analogvnn.graph.ModelGraphState#

Module Contents#

Classes#

ModelGraphState

The state of a model graph.
class analogvnn.graph.ModelGraphState.ModelGraphState(use_autograd_graph: bool = False, allow_loops=False)[source]#

The state of a model graph.

Variables:

  • allow_loops (bool) – if True, the graph is allowed to contain loops.

  • forward_input_output_graph (Optional[Dict[GRAPH_NODE_TYPE, InputOutput]]) – the input and output of the

  • pass. (forward) –

  • use_autograd_graph (bool) – if True, the autograd graph is used to calculate the gradients.

  • _loss (Tensor) – the loss.

  • INPUT (GraphEnum) – GraphEnum.INPUT

  • OUTPUT (GraphEnum) – GraphEnum.OUTPUT

  • STOP (GraphEnum) – GraphEnum.STOP

Properties:

input (Tensor): the input of the forward pass. output (Tensor): the output of the forward pass. loss (Tensor): the loss.

property inputs: Optional[analogvnn.graph.ArgsKwargs.ArgsKwargs][source]#

Get the inputs.

Returns:

the inputs.

Return type:

ArgsKwargs

property outputs: Optional[analogvnn.graph.ArgsKwargs.ArgsKwargs][source]#

Get the output.

Returns:

the output.

Return type:

ArgsKwargs

property loss[source]#

Get the loss.

Returns:

the loss.

Return type:

Tensor

allow_loops: bool[source]#
use_autograd_graph: bool[source]#
forward_input_output_graph: Optional[Dict[analogvnn.graph.GraphEnum.GRAPH_NODE_TYPE, analogvnn.graph.ArgsKwargs.InputOutput]][source]#
_loss: Optional[torch.Tensor][source]#
INPUT[source]#
OUTPUT[source]#
STOP[source]#
ready_for_forward(exception: bool = False) → bool[source]#

Check if the state is ready for forward pass.

Parameters:

exception (bool) – If True, an exception is raised if the state is not ready for forward pass.

Returns:

True if the state is ready for forward pass.

Return type:

bool

Raises:

RuntimeError – If the state is not ready for forward pass and exception is True.

ready_for_backward(exception: bool = False) → bool[source]#

Check if the state is ready for backward pass.

Parameters:

exception (bool) – if True, raise an exception if the state is not ready for backward pass.

Returns:

True if the state is ready for backward pass.

Return type:

bool

Raises:

RuntimeError – if the state is not ready for backward pass and exception is True.

set_loss(loss: Union[torch.Tensor, None]) → ModelGraphState[source]#

Set the loss.

Parameters:

loss (Tensor) – the loss.

Returns:

self.

Return type:

ModelGraphState