returnn.tf.layers.variable

VariableLayer and related

class returnn.tf.layers.variable.VariableLayer(shape, dtype='float32', add_batch_axis=False, add_time_axis=False, trainable=True, saveable=True, non_critical_for_restore=False, init=None, init_by_layer=None, param_name=None, **kwargs)[source]

Represents a variable. Can add batch/time dimension if wanted. Can be trainable. See defaults.

Parameters:

  • shape (tuple[int|Dim]|list[int|Dim])

  • dtype (str)

  • add_batch_axis (bool)

  • add_time_axis (bool)

  • trainable (bool) – whether it is updated by grad descent

  • saveable (bool) – whether it is stored in the checkpoint

  • non_critical_for_restore (bool) – if True, and it cannot be found in a checkpoint, it will not be an error

  • init (str|float|int|None) – see returnn.tf.util.basic.get_initializer(). 0 by default. Alternatively, you can also use option init_by_layer.

  • init_by_layer (LayerBase|None)

  • param_name (str|None) – self.name (layer name) by default

layer_class: Optional[str] = 'variable'[source]
get_dep_layers()[source]
Return type:

list[LayerBase]

classmethod transform_config_dict(d, network, get_layer)[source]
Parameters:

  • d (dict[str]) – will modify inplace

  • network (returnn.tf.network.TFNetwork)

  • get_layer (((str) -> LayerBase)) – function to get or construct another layer

classmethod get_out_data_from_opts(name, network, shape, dtype='float32', add_batch_axis=False, add_time_axis=False, **kwargs)[source]
Parameters:
Return type:

Tensor

class returnn.tf.layers.variable.VariableAssignLayer(var: LayerBase, value: LayerBase, control_dependencies: Sequence[LayerBase] | None = None, op: str = 'assign', **kwargs)[source]

Assigns a new value to a variable.

Parameters:

  • var

  • value

  • control_dependencies

  • op – “assign” or “add”

layer_class: Optional[str] = 'variable_assign'[source]
get_dep_layers() → List[LayerBase][source]

deps

classmethod transform_config_dict(d, network, get_layer)[source]

transform

classmethod get_out_data_from_opts(name: str, var: LayerBase, **kwargs)[source]

out

class returnn.tf.layers.variable.VariableReadLayer(var: LayerBase, control_dependencies: Sequence[LayerBase] | None = None, **kwargs)[source]

Read a variable (currently expected from VariableLayer). Supports control dependencies to exactly specify when it should be read.

Parameters:

  • var – e.g. VariableLayer

  • control_dependencies – to control what ops must run before the var is read (e.g. assign ops)

layer_class: Optional[str] = 'variable_read'[source]
get_dep_layers() → List[LayerBase][source]

deps

classmethod transform_config_dict(d, network, get_layer)[source]

transform

classmethod get_out_data_from_opts(name: str, var: LayerBase, **kwargs)[source]

out