zfit.param.
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
str
value_fn (Callable) – Function that returns the value of the composed parameter and takes as arguments params as arguments.
Callable
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.
Union
Dict
ZfitParameter
Iterable
dtype (DType) – Output of value_fn dtype
DType
dependents (Union[Dict[str, ZfitParameter], Iterable[ZfitParameter], ZfitParameter]) –
Deprecated since version unknown: use params instead.
SaveSliceInfo
Bases: object
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.
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.
__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__
Compares two variables element-wise for equality.
add_cache_deps
Add dependencies that render the cache invalid if they change.
cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –
ForwardRef
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.
bool
TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
assign
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.
constraint
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.
dtype
The dtype of the object
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
DEPRECATED FUNCTION
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_cache_deps
Return a set of all independent Parameter that this object depends on.
Parameter
only_floating (bool) – If True, only return floating Parameter
OrderedSet
get_dependencies
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
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
op
The Operation of this variable.
ref
Returns a hashable reference object to this Variable.
The primary use case for this API is to put variables in a set/dictionary. We can’t put variables in a set/dictionary as variable.__hash__() is no longer available starting Tensorflow 2.0.
The following will raise an exception starting 2.0
>>> x = tf.Variable(5) >>> y = tf.Variable(10) >>> z = tf.Variable(10) >>> variable_set = {x, y, z} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key. >>> variable_dict = {x: 'five', y: 'ten'} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
Instead, we can use variable.ref().
>>> variable_set = {x.ref(), y.ref(), z.ref()} >>> x.ref() in variable_set True >>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'} >>> variable_dict[y.ref()] 'ten'
Also, the reference object provides .deref() function that returns the original Variable.
>>> x = tf.Variable(5) >>> x.ref().deref() <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>
register_cacher
Register a cacher that caches values produces by this instance; a dependent.
cacher (Union[ForwardRef, Iterable[ForwardRef]]) –
reset_cache_self
Clear the cache of self and all dependent cachers.
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.
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]).
Converts a Variable to a VariableDef protocol buffer.
A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.