parameter

Define Parameter which holds the value.

class zfit.core.parameter.BaseComposedParameter(params, value_fn, name='BaseComposedParameter', **kwargs)[source]

Bases: zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.OverloadableMixin, zfit.core.parameter.BaseParameter

add_cache_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

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

Raises

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

copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
property dtype

The dtype of the object

property floating
get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

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

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

graph_caching_methods = []
property independent
instances = <_weakrefset.WeakSet object>
property name

The name of the object.

numpy()[source]
property params
read_value()[source]
register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

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

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

property shape
value()[source]
class zfit.core.parameter.BaseParameter[source]

Bases: zfit.core.interfaces.ZfitParameter

abstract property dtype

The DType of Tensor`s handled by this `model.

abstract property floating
abstract get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])
abstract get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]
abstract property independent
abstract property name
abstract property params
abstract read_value() → tensorflow.python.framework.ops.Tensor
property shape
abstract value() → tensorflow.python.framework.ops.Tensor
class zfit.core.parameter.ComplexParameter(name, value_fn, dependents, dtype=tf.complex128, **kwargs)[source]

Bases: zfit.core.parameter.ComposedParameter

add_cache_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

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

Raises

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

property arg
property conj
copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
property dtype

The dtype of the object

property floating
static from_cartesian(name, real, imag, dtype=tf.complex128, floating=True, **kwargs)[source]
static from_polar(name, mod, arg, dtype=tf.complex128, floating=True, **kwargs)[source]
get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

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

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

graph_caching_methods = []
property imag
property independent
instances = <_weakrefset.WeakSet object>
property mod
property name

The name of the object.

numpy()
property params
read_value()
property real
register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

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

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

property shape
value()
class zfit.core.parameter.ComposedParameter(name, value_fn, dependents, dtype=tf.float64, **kwargs)[source]

Bases: zfit.core.parameter.BaseComposedParameter

add_cache_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

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

Raises

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

copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
property dtype

The dtype of the object

property floating
get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

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

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

graph_caching_methods = []
property independent
instances = <_weakrefset.WeakSet object>
property name

The name of the object.

numpy()
property params
read_value()
register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

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

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

property shape
value()
class zfit.core.parameter.ConstantParameter(name, value, dtype=tf.float64)[source]

Bases: zfit.core.parameter.OverloadableMixin, zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.BaseParameter

add_cache_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

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

Raises

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

copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
property dtype

The dtype of the object

property floating
get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

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

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

graph_caching_methods = []
property independent
instances = <_weakrefset.WeakSet object>
property name

The name of the object.

property params
read_value() → tensorflow.python.framework.ops.Tensor[source]
register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

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

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

property shape
value() → tensorflow.python.framework.ops.Tensor[source]
class zfit.core.parameter.MetaBaseParameter[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.

class zfit.core.parameter.OverloadableMixin[source]

Bases: zfit.core.interfaces.ZfitParameter

abstract property dtype

The DType of Tensor`s handled by this `model.

abstract property floating
abstract get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])
abstract get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]
abstract property independent
abstract property name
abstract property params
abstract read_value() → tensorflow.python.framework.ops.Tensor
property shape
abstract value() → tensorflow.python.framework.ops.Tensor
class zfit.core.parameter.Parameter(name: str, value: Union[int, float, complex, tensorflow.python.framework.ops.Tensor], lower_limit: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, upper_limit: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, step_size: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, floating: bool = True, dtype: tensorflow.python.framework.dtypes.DType = tf.float64, **kwargs)[source]

Bases: zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.TFBaseVariable, zfit.core.parameter.BaseParameter, zfit.core.interfaces.ZfitIndependentParameter

Class for fit parameters, derived from TF Variable class.

name : name of the parameter, value : starting value lower_limit : lower limit upper_limit : upper limit step_size : step size

DEFAULT_STEP_SIZE = 0.001
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_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

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

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.

property at_limit

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

The precision is up to 1e-5 relative.

Returns

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

Return type

tf.Tensor

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

Assigns tf.IndexedSlices to this variable batch-wise.

Analogous to batch_gather. This assumes that this variable and the sparse_delta IndexedSlices have a series of leading dimensions that are the same for all of them, and the updates are performed on the last dimension of indices. In other words, the dimensions should be the following:

num_prefix_dims = sparse_delta.indices.ndims - 1 batch_dim = num_prefix_dims + 1 `sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[

batch_dim:]`

where

sparse_delta.updates.shape[:num_prefix_dims] == sparse_delta.indices.shape[:num_prefix_dims] == var.shape[:num_prefix_dims]

And the operation performed can be expressed as:

`var[i_1, …, i_n,
sparse_delta.indices[i_1, …, i_n, j]] = sparse_delta.updates[

i_1, …, i_n, j]`

When sparse_delta.indices is a 1D tensor, this operation is equivalent to scatter_update.

To avoid this operation one can looping over the first ndims of the variable and using scatter_update on the subtensors that result of slicing the first dimension. This is a valid option for ndims = 1, but less efficient than this implementation.

Parameters
  • sparse_deltatf.IndexedSlices to be assigned to this variable.

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

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.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

eval(session=None)

Evaluates and returns the value of this variable.

experimental_ref()

Returns a hashable reference object to this Variable.

Warning: Experimental API that could be changed or removed.

The primary usecase 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.

```python import tensorflow as tf

x = tf.Variable(5) y = tf.Variable(10) z = tf.Variable(10)

# The followings will raise an exception starting 2.0 # TypeError: Variable is unhashable if Variable equality is enabled. variable_set = {x, y, z} variable_dict = {x: ‘five’, y: ‘ten’} ```

Instead, we can use variable.experimental_ref().

```python variable_set = {x.experimental_ref(),

y.experimental_ref(), z.experimental_ref()}

print(x.experimental_ref() in variable_set) ==> True

variable_dict = {x.experimental_ref(): ‘five’,

y.experimental_ref(): ‘ten’, z.experimental_ref(): ‘ten’}

print(variable_dict[y.experimental_ref()]) ==> ten ```

Also, the reference object provides .deref() function that returns the original Variable.

`python x = tf.Variable(5) print(x.experimental_ref().deref()) ==> <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5> `

property floating
static from_proto(variable_def, import_scope=None)

Returns a Variable object created from variable_def.

gather_nd(indices, name=None)

Reads the value of this variable sparsely, using gather_nd.

get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

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

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

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 has_limits

If the parameter has limits set or not

Returns

bool

property independent
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 lower
property lower_limit
property name

The name of the object.

numpy()
property op

The op for this variable.

property params
randomize(minval: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, maxval: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, sampler: Callable = <function random_uniform>) → tensorflow.python.framework.ops.Tensor[source]

Update the parameter with a randomised value between minval and maxval and return it.

Parameters
  • minval (Numerical) – The lower bound of the sampler. If not given, lower_limit is used.

  • maxval (Numerical) – The upper bound of the sampler. If not given, upper_limit is used.

  • () (sampler) – A sampler with the same interface as tf.random.uniform

Returns

The sampled value

Return type

tf.Tensor

read_value()[source]

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

the read operation.

register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

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

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

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

Adds tf.IndexedSlices to this variable.

Parameters
  • sparse_deltatf.IndexedSlices to be added to this variable.

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

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered addition has completed.

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

A Tensor that will hold the new value of this variable after the scattered division has completed.

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

A Tensor that will hold the new value of this variable after the scattered maximization has completed.

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

A Tensor that will hold the new value of this variable after the scattered minimization has completed.

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

A Tensor that will hold the new value of this variable after the scattered multiplication has completed.

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 K`th 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:

print sess.run(add)

```

The resulting update to ref would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters
  • indices – The indices to be used in the operation.

  • updates – The values to be used in the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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 K`th 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

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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 K`th 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

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

set_shape(shape)

Unsupported.

set_value(value: Union[int, float, complex, tensorflow.python.framework.ops.Tensor])[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 (float) – The value the parameter will take on.

property shape

The shape of this variable.

sparse_read(indices, name=None)

Reads the value of this variable sparsely, using gather.

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.

Returns

the step size

Return type

tf.Tensor

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
property upper
property upper_limit
value()[source]

A cached operation which reads the value of this variable.

class zfit.core.parameter.TFBaseVariable(initial_value=None, trainable=None, collections=None, validate_shape=True, caching_device=None, name=None, dtype=None, variable_def=None, import_scope=None, constraint=None, distribute_strategy=None, synchronization=None, aggregation=None, shape=None)[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

sparse_delta.updates.shape[:num_prefix_dims] == sparse_delta.indices.shape[:num_prefix_dims] == var.shape[:num_prefix_dims]

And the operation performed can be expressed as:

`var[i_1, …, i_n,
sparse_delta.indices[i_1, …, i_n, j]] = sparse_delta.updates[

i_1, …, i_n, j]`

When sparse_delta.indices is a 1D tensor, this operation is equivalent to scatter_update.

To avoid this operation one can looping over the first ndims of the variable and using scatter_update on the subtensors that result of slicing the first dimension. This is a valid option for ndims = 1, but less efficient than this implementation.

Parameters
  • sparse_deltatf.IndexedSlices to be assigned to this variable.

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

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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()

Returns a hashable reference object to this Variable.

Warning: Experimental API that could be changed or removed.

The primary usecase 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.

```python import tensorflow as tf

x = tf.Variable(5) y = tf.Variable(10) z = tf.Variable(10)

# The followings will raise an exception starting 2.0 # TypeError: Variable is unhashable if Variable equality is enabled. variable_set = {x, y, z} variable_dict = {x: ‘five’, y: ‘ten’} ```

Instead, we can use variable.experimental_ref().

```python variable_set = {x.experimental_ref(),

y.experimental_ref(), z.experimental_ref()}

print(x.experimental_ref() in variable_set) ==> True

variable_dict = {x.experimental_ref(): ‘five’,

y.experimental_ref(): ‘ten’, z.experimental_ref(): ‘ten’}

print(variable_dict[y.experimental_ref()]) ==> ten ```

Also, the reference object provides .deref() function that returns the original Variable.

`python x = tf.Variable(5) print(x.experimental_ref().deref()) ==> <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5> `

static from_proto(variable_def, import_scope=None)

Returns a Variable object created from variable_def.

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

the read operation.

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

Adds tf.IndexedSlices to this variable.

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

A Tensor that will hold the new value of this variable after the scattered addition has completed.

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

A Tensor that will hold the new value of this variable after the scattered division has completed.

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

A Tensor that will hold the new value of this variable after the scattered maximization has completed.

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

A Tensor that will hold the new value of this variable after the scattered minimization has completed.

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

A Tensor that will hold the new value of this variable after the scattered multiplication has completed.

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 K`th 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:

print sess.run(add)

```

The resulting update to ref would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters
  • indices – The indices to be used in the operation.

  • updates – The values to be used in the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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 K`th 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

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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 K`th 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

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

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

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

set_shape(shape)

Unsupported.

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.WrappedVariable(initial_value, constraint, *args, **kwargs)[source]

Bases: object

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

Bases: zfit.core.baseobject.BaseNumeric

add_cache_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

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

Raises

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

copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
property dtype

The dtype of the object

get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

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

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

graph_caching_methods = []
instances = <_weakrefset.WeakSet object>
property name

The name of the object.

property params
register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

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

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

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

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

Parameters
  • () (name) –

  • ()

  • prefer_constant – If True, create a ConstantParameter instead of a Parameter _if possible_.

zfit.core.parameter.get_auto_number()[source]
zfit.core.parameter.register_tensor_conversion(convertable, overload_operators=True, priority=1)[source]
zfit.core.parameter.set_values(params: Union[zfit.core.parameter.Parameter, Iterable[zfit.core.parameter.Parameter]], values: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, Iterable[Union[int, float, complex, tensorflow.python.framework.ops.Tensor]], zfit.minimizers.interface.ZfitResult])[source]

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

Parameters
  • params – Parameters to set the values

  • values – list-like object that supports indexing

Returns: