ConstantParameter#

class zfit.param.ConstantParameter(name, value, *, label=None)[source]#

Bases: OverloadableMixin, ZfitParameterMixin, BaseParameter, SerializableMixin

Constant parameter.

Value cannot change.

Constant parameter that cannot change its value.

Parameters:

name – Unique identifier of the parameter.
value – Constant value.
label (str | None) – |@doc:param.init.label||@docend:param.init.label|

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 not None, 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.

to_proto(export_scope=None)#

Returns a SaveSliceInfoDef() proto.

Parameters:: export_scope – Optional string. Name scope to remove.
Returns:: A SaveSliceInfoDef protocol buffer, or None if the Variable is not in the specified name scope.

__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.

Parameters:

cache_deps (ztyping.CacherOrCachersType)
allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitGraphCachable will raise an error.

Raises:

TypeError – if one of the cache_dependents is not a ZfitGraphCachable _and_ allow_non_cachable if False.

assign(value, use_locking=False, name=None, read_value=True)#

Assigns a new value to the variable.

This is essentially a shortcut for assign(self, value).

Parameters:

value – A Tensor. The new value for this variable.
use_locking – If True, use locking during the assignment.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns:

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_add(delta, use_locking=False, name=None, read_value=True)#

Adds a value to this variable.

This is essentially a shortcut for assign_add(self, delta).

Parameters:

delta – A Tensor. The value to add to this variable.
use_locking – If True, use locking during the operation.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns:

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_sub(delta, use_locking=False, name=None, read_value=True)#

Subtracts a value from this variable.

This is essentially a shortcut for assign_sub(self, delta).

Parameters:

delta – A Tensor. The value to subtract from this variable.
use_locking – If True, use locking during the operation.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns:

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

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 using scatter_update on the subtensors that result of slicing the first dimension. This is a valid option for ndims = 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 an IndexedSlices.

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 above limit then the Op raises the exception OutOfRangeError.

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 device#: The device of this variable.

property dtype: DType#: The dtype of the object.

eval(session=None)#

In a session, computes and returns the value of this variable.

This is not a graph construction method, it does not 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. print(v.eval(sess)) # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. print(v.eval())

```

Parameters:: session – The session to use to evaluate this variable. If none, the default session is used.
Returns:: A numpy ndarray with a copy of 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:

object

Returns:

The deserialized object.

static from_proto(variable_def, import_scope=None)#: Returns a Variable object created from variable_def.

gather_nd(indices, name=None)#

Gather slices from params into a Tensor with shape specified by indices.

See tf.gather_nd for details.

Parameters:

indices – A Tensor. Must be one of the following types: int32, int64. Index tensor.
name – A name for the operation (optional).

Returns:

A Tensor. Has the same type as params.

get_cache_deps(only_floating=True)#

Return a set of all independent Parameter that this object depends on.

Parameters:: only_floating (bool) – If True, only return floating Parameter
Return type:: OrderedSet

get_dependencies(only_floating: bool = True) → ztyping.DependentsType#

DEPRECATED FUNCTION

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).

Return type:: OrderedSet

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)#

Recursively collect parameters that this object depends on according to the filter criteria.

Which parameters should be included can be steered using the arguments as a filter.

None: do not filter on this. E.g. floating=None will return parameters that are floating as well as
parameters that are fixed.
True: only return parameters that fulfil this criterion
False: only return parameters that do not fulfil this criterion. E.g. floating=False will return
only parameters that are not floating.

Parameters:

floating (bool | None) – if a parameter is floating, e.g. if floating() returns True
is_yield (bool | None) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also non-yields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.
extract_independent (bool | None) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type:

set[ZfitParameter]

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 and from_dict methods.

Returns:: The representation of the object.
Return type:: pydantic.BaseModel

get_shape()#

Alias of Variable.shape.

Return type:: TensorShape

property graph#: The Graph of this variable.

property initial_value#

Returns the Tensor used as the initial value for the variable.

Note that this is different from initialized_value() which runs the op that initializes the variable before returning its value. This method returns the tensor that is used by the op that initializes the variable.

Returns:: A Tensor.

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 initializer operation for this variable.

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#: The Operation of this 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 an IndexedSlices.

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 an IndexedSlices.

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 an IndexedSlices.

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 an IndexedSlices.

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 an IndexedSlices.

scatter_nd_add(indices, updates, name=None)#

Applies sparse addition to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.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: v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) v.scatter_nd_add(indices, updates) print(v)

```

The resulting update to v 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_sub(indices, updates, name=None)#

Applies sparse subtraction to individual values or slices in a Variable.

Assuming the variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.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: v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) v.scatter_nd_sub(indices, updates) print(v)

```

After the update v would look like this:

[1, -9, 3, -6, -4, 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.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.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: v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) v.scatter_nd_update(indices, updates) print(v)

```

The resulting update to v 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 an IndexedSlices.

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 an IndexedSlices.

set_shape(shape)#

Overrides the shape for this variable.

Parameters:: shape – the TensorShape representing the overridden shape.

sparse_read(indices, name=None)#

Gather slices from params axis axis according to indices.

This function supports a subset of tf.gather, see tf.gather for details on usage.

Parameters:

indices – The index Tensor. Must be one of the following types: int32, int64. Must be in range [0, params.shape[axis]).
name – A name for the operation (optional).

Returns:

A Tensor. Has the same type as params.

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:: dict

to_json()#

Convert the object to a json string.

Returns:: The json string.
Return type:: str

to_proto(export_scope=None)#

Converts a Variable to a VariableDef protocol buffer.

Parameters:: export_scope – Optional string. Name scope to remove.
Returns:: A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

to_yaml()#

Convert the object to a yaml string.

Returns:: The yaml string.
Return type:: str

ConstantParameter#

Args#