Define Parameter which holds the value.
zfit.core.parameter.
MetaBaseParameter
Bases: tensorflow.python.ops.variables.VariableMetaclass, abc.ABCMeta
tensorflow.python.ops.variables.VariableMetaclass
abc.ABCMeta
__instancecheck__
Override for isinstance(instance, cls).
__subclasscheck__
Override for issubclass(subclass, cls).
mro
Return a type’s method resolution order.
register
Register a virtual subclass of an ABC.
Returns the subclass, to allow usage as a class decorator.
register_tensor_conversion
OverloadableMixin
Bases: zfit.core.interfaces.ZfitParameter
zfit.core.interfaces.ZfitParameter
dtype
The DType of Tensor`s handled by this `model.
DType
floating
bool
get_cache_deps
OrderedSet
get_dependencies
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).
get_params
Recursively collect parameters that this object depends on according to the filter criteria.
parameters that are fixed.
True: only return parameters that fulfil this criterion
only parameters that are not floating.
floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True
Optional
floating()
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.
Set[ZfitParameter]
Set
ZfitParameter
independent
name
str
params
~ParametersType
read_value
Tensor
shape
value
WrappedVariable
Bases: object
object
constraint
read_valu
numpy
assign
BaseParameter
Bases: tensorflow.python.ops.variables.Variable, zfit.core.interfaces.ZfitParameter, tensorflow.python.types.core.Tensor
tensorflow.python.ops.variables.Variable
tensorflow.python.types.core.Tensor
SaveSliceInfo
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.
full_name – Name of the full variable of which this Variable is a slice.
full_shape – Shape of the full variable, as a list of int.
var_offset – Offset of this Variable into the full variable, as a list of int.
var_shape – Shape of this Variable, as a list of int.
save_slice_info_def – SaveSliceInfoDef protocol buffer. If not None, recreates the SaveSliceInfo object its contents. save_slice_info_def and other arguments are mutually exclusive.
import_scope – Optional string. Name scope to add. Only used when initializing from protocol buffer.
spec
Computes the spec string used for saving.
to_proto
Returns a SaveSliceInfoDef() proto.
export_scope – Optional string. Name scope to remove.
A SaveSliceInfoDef protocol buffer, or None if the Variable is not in the specified name scope.
__eq__
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.
TypeError – when invoked.
__ne__
aggregation
Assigns a new value to the variable.
This is essentially a shortcut for assign(self, value).
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.
The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.
assign_add
Adds a value to this variable.
This is essentially a shortcut for assign_add(self, delta).
delta – A Tensor. The value to add to this variable.
use_locking – If True, use locking during the operation.
assign_sub
Subtracts a value from this variable.
This is essentially a shortcut for assign_sub(self, delta).
delta – A Tensor. The value to subtract from this variable.
batch_scatter_update
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:
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.
sparse_delta – tf.IndexedSlices to be assigned to this variable.
name – the name of the operation.
The updated variable.
TypeError – if sparse_delta is not an IndexedSlices.
Returns the constraint function associated with this variable.
The constraint function that was passed to the variable constructor. Can be None if no constraint was passed.
count_up_to
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).
limit – value at which incrementing the variable raises an error.
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.
device
The device of this variable.
The DType of this variable.
eval
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()
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())
```
session – The session to use to evaluate this variable. If none, the default session is used.
A numpy ndarray with a copy of the value of this variable.
experimental_ref
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.
from_proto
Returns a Variable object created from variable_def.
gather_nd
Gather slices from params into a Tensor with shape specified by indices.
See tf.gather_nd for details.
indices – A Tensor. Must be one of the following types: int32, int64. Index tensor.
name – A name for the operation (optional).
A Tensor. Has the same type as params.
get_shape
Alias of Variable.shape.
graph
The Graph of this variable.
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.
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) `
A Tensor holding the value of this variable after its initializer has run.
initializer
The initializer operation for this variable.
load
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.
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]
value – New variable value
ValueError – Session is not passed and no default session
The name of this variable.
op
The Operation of this variable.
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.
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.
Instead, we can use variable.ref().
>>> variable_set = {x.ref(), y.ref(), z.ref()} >>> x.ref() in variable_set True >>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'} >>> variable_dict[y.ref()] 'ten'
Also, the reference object provides .deref() function that returns the original Variable.
>>> x = tf.Variable(5) >>> x.ref().deref() <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>
scatter_add
Adds tf.IndexedSlices to this variable.
sparse_delta – tf.IndexedSlices to be added to this variable.
scatter_div
Divide this variable by tf.IndexedSlices.
sparse_delta – tf.IndexedSlices to divide this variable by.
scatter_max
Updates this variable with the max of tf.IndexedSlices and itself.
sparse_delta – tf.IndexedSlices to use as an argument of max with this variable.
scatter_min
Updates this variable with the min of tf.IndexedSlices and itself.
sparse_delta – tf.IndexedSlices to use as an argument of min with this variable.
scatter_mul
Multiply this variable by tf.IndexedSlices.
sparse_delta – tf.IndexedSlices to multiply this variable by.
scatter_nd_add
Applies sparse addition to individual values or slices in a Variable.
The Variable has rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.
The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.
updates is Tensor of rank Q-1+P-K with shape:
` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
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:
print sess.run(add)
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.
indices – The indices to be used in the operation.
updates – The values to be used in the operation.
scatter_nd_sub
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.
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)
[1, -9, 3, -6, -6, 6, 7, -4]
scatter_nd_update
Applies sparse assignment to individual values or slices in a Variable.
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:
[1, 11, 3, 10, 9, 6, 7, 12]
scatter_sub
Subtracts tf.IndexedSlices from this variable.
sparse_delta – tf.IndexedSlices to be subtracted from this variable.
scatter_update
Assigns tf.IndexedSlices to this variable.
set_shape
Overrides the shape for this variable.
shape – the TensorShape representing the overridden shape.
The TensorShape of this variable.
A TensorShape.
sparse_read
Gather slices from params axis axis according to indices.
This function supports a subset of tf.gather, see tf.gather for details on usage.
indices – The index Tensor. Must be one of the following types: int32, int64. Must be in range [0, params.shape[axis]).
synchronization
Converts a Variable to a VariableDef protocol buffer.
A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.
trainable
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.
ZfitParameterMixin
Bases: zfit.core.baseobject.BaseNumeric
zfit.core.baseobject.BaseNumeric
add_cache_deps
Add dependencies that render the cache invalid if they change.
cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –
Union
ForwardRef
Iterable
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.
TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
copy
ZfitObject
The dtype of the object
Return a set of all independent Parameter that this object depends on.
Parameter
only_floating (bool) – If True, only return floating Parameter
graph_caching_methods
instances
register_cacher
Register a cacher that caches values produces by this instance; a dependent.
cacher (Union[ForwardRef, Iterable[ForwardRef]]) –
reset_cache
reset_cache_self
Clear the cache of self and all dependent cachers.
TFBaseVariable
Bases: tensorflow.python.ops.resource_variable_ops.ResourceVariable
tensorflow.python.ops.resource_variable_ops.ResourceVariable
Creates a variable.
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_def – VariableDef 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.
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
Assigns a new value to this variable.
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.
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.
name – The name to use for the operation.
create
The op responsible for initializing this variable.
The device this variable is on.
The dtype of this variable.
Evaluates and returns the value of this variable.
Reads the value of this variable sparsely, using gather_nd.
handle
The handle by which this variable can be accessed.
is_initialized
Checks whether a resource variable has been initialized.
Outputs boolean scalar indicating whether the tensor has been initialized.
A Tensor of type bool.
The name of the handle for this variable.
The op for this variable.
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.
the read operation.
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.
` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `
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:
scatter_nd_max
scatter_nd_min
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:
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:
The shape of this variable.
Reads the value of this variable sparsely, using gather.
Converts a ResourceVariable to a VariableDef protocol buffer.
RuntimeError – If run in EAGER mode.
A cached operation which reads the value of this variable.
Bases: zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.TFBaseVariable, zfit.core.parameter.BaseParameter, zfit.core.interfaces.ZfitIndependentParameter
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 (str) – name of the parameter
value (Union[int, float, complex, Tensor, ZfitParameter]) – starting value
int
float
complex
lower_limit (Union[int, float, complex, Tensor, ZfitParameter, None]) – lower limit
None
upper_limit (Union[int, float, complex, Tensor, ZfitParameter, None]) – upper limit
step_size (Union[int, float, complex, Tensor, ZfitParameter, None]) – step size
DEFAULT_STEP_SIZE
lower
upper
has_limits
If the parameter has limits set or not.
at_limit
If the value is at the limit (or over it).
The precision is up to 1e-5 relative.
Boolean tf.Tensor that tells whether the value is at the limits.
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.
The step size
set_value
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.
value (Union[int, float, complex, Tensor, ZfitParameter]) – The value the parameter will take on.
randomize
Update the parameter with a randomised value between minval and maxval and return it.
minval (Union[int, float, complex, Tensor, ZfitParameter, None]) – The lower bound of the sampler. If not given, lower_limit is used.
maxval (Union[int, float, complex, Tensor, ZfitParameter, None]) – The upper bound of the sampler. If not given, upper_limit is used.
sampler (Callable) – A sampler with the same interface as np.random.uniform
Callable
The sampled value
lower_limit
upper_limit
BaseComposedParameter
Bases: zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.OverloadableMixin, zfit.core.parameter.BaseParameter
zfit.core.parameter.OverloadableMixin
ConstantParameter
Bases: zfit.core.parameter.OverloadableMixin, zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.BaseParameter
Constant parameter. Value cannot change.
name –
value –
dtype –
static_value
ComposedParameter
Bases: zfit.core.parameter.BaseComposedParameter
zfit.core.parameter.BaseComposedParameter
Arbitrary composition of parameters.
A ComposedParameter allows for arbitrary combinations of parameters and correlations
name (str) – Unique name of the Parameter
value_fn (Callable) – Function that returns the value of the composed parameter and takes as arguments params as arguments.
params (Union[Dict[str, ZfitParameter], Iterable[ZfitParameter], ZfitParameter]) – If it is a dict, this will direclty be used as the params attribute, otherwise the parameters will be automatically named with f”param_{i}”. The values act as arguments to value_fn.
Dict
dtype (DType) – Output of value_fn dtype
dependents (Union[Dict[str, ZfitParameter], Iterable[ZfitParameter], ZfitParameter]) –
Deprecated since version unknown: use params instead.
ComplexParameter
Bases: zfit.core.parameter.ComposedParameter
zfit.core.parameter.ComposedParameter
Create a complex parameter.
Note
Use the constructor class methods instead of the __init__() constructor:
ComplexParameter.from_cartesian()
ComplexParameter.from_polar()
from_cartesian
Create a complex parameter from cartesian coordinates.
name – Name of the parameter.
real – Real part of the complex number.
imag – Imaginary part of the complex number.
from_polar
Create a complex parameter from polar coordinates.
real – Modulus (r) the complex number.
imag – Argument (phi) of the complex number.
conj
Returns a complex conjugated copy of the complex parameter.
real
Real part of the complex parameter.
imag
Imaginary part of the complex parameter.
mod
Modulus (r) of the complex parameter.
arg
Argument (phi) of the complex parameter.
get_auto_number
convert_to_parameter
Convert a numerical to a constant/floating parameter or return if already a parameter.
prefer_constant – If True, create a ConstantParameter instead of a Parameter, if possible.
set_values
Set the values (using a context manager or not) of multiple parameters.
params (Union[Parameter, Iterable[Parameter]]) – Parameters to set the values.
values (Union[int, float, complex, Tensor, ZfitParameter, Iterable[Union[int, float, complex, Tensor, ZfitParameter]], ZfitResult]) – List-like object that supports indexing.
ZfitResult
Returns: