a randomised value between minval and maxval and return it.
- Parameters:
minval (ztyping.NumericalScalarType | None) – The lower bound of the sampler. If not given,
lower_limit
is used.maxval (ztyping.NumericalScalarType | None) – The upper bound of the sampler. If not given,
upper_limit
is used.sampler (Callable) – A sampler with the same interface as
np.random.uniform
- Return type:
tf.Tensor
- Returns:
The sampled value
- class SaveSliceInfo(full_name=None, full_shape=None, var_offset=None, var_shape=None, save_slice_info_def=None, import_scope=None)#
Bases:
object
Information on how to save this Variable as a slice.
Provides internal support for saving variables as slices of a larger variable. This API is not public and is subject to change.
Available properties:
full_name
full_shape
var_offset
var_shape
Create a
SaveSliceInfo
.- Parameters:
full_name – Name of the full variable of which this
Variable
is a slice.full_shape – Shape of the full variable, as a list of int.
var_offset – Offset of this
Variable
into the full variable, as a list of int.var_shape – Shape of this
Variable
, as a list of int.save_slice_info_def –
SaveSliceInfoDef
protocol buffer. If notNone
, recreates the SaveSliceInfo object its contents.save_slice_info_def
and other arguments are mutually exclusive.import_scope – Optional
string
. Name scope to add. Only used when initializing from protocol buffer.
- property spec#
Computes the spec string used for saving.
- __array__(dtype=None)#
Allows direct conversion to a numpy array.
>>> np.array(tf.Variable([1.0])) array([1.], dtype=float32)
- Returns:
The variable value as a numpy array.
- __iter__()#
When executing eagerly, iterates over the value of the variable.
- __ne__(other)#
Compares two variables element-wise for equality.
- add_cache_deps(cache_deps, allow_non_cachable=True)#
Add dependencies that render the cache invalid if they change.
- assign_add(delta, use_locking=None, name=None, read_value=True)#
Adds a value to this variable.
- Parameters:
- Returns:
If
read_value
isTrue
, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return theOperation
that does the assignment, and when in eager mode it will returnNone
.
- assign_sub(delta, use_locking=None, name=None, read_value=True)#
Subtracts a value from this variable.
- Parameters:
- Returns:
If
read_value
isTrue
, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return theOperation
that does the assignment, and when in eager mode it will returnNone
.
- batch_scatter_update(sparse_delta, use_locking=False, name=None)#
Assigns
tf.IndexedSlices
to this variable batch-wise.Analogous to
batch_gather
. This assumes that this variable and the sparse_delta IndexedSlices have a series of leading dimensions that are the same for all of them, and the updates are performed on the last dimension of indices. In other words, the dimensions should be the following:num_prefix_dims = sparse_delta.indices.ndims - 1
batch_dim = num_prefix_dims + 1
`sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[batch_dim:]`
where
sparse_delta.updates.shape[:num_prefix_dims]
== sparse_delta.indices.shape[:num_prefix_dims]
== var.shape[:num_prefix_dims]
And the operation performed can be expressed as:
- `var[i_1, …, i_n,
- sparse_delta.indices[i_1, …, i_n, j]] = sparse_delta.updates[
i_1, …, i_n, j]`
When sparse_delta.indices is a 1D tensor, this operation is equivalent to
scatter_update
.To avoid this operation one can looping over the first
ndims
of the variable and usingscatter_update
on the subtensors that result of slicing the first dimension. This is a valid option forndims = 1
, but less efficient than this implementation.- Parameters:
sparse_delta –
tf.IndexedSlices
to be assigned to this variable.use_locking – If
True
, use locking during the operation.name – the name of the operation.
- Returns:
The updated variable.
- Raises:
TypeError – if
sparse_delta
is not anIndexedSlices
.
- property constraint#
Returns the constraint function associated with this variable.
- Returns:
The constraint function that was passed to the variable constructor. Can be
None
if no constraint was passed.
- count_up_to(limit)#
Increments this variable until it reaches
limit
. (deprecated)Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Dataset.range instead.
When that Op is run it tries to increment the variable by
1
. If incrementing the variable would bring it abovelimit
then the Op raises the exceptionOutOfRangeError
.If no error is raised, the Op outputs the value of the variable before the increment.
This is essentially a shortcut for
count_up_to(self, limit)
.- Parameters:
limit – value at which incrementing the variable raises an error.
- Returns:
A
Tensor
that will hold the variable value before the increment. If no other Op modifies this variable, the values produced will all be distinct.
- property create#
The op responsible for initializing this variable.
- property device#
The device this variable is on.
- property dtype: DType#
The dtype of the object.
- eval(session=None)#
Evaluates and returns the value of this variable.
- experimental_ref()#
DEPRECATED FUNCTION
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.
- classmethod from_asdf(asdf_obj, *, reuse_params=None)#
Load an object from an asdf file.
Args#
asdf_obj: Object reuse_params:If parameters, the parameters
will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.
- classmethod from_dict(dict_, *, reuse_params=None)#
Creates an object from a dictionary structure as generated by
to_dict
.- Parameters:
dict – Dictionary structure.
reuse_params – If parameters, the parameters will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.
- Returns:
The deserialized object.
- classmethod from_json(cls, json, *, reuse_params=None)#
Load an object from a json string.
- Parameters:
json (
str
) – Serialized object in a JSON string.reuse_params – If parameters, the parameters will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.
- Return type:
- Returns:
The deserialized object.
- classmethod get_repr()#
Abstract representation of the object for serialization.
This objects knows how to serialize and deserialize the object and is used by the
to_json
,from_json
,to_dict
andfrom_dict
methods.- Returns:
The representation of the object.
- Return type:
- get_shape()#
Alias of
Variable.shape
.- Return type:
TensorShape
- property graph#
The
Graph
of this variable.
- property handle#
The handle by which this variable can be accessed.
- property initial_value#
Returns the Tensor used as the initial value for the variable.
- initialized_value()#
Returns the value of the initialized variable. (deprecated)
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
You should use this instead of the variable itself to initialize another variable with a value that depends on the value of this variable.
`python # Initialize 'v' with a random tensor. v = tf.Variable(tf.random.truncated_normal([10, 40])) # Use `initialized_value` to guarantee that `v` has been # initialized before its value is used to initialize `w`. # The random values are picked only once. w = tf.Variable(v.initialized_value() * 2.0) `
- Returns:
A
Tensor
holding the value of this variable after its initializer has run.
- property initializer#
The op responsible for initializing this variable.
- is_initialized(name=None)#
Checks whether a resource variable has been initialized.
Outputs boolean scalar indicating whether the tensor has been initialized.
- Parameters:
name – A name for the operation (optional).
- Returns:
A
Tensor
of typebool
.
- load(value, session=None)#
Load new value into this variable. (deprecated)
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Variable.assign which has equivalent behavior in 2.X.
Writes new value to variable’s memory. Doesn’t add ops to the graph.
This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See
tf.compat.v1.Session
for more information on launching a graph and on sessions.```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()
- with tf.compat.v1.Session() as sess:
sess.run(init) # Usage passing the session explicitly. v.load([2, 3], sess) print(v.eval(sess)) # prints [2 3] # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. v.load([3, 4], sess) print(v.eval()) # prints [3 4]
- Parameters:
value – New variable value
session – The session to use to evaluate this variable. If none, the default session is used.
- Raises:
ValueError – Session is not passed and no default session
- property op: Operation#
The op for this variable.
- read_value_no_copy()#
Constructs an op which reads the value of this variable without copy.
The variable is read without making a copy even when it has been sparsely accessed. Variables in copy-on-read mode will be converted to copy-on-write mode.
- Returns:
The value of the variable.
- ref()#
Returns a hashable reference object to this Variable.
The primary use case for this API is to put variables in a set/dictionary. We can’t put variables in a set/dictionary as
variable.__hash__()
is no longer available starting Tensorflow 2.0.The following will raise an exception starting 2.0
>>> x = tf.Variable(5) >>> y = tf.Variable(10) >>> z = tf.Variable(10) >>> variable_set = {x, y, z} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key. >>> variable_dict = {x: 'five', y: 'ten'} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
Instead, we can use
variable.ref()
.>>> variable_set = {x.ref(), y.ref(), z.ref()} >>> x.ref() in variable_set True >>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'} >>> variable_dict[y.ref()] 'ten'
Also, the reference object provides
deref()
function that returns the original Variable.>>> x = tf.Variable(5) >>> x.ref().deref() <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>
- register_cacher(cacher)#
Register a
cacher
that caches values produces by this instance; a dependent.- Parameters:
cacher (ztyping.CacherOrCachersType)
- reset_cache_self()#
Clear the cache of self and all dependent cachers.
- scatter_add(sparse_delta, use_locking=False, name=None)#
Adds
tf.IndexedSlices
to this variable.- Parameters:
sparse_delta –
tf.IndexedSlices
to be added to this variable.use_locking – If
True
, use locking during the operation.name – the name of the operation.
- Returns:
The updated variable.
- Raises:
TypeError – if
sparse_delta
is not anIndexedSlices
.
- scatter_div(sparse_delta, use_locking=False, name=None)#
Divide this variable by
tf.IndexedSlices
.- Parameters:
sparse_delta –
tf.IndexedSlices
to divide this variable by.use_locking – If
True
, use locking during the operation.name – the name of the operation.
- Returns:
The updated variable.
- Raises:
TypeError – if
sparse_delta
is not anIndexedSlices
.
- scatter_max(sparse_delta, use_locking=False, name=None)#
Updates this variable with the max of
tf.IndexedSlices
and itself.- Parameters:
sparse_delta –
tf.IndexedSlices
to use as an argument of max with this variable.use_locking – If
True
, use locking during the operation.name – the name of the operation.
- Returns:
The updated variable.
- Raises:
TypeError – if
sparse_delta
is not anIndexedSlices
.
- scatter_min(sparse_delta, use_locking=False, name=None)#
Updates this variable with the min of
tf.IndexedSlices
and itself.- Parameters:
sparse_delta –
tf.IndexedSlices
to use as an argument of min with this variable.use_locking – If
True
, use locking during the operation.name – the name of the operation.
- Returns:
The updated variable.
- Raises:
TypeError – if
sparse_delta
is not anIndexedSlices
.
- scatter_mul(sparse_delta, use_locking=False, name=None)#
Multiply this variable by
tf.IndexedSlices
.- Parameters:
sparse_delta –
tf.IndexedSlices
to multiply this variable by.use_locking – If
True
, use locking during the operation.name – the name of the operation.
- Returns:
The updated variable.
- Raises:
TypeError – if
sparse_delta
is not anIndexedSlices
.
- scatter_nd_add(indices, updates, name=None)#
Applies sparse addition to individual values or slices in a Variable.
ref
is aTensor
with rankP
andindices
is aTensor
of rankQ
.indices
must be integer tensor, containing indices intoref
. It must be shape[d_0, ..., d_{Q-2}, K]
where0 < K <= P
.The innermost dimension of
indices
(with lengthK
) corresponds to indices into elements (ifK = P
) or slices (ifK < P
) along theK`th dimension of `ref
.updates
isTensor
of rankQ-1+P-K
with shape:` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
- ```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) add = ref.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(add)
The resulting update to ref would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See
tf.scatter_nd
for more details about how to make updates to slices.- Parameters:
indices – The indices to be used in the operation.
updates – The values to be used in the operation.
name – the name of the operation.
- Returns:
The updated variable.
- scatter_nd_max(indices, updates, name=None)#
Updates this variable with the max of
tf.IndexedSlices
and itself.ref
is aTensor
with rankP
andindices
is aTensor
of rankQ
.indices
must be integer tensor, containing indices intoref
. It must be shape[d_0, ..., d_{Q-2}, K]
where0 < K <= P
.The innermost dimension of
indices
(with lengthK
) corresponds to indices into elements (ifK = P
) or slices (ifK < P
) along theK`th dimension of `ref
.updates
isTensor
of rankQ-1+P-K
with shape:` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `
See
tf.scatter_nd
for more details about how to make updates to slices.- Parameters:
indices – The indices to be used in the operation.
updates – The values to be used in the operation.
name – the name of the operation.
- Returns:
The updated variable.
- scatter_nd_min(indices, updates, name=None)#
Updates this variable with the min of
tf.IndexedSlices
and itself.ref
is aTensor
with rankP
andindices
is aTensor
of rankQ
.indices
must be integer tensor, containing indices intoref
. It must be shape[d_0, ..., d_{Q-2}, K]
where0 < K <= P
.The innermost dimension of
indices
(with lengthK
) corresponds to indices into elements (ifK = P
) or slices (ifK < P
) along theK`th dimension of `ref
.updates
isTensor
of rankQ-1+P-K
with shape:` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `
See
tf.scatter_nd
for more details about how to make updates to slices.- Parameters:
indices – The indices to be used in the operation.
updates – The values to be used in the operation.
name – the name of the operation.
- Returns:
The updated variable.
- scatter_nd_sub(indices, updates, name=None)#
Applies sparse subtraction to individual values or slices in a Variable.
ref
is aTensor
with rankP
andindices
is aTensor
of rankQ
.indices
must be integer tensor, containing indices intoref
. It must be shape[d_0, ..., d_{Q-2}, K]
where0 < K <= P
.The innermost dimension of
indices
(with lengthK
) corresponds to indices into elements (ifK = P
) or slices (ifK < P
) along theK`th dimension of `ref
.updates
isTensor
of rankQ-1+P-K
with shape:` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
- ```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = ref.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to ref would look like this:
[1, -9, 3, -6, -6, 6, 7, -4]
See
tf.scatter_nd
for more details about how to make updates to slices.- Parameters:
indices – The indices to be used in the operation.
updates – The values to be used in the operation.
name – the name of the operation.
- Returns:
The updated variable.
- scatter_nd_update(indices, updates, name=None)#
Applies sparse assignment to individual values or slices in a Variable.
ref
is aTensor
with rankP
andindices
is aTensor
of rankQ
.indices
must be integer tensor, containing indices intoref
. It must be shape[d_0, ..., d_{Q-2}, K]
where0 < K <= P
.The innermost dimension of
indices
(with lengthK
) corresponds to indices into elements (ifK = P
) or slices (ifK < P
) along theK`th dimension of `ref
.updates
isTensor
of rankQ-1+P-K
with shape:` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
- ```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = ref.scatter_nd_update(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See
tf.scatter_nd
for more details about how to make updates to slices.- Parameters:
indices – The indices to be used in the operation.
updates – The values to be used in the operation.
name – the name of the operation.
- Returns:
The updated variable.
- scatter_sub(sparse_delta, use_locking=False, name=None)#
Subtracts
tf.IndexedSlices
from this variable.- Parameters:
sparse_delta –
tf.IndexedSlices
to be subtracted from this variable.use_locking – If
True
, use locking during the operation.name – the name of the operation.
- Returns:
The updated variable.
- Raises:
TypeError – if
sparse_delta
is not anIndexedSlices
.
- scatter_update(sparse_delta, use_locking=False, name=None)#
Assigns
tf.IndexedSlices
to this variable.- Parameters:
sparse_delta –
tf.IndexedSlices
to be assigned to this variable.use_locking – If
True
, use locking during the operation.name – the name of the operation.
- Returns:
The updated variable.
- Raises:
TypeError – if
sparse_delta
is not anIndexedSlices
.
- property shape#
The shape of this variable.
- sparse_read(indices, name=None)#
Reads the value of this variable sparsely, using
gather
.
- to_asdf()#
Convert the object to an asdf file.
- to_dict()#
Convert the object to a nested dictionary structure.
- Returns:
The dictionary structure.
- Return type:
- to_proto(export_scope=None)#
Converts a
ResourceVariable
to aVariableDef
protocol buffer.- Parameters:
export_scope – Optional
string
. Name scope to remove.- Raises:
RuntimeError – If run in EAGER mode.
- Returns:
A
VariableDef
protocol buffer, orNone
if theVariable
is not in the specified name scope.