# zfit.core.parameter module¶

Define Parameter which holds the value.

class zfit.core.parameter.MetaBaseParameter(name, bases, namespace, **kwargs)[source]

Bases: tensorflow.python.ops.variables.VariableMetaclass, abc.ABCMeta

__instancecheck__(instance)

Override for isinstance(instance, cls).

__subclasscheck__(subclass)

Override for issubclass(subclass, cls).

mro()

Return a type’s method resolution order.

register(subclass)

Register a virtual subclass of an ABC.

Returns the subclass, to allow usage as a class decorator.

zfit.core.parameter.register_tensor_conversion(convertable, name=None, overload_operators=True, priority=10)[source]
class zfit.core.parameter.OverloadableMixin[source]
abstract property dtype

The DType of Tensors handled by this model.

Return type

DType

abstract property floating
Return type

bool

abstract get_cache_deps(only_floating=True)
Return type

OrderedSet

get_dependencies(only_floating=True)

DEPRECATED FUNCTION

Warning: 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

abstract get_params(floating=True, is_yield=None, extract_independent=True)

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 (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – 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 (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type
abstract property independent
Return type

bool

abstract property name
Return type

str

abstract property params
Return type

~ParametersType

abstract read_value()
Return type

Tensor

abstract property shape
abstract value()
Return type

Tensor

class zfit.core.parameter.WrappedVariable(*args, **kwargs)[source]

Bases: object

abstract property name
property constraint
property dtype
value()[source]
read_valu()[source]
property shape
numpy()[source]
assign(value, use_locking=False, name=None, read_value=True)[source]
class zfit.core.parameter.BaseParameter(*args, **kwargs)[source]

Bases: tensorflow.python.ops.variables.Variable, zfit.core.interfaces.ZfitParameter, tensorflow.python.types.core.Tensor

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_defSaveSliceInfoDef 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.

__eq__(other)

Compares two variables element-wise for equality.

__iter__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__ne__(other)

Compares two variables element-wise for equality.

property aggregation
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

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_deltatf.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)

Warning: 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

The DType of this variable.

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

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

abstract property floating
Return type

bool

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.

abstract get_cache_deps(only_floating=True)
Return type

OrderedSet

get_dependencies(only_floating=True)

DEPRECATED FUNCTION

Warning: 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

abstract get_params(floating=True, is_yield=None, extract_independent=True)

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 (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – 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 (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type
get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

abstract property independent
Return type

bool

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)

Warning: 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)

Warning: 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 name

The name of this variable.

property op

The Operation of this variable.

abstract property params
Return type

~ParametersType

read_value()

Returns the value of this variable, read in the current context.

Can be different from value() if it’s on another device, with control dependencies, etc.

Returns

A Tensor containing 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.


>>> 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>

scatter_add(sparse_delta, use_locking=False, name=None)

Parameters
• sparse_deltatf.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_deltatf.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_deltatf.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_deltatf.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_deltatf.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 Kth 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]) add = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

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 Kth 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]) op = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

The resulting update to v 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.

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 Kth 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]) op = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

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_deltatf.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_deltatf.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.

property shape

The TensorShape of this variable.

Returns

A TensorShape.

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.

property synchronization
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.

property trainable
value()

Returns the last snapshot of this variable.

You usually do not need to call this method as all ops that need the value of the variable call it automatically through a convert_to_tensor() call.

Returns a Tensor which holds the value of the variable. You can not assign a new value to this tensor as it is not a reference to the variable.

To avoid copies, if the consumer of the returned value is on the same device as the variable, this actually returns the live value of the variable, not a copy. Updates to the variable are seen by the consumer. If the consumer is on a different device it will get a copy of the variable.

Returns

A Tensor containing the value of the variable.

class zfit.core.parameter.ZfitParameterMixin(name, **kwargs)[source]
property name
Return type

str

add_cache_deps(cache_deps, allow_non_cachable=True)

Add dependencies that render the cache invalid if they change.

Parameters
Raises

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

copy(deep=False, name=None, **overwrite_params)
Return type

ZfitObject

property dtype

The dtype of the object

Return type

DType

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=True)

DEPRECATED FUNCTION

Warning: 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 (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – 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 (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type
graph_caching_methods = []
instances = <_weakrefset.WeakSet object>
property params
Return type

~ParametersType

register_cacher(cacher)

Register a cacher that caches values produces by this instance; a dependent.

Parameters
reset_cache(reseter)
reset_cache_self()

Clear the cache of self and all dependent cachers.

class zfit.core.parameter.TFBaseVariable(*args, **kwargs)[source]

Bases: tensorflow.python.ops.resource_variable_ops.ResourceVariable

Creates a variable.

Parameters
• initial_value – A Tensor, or Python object convertible to a Tensor, which is the initial value for the Variable. Can also be a callable with no argument that returns the initial value when called. (Note that initializer functions from init_ops.py must first be bound to a shape before being used here.)

• trainable – If True, the default, also adds the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES. This collection is used as the default list of variables to use by the Optimizer classes. Defaults to True, unless synchronization is set to ON_READ, in which case it defaults to False.

• collections – List of graph collections keys. The new variable is added to these collections. Defaults to [GraphKeys.GLOBAL_VARIABLES].

• validate_shape – Ignored. Provided for compatibility with tf.Variable.

• caching_device – Optional device string or function describing where the Variable should be cached for reading. Defaults to the Variable’s device. If not None, caches on another device. Typical use is to cache on the device where the Ops using the Variable reside, to deduplicate copying through Switch and other conditional statements.

• name – Optional name for the variable. Defaults to ‘Variable’ and gets uniquified automatically.

• dtype – If set, initial_value will be converted to the given type. If None, either the datatype will be kept (if initial_value is a Tensor) or float32 will be used (if it is a Python object convertible to a Tensor).

• variable_defVariableDef protocol buffer. If not None, recreates the ResourceVariable object with its contents. variable_def and other arguments (except for import_scope) are mutually exclusive.

• import_scope – Optional string. Name scope to add to the ResourceVariable. Only used when variable_def is provided.

• constraint – An optional projection function to be applied to the variable after being updated by an Optimizer (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected Tensor representing the value of the variable and return the Tensor for the projected value (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.

• distribute_strategy – The tf.distribute.Strategy this variable is being created inside of.

• synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize.

• aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation.

• shape – (optional) The shape of this variable. If None, the shape of initial_value will be used. When setting this argument to tf.TensorShape(None) (representing an unspecified shape), the variable can be assigned with values of different shapes.

Raises

ValueError – If the initial value is not specified, or does not have a shape and validate_shape is True.

@compatibility(eager) When Eager Execution is enabled, the default for the collections argument is None, which signifies that this Variable will not be added to any collections. @end_compatibility

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_defSaveSliceInfoDef 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.

__eq__(other)

Compares two variables element-wise for equality.

__iter__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__ne__(other)

Compares two variables element-wise for equality.

property aggregation
assign(value, use_locking=None, name=None, read_value=True)

Assigns a new value to this variable.

Parameters
• value – A Tensor. The new value for this variable.

• use_locking – If True, use locking during the assignment.

• name – The name to use for the assignment.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

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

Adds a value to this variable.

Parameters
• delta – A Tensor. The value to add to this variable.

• use_locking – If True, use locking during the operation.

• name – The name to use for the operation.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

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

Subtracts a value from this variable.

Parameters
• delta – A Tensor. The value to subtract from this variable.

• use_locking – If True, use locking during the operation.

• name – The name to use for the operation.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

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

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_deltatf.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)

Warning: 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 create

The op responsible for initializing this variable.

property device

The device this variable is on.

property dtype

The dtype of this variable.

eval(session=None)

Evaluates and returns the value of this variable.

experimental_ref()

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

static from_proto(variable_def, import_scope=None)
gather_nd(indices, name=None)

Reads the value of this variable sparsely, using gather_nd.

get_shape()

Alias of Variable.shape.

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)

Warning: 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 type bool.

load(value, session=None)

Load new value into this variable. (deprecated)

Warning: 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 name

The name of the handle for this variable.

numpy()
property op

The op for this variable.

read_value()

Constructs an op which reads the value of this variable.

Should be used when there are multiple reads, or when it is desirable to read the value only after some condition is true.

Returns

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.


>>> 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>

scatter_add(sparse_delta, use_locking=False, name=None)

Parameters
• sparse_deltatf.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_deltatf.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_deltatf.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_deltatf.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_deltatf.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.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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:

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 a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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 a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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 a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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 a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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_deltatf.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_deltatf.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)
property shape

The shape of this variable.

sparse_read(indices, name=None)

Reads the value of this variable sparsely, using gather.

property synchronization
to_proto(export_scope=None)

Converts a ResourceVariable to a VariableDef protocol buffer.

Parameters

export_scope – Optional string. Name scope to remove.

Raises

RuntimeError – If run in EAGER mode.

Returns

A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property trainable
value()

A cached operation which reads the value of this variable.

class zfit.core.parameter.Parameter(name, value, lower_limit=None, upper_limit=None, step_size=None, floating=True, dtype=tf.float64, **kwargs)[source]

Class for fit parameters, derived from TF Variable class.

Parameters
DEFAULT_STEP_SIZE = 0.001
property lower
property upper
property has_limits

If the parameter has limits set or not.

Return type

bool

property at_limit

If the value is at the limit (or over it).

The precision is up to 1e-5 relative.

Return type

Tensor

Returns

Boolean tf.Tensor that tells whether the value is at the limits.

value()[source]
read_value()[source]
property floating
property independent
property step_size

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 step_size can produce NaNs, a too small won’t converge.

If the step size is not set, the DEFAULT_STEP_SIZE is used.

Return type

Tensor

Returns

The step size

set_value(value)[source]

Set the Parameter to value (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 (Union[int, float, complex, Tensor, ZfitParameter]) – 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
Return type

Tensor

Returns

The sampled value

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)[source]
Return type
property lower_limit
property upper_limit
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_defSaveSliceInfoDef 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__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__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
Raises

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

property aggregation
assign(value, use_locking=None, name=None, read_value=True)

Assigns a new value to this variable.

Parameters
• value – A Tensor. The new value for this variable.

• use_locking – If True, use locking during the assignment.

• name – The name to use for the assignment.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

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

Adds a value to this variable.

Parameters
• delta – A Tensor. The value to add to this variable.

• use_locking – If True, use locking during the operation.

• name – The name to use for the operation.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

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

Subtracts a value from this variable.

Parameters
• delta – A Tensor. The value to subtract from this variable.

• use_locking – If True, use locking during the operation.

• name – The name to use for the operation.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

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

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_deltatf.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.

copy(deep=False, name=None, **overwrite_params)
Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: 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 create

The op responsible for initializing this variable.

property device

The device this variable is on.

property dtype

The dtype of the object

Return type

DType

eval(session=None)

Evaluates and returns the value of this variable.

experimental_ref()

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

static from_proto(variable_def, import_scope=None)
gather_nd(indices, name=None)

Reads the value of this variable sparsely, using gather_nd.

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=True)

DEPRECATED FUNCTION

Warning: 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_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []
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)

Warning: 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.

instances = <_weakrefset.WeakSet object>
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 type bool.

load(value, session=None)

Load new value into this variable. (deprecated)

Warning: 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 name
Return type

str

numpy()
property op

The op for this variable.

property params
Return type

~ParametersType

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.


>>> 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
reset_cache(reseter)
reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Parameters
• sparse_deltatf.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_deltatf.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_deltatf.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_deltatf.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_deltatf.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.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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:

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 a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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 a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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 a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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 a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. 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 Kth dimension of ref.

updates is Tensor of rank Q-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_deltatf.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_deltatf.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)
property shape

The shape of this variable.

sparse_read(indices, name=None)

Reads the value of this variable sparsely, using gather.

property synchronization
to_proto(export_scope=None)

Converts a ResourceVariable to a VariableDef protocol buffer.

Parameters

export_scope – Optional string. Name scope to remove.

Raises

RuntimeError – If run in EAGER mode.

Returns

A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property trainable
class zfit.core.parameter.BaseComposedParameter(*args, **kwargs)[source]
property floating
property params
value()[source]
read_value()[source]
property shape
numpy()[source]
property independent
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_defSaveSliceInfoDef 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__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__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
Raises

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

property aggregation
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

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_deltatf.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.

copy(deep=False, name=None, **overwrite_params)
Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: 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

The dtype of the object

Return type

DType

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

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

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=True)

DEPRECATED FUNCTION

Warning: 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 (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – 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 (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type
get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []
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)

Warning: 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.

instances = <_weakrefset.WeakSet object>
load(value, session=None)

Load new value into this variable. (deprecated)

Warning: 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 name
Return type

str

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.


>>> 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
reset_cache(reseter)
reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Parameters
• sparse_deltatf.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_deltatf.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_deltatf.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_deltatf.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_deltatf.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 Kth 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]) add = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

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 Kth 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]) op = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

The resulting update to v 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.

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 Kth 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]) op = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

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_deltatf.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_deltatf.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.

property synchronization
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.

property trainable
class zfit.core.parameter.ConstantParameter(*args, **kwargs)[source]

Constant parameter. Value cannot change.

Parameters
• name

• value

• dtype

property shape
value()[source]
Return type

Tensor

read_value()[source]
Return type

Tensor

property floating
property independent
Return type

bool

property static_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_defSaveSliceInfoDef 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__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__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
Raises

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

property aggregation
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

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_deltatf.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.

copy(deep=False, name=None, **overwrite_params)
Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: 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

The dtype of the object

Return type

DType

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

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

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=True)

DEPRECATED FUNCTION

Warning: 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 (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – 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 (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type
get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []
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)

Warning: 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.

instances = <_weakrefset.WeakSet object>
load(value, session=None)

Load new value into this variable. (deprecated)

Warning: 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 name
Return type

str

property op

The Operation of this variable.

property params
Return type

~ParametersType

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.


>>> 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
reset_cache(reseter)
reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Parameters
• sparse_deltatf.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_deltatf.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_deltatf.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_deltatf.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_deltatf.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 Kth 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]) add = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

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 Kth 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]) op = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

The resulting update to v 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.

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 Kth 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]) op = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

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_deltatf.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_deltatf.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.

property synchronization
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.

property trainable
class zfit.core.parameter.ComposedParameter(name, value_fn, params=<class 'zfit.util.checks.NotSpecified'>, dtype=tf.float64, dependents=<class 'zfit.util.checks.NotSpecified'>)[source]

Arbitrary composition of parameters.

A ComposedParameter allows for arbitrary combinations of parameters and correlations

Parameters
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_defSaveSliceInfoDef 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__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__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
Raises

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

property aggregation
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

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_deltatf.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.

copy(deep=False, name=None, **overwrite_params)
Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: 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

The dtype of the object

Return type

DType

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

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

property floating
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=True)

DEPRECATED FUNCTION

Warning: 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 (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – 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 (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type
get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []
property independent
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)

Warning: 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.

instances = <_weakrefset.WeakSet object>
load(value, session=None)

Load new value into this variable. (deprecated)

Warning: 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 name
Return type

str

numpy()
property op

The Operation of this variable.

property params
read_value()
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.


>>> 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
reset_cache(reseter)
reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Parameters
• sparse_deltatf.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_deltatf.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_deltatf.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_deltatf.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_deltatf.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 Kth 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]) add = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

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 Kth 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]) op = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

The resulting update to v 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.

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 Kth 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]) op = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

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_deltatf.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_deltatf.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.

property 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.

property synchronization
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.

property trainable
value()
class zfit.core.parameter.ComplexParameter(*args, **kwargs)[source]

Create a complex parameter.

Note

Use the constructor class methods instead of the __init__() constructor:

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_defSaveSliceInfoDef 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__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__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
Raises

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

property aggregation
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

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_deltatf.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.

copy(deep=False, name=None, **overwrite_params)
Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: 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

The dtype of the object

Return type

DType

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

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

property floating
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=True)

DEPRECATED FUNCTION

Warning: 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 (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – 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 (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type
get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []
property independent
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)

Warning: 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.

instances = <_weakrefset.WeakSet object>
load(value, session=None)

Load new value into this variable. (deprecated)

Warning: 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 name
Return type

str

numpy()
property op

The Operation of this variable.

property params
read_value()
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.


>>> 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
reset_cache(reseter)
reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Parameters
• sparse_deltatf.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_deltatf.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_deltatf.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_deltatf.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_deltatf.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 Kth 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]) add = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

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 Kth 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]) op = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

The resulting update to v 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.

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 Kth 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]) op = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

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_deltatf.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_deltatf.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.

property 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.

property synchronization
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.

property trainable
value()
classmethod from_cartesian(name, real, imag, dtype=tf.complex128, floating=True)[source]

Create a complex parameter from cartesian coordinates.

Parameters
• name – Name of the parameter.

• real – Real part of the complex number.

• imag – Imaginary part of the complex number.

classmethod from_polar(name, mod, arg, dtype=tf.complex128, floating=True, **kwargs)[source]

Create a complex parameter from polar coordinates.

Parameters
• name – Name of the parameter.

• real – Modulus (r) the complex number.

• imag – Argument (phi) of the complex number.

property conj

Returns a complex conjugated copy of the complex parameter.

property real

Real part of the complex parameter.

property imag

Imaginary part of the complex parameter.

property mod

Modulus (r) of the complex parameter.

property arg

Argument (phi) of the complex parameter.

zfit.core.parameter.get_auto_number()[source]
zfit.core.parameter.convert_to_parameter(value, name=None, prefer_constant=True, dependents=None)[source]

Convert a numerical to a constant/floating parameter or return if already a parameter.

Parameters
• value

• name

• prefer_constant – If True, create a ConstantParameter instead of a Parameter, if possible.

Return type

ZfitParameter

zfit.core.parameter.set_values`(params, values)[source]

Set the values (using a context manager or not) of multiple parameters.

Parameters

Returns: