torchdms.model.FullyConnected¶

class torchdms.model.FullyConnected(layer_sizes, activations, *args, beta_l1_coefficient=0.0, interaction_l1_coefficient=0.0, **kwargs)[source]¶

Bases: torchdms.model.TorchdmsModel

A flexible fully connected neural network model.

Todo

schematic image

Parameters

layer_sizes (List[int]) – Sequence of widths for each layer between input and output.
activations (List[Callable]) –
Corresponding activation functions for each layer. The first layer with None or nn.Identity() activation is the latent space.

Todo

allowable activation function names?
args – base positional arguments, see torchdms.model.TorchdmsModel
beta_l1_coefficient (float) – lasso penalty on latent space \(\beta\) coefficients
interaction_l1_coefficient (float) – lasso penalty on parameters in the pre-latent interaction layer(s)
kwargs – base keyword arguments, see torchdms.model.TorchdmsModel

Example

With layer_sizes = [10, 2, 10, 10] and activations = ["relu", None, "relu", "relu"] we have a latent space of 2 nodes, feeding into two more dense layers, each with 10 nodes, before the output. Layers before the latent layer are a nonlinear module for site-wise interactions, in this case one layer of 10 nodes. The latent space has skip connections directly from the input layer, so we always model single mutation effects.

Methods

`add_module`	Adds a child module to the current module.
`apply`	Applies `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`beta_coefficients`	Beta coefficients (single mutant effects only, no interaction terms)
`bfloat16`	Casts all floating point parameters and buffers to `bfloat16` datatype.
`buffers`	Returns an iterator over module buffers.
`children`	Returns an iterator over immediate children modules.
`cpu`	Moves all model parameters and buffers to the CPU.
`cuda`	Moves all model parameters and buffers to the GPU.
`double`	Casts all floating point parameters and buffers to `double` datatype.
`eval`	Sets the module in evaluation mode.
`extra_repr`	Set the extra representation of the module
`fix_gauge`	Perform gauge-fixing procedure on latent space parameters.
`float`	Casts all floating point parameters and buffers to `float` datatype.
`forward`	data \(X\) \(\rightarrow\) output \(Y\).
`from_latent_to_output`	Evaluate the mapping from the latent space to output.
`get_buffer`	Returns the buffer given by `target` if it exists, otherwise throws an error.
`get_extra_state`	Returns any extra state to include in the module's state_dict.
`get_parameter`	Returns the parameter given by `target` if it exists, otherwise throws an error.
`get_submodule`	Returns the submodule given by `target` if it exists, otherwise throws an error.
`half`	Casts all floating point parameters and buffers to `half` datatype.
`load_state_dict`	Copies parameters and buffers from `state_dict` into this module and its descendants.
`modules`	Returns an iterator over all modules in the network.
`monotonic_params_from_latent_space`	Yields parameters to be floored to zero in a monotonic model.
`named_buffers`	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
`named_parameters`	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`numpy_single_mutant_predictions`	Single mutant predictions as a numpy array of shape (AAs, sites, outputs).
`parameters`	Returns an iterator over module parameters.
`randomize_parameters`	Randomize model parameters.
`register_backward_hook`	Registers a backward hook on the module.
`register_buffer`	Adds a buffer to the module.
`register_forward_hook`	Registers a forward hook on the module.
`register_forward_pre_hook`	Registers a forward pre-hook on the module.
`register_full_backward_hook`	Registers a backward hook on the module.
`register_parameter`	Adds a parameter to the module.
`regularization_loss`	Lasso penalty of single mutant effects and pre-latent interaction weights, a scalar-valued `torch.Tensor`
`requires_grad_`	Change if autograd should record operations on parameters in this module.
`seq_to_binary`	Takes a string of amino acids and creates an appropriate one-hot encoding.
`set_extra_state`	This function is called from `load_state_dict()` to handle any extra state found within the state_dict.
`set_require_grad_for_all_parameters`	Set `require_grad` for all parameters.
`share_memory`	See `torch.Tensor.share_memory_()`
`single_mutant_predictions`	Single mutant predictions as a list (across output dimensions) of Pandas dataframes.
`state_dict`	Returns a dictionary containing a whole state of the module.
`str_summary`	A one-line summary of the model.
`to`	Moves and/or casts the parameters and buffers.
`to_empty`	Moves the parameters and buffers to the specified device without copying storage.
`to_latent`	Latent space representation \(Z\)
`train`	Sets the module in training mode.
`type`	Casts all parameters and buffers to `dst_type`.
`xpu`	Moves all model parameters and buffers to the XPU.
`zero_grad`	Sets gradients of all model parameters to zero.

Attributes

`T_destination`	alias of TypeVar('T_destination', bound=`Mapping`[`str`, `torch.Tensor`])
`characteristics`	Salient characteristics of the model that aren't represented in the PyTorch description.
`dump_patches`	This allows better BC support for `load_state_dict()`.
`internal_layer_dimensions`	List of widths of internal layers.
`latent_dim`	Number of dimensions in latent space.
`sequence_length`	input amino acid sequence length

property characteristics: Dict¶

Salient characteristics of the model that aren’t represented in the PyTorch description.

Return type: Dict

property internal_layer_dimensions: List[int]¶

List of widths of internal layers.

Return type: List[int]

property latent_dim: int¶

Number of dimensions in latent space.

Return type: int

str_summary()[source]¶

A one-line summary of the model.

Return type: str

fix_gauge(gauge_mask)[source]¶

Perform gauge-fixing procedure on latent space parameters.

Parameters: gauge_mask – 0/1 mask array the same shape as latent space input with 1s for parameters that should be zeroed

to_latent(x, **kwargs)[source]¶

Latent space representation \(Z\)

\[z \equiv \phi(x) \equiv \beta^\intercal x\]

Parameters: x (Tensor) – input data tensor \(X\)
Return type: Tensor

from_latent_to_output(z, **kwargs)[source]¶

Evaluate the mapping from the latent space to output.

\[y = g(z)\]

Parameters: z (Tensor) – latent space representation
Return type: Tensor

beta_coefficients()[source]¶

Beta coefficients (single mutant effects only, no interaction terms)

Return type: Tensor

regularization_loss()[source]¶

Lasso penalty of single mutant effects and pre-latent interaction weights, a scalar-valued torch.Tensor

Return type: Tensor

__call__(*input, **kwargs)¶: Call self as a function.

add_module(name, module)¶

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters

name (string) – name of the child module. The child module can be accessed from this module using the given name
module (Module) – child module to be added to the module.

Return type

None

apply(fn)¶

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also torch.nn.init).

Parameters: fn (Module -> None) – function to be applied to each submodule
Returns: self
Return type: Module

Example:

>>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)

bfloat16()¶

Casts all floating point parameters and buffers to bfloat16 datatype.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

buffers(recurse=True)¶

Returns an iterator over module buffers.

Parameters: recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields: torch.Tensor – module buffer

Example:

>>> for buf in model.buffers():
>>>     print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

Return type: Iterator[Tensor]

children()¶

Returns an iterator over immediate children modules.

Yields: Module – a child module
Return type: Iterator[Module]

cpu()¶

Moves all model parameters and buffers to the CPU.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

cuda(device=None)¶

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Note

This method modifies the module in-place.

Parameters: device (int, optional) – if specified, all parameters will be copied to that device
Returns: self
Return type: Module

double()¶

Casts all floating point parameters and buffers to double datatype.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

eval()¶

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

This is equivalent with self.train(False).

See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.

Returns: self
Return type: Module

extra_repr()¶

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

Return type: str

float()¶

Casts all floating point parameters and buffers to float datatype.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

forward(x, **kwargs)¶

data \(X\) \(\rightarrow\) output \(Y\).

\[y = g(\phi(x))\]

Parameters: x (Tensor) – input data tensor \(X\)
Return type: Tensor

get_buffer(target)¶

Returns the buffer given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

Parameters: target (str) – The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify a fully-qualified string.)
Returns: The buffer referenced by target
Return type: torch.Tensor
Raises: AttributeError – If the target string references an invalid path or resolves to something that is not a buffer

get_extra_state()¶

Returns any extra state to include in the module’s state_dict. Implement this and a corresponding set_extra_state() for your module if you need to store extra state. This function is called when building the module’s state_dict().

Note that extra state should be pickleable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.

Returns: Any extra state to store in the module’s state_dict
Return type: object

get_parameter(target)¶

Returns the parameter given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

Parameters: target (str) – The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify a fully-qualified string.)
Returns: The Parameter referenced by target
Return type: torch.nn.Parameter
Raises: AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Parameter

get_submodule(target)¶

Returns the submodule given by target if it exists, otherwise throws an error.

For example, let’s say you have an nn.Module A that looks like this:

(The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.)

To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv").

The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, get_submodule should always be used.

Parameters: target (str) – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
Returns: The submodule referenced by target
Return type: torch.nn.Module
Raises: AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Module

half()¶

Casts all floating point parameters and buffers to half datatype.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

load_state_dict(state_dict, strict=True)¶

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters

state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns

missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys

Return type

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

modules()¶

Returns an iterator over all modules in the network.

Yields: Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
        print(idx, '->', m)

0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)

Return type: Iterator[Module]

monotonic_params_from_latent_space()¶

Yields parameters to be floored to zero in a monotonic model. This is every parameter after the latent space excluding bias parameters.

We follow the convention that the latent layers of a network are named like latent_layer* and the weights and bias are denoted layer_name*.weight and layer_name*.bias.

Layers from nested modules will be prefixed (e.g. with Independent).

Return type: Generator[Tensor, None, None]

named_buffers(prefix='', recurse=True)¶

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters

prefix (str) – prefix to prepend to all buffer names.
recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.

Yields

(string, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> for name, buf in self.named_buffers():
>>>    if name in ['running_var']:
>>>        print(buf.size())

Return type: Iterator[Tuple[str, Tensor]]

named_children()¶

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields: (string, Module) – Tuple containing a name and child module

Example:

>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)

Return type: Iterator[Tuple[str, Module]]

named_modules(memo=None, prefix='', remove_duplicate=True)¶

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Parameters

memo (Optional[Set[Module]]) – a memo to store the set of modules already added to the result
prefix (str) – a prefix that will be added to the name of the module
remove_duplicate (bool) – whether to remove the duplicated module instances in the result
not (or) –

Yields

(string, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
        print(idx, '->', m)

0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

named_parameters(prefix='', recurse=True)¶

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters

prefix (str) – prefix to prepend to all parameter names.
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

Yields

(string, Parameter) – Tuple containing the name and parameter

Example:

>>> for name, param in self.named_parameters():
>>>    if name in ['bias']:
>>>        print(param.size())

Return type: Iterator[Tuple[str, Parameter]]

numpy_single_mutant_predictions()¶

Single mutant predictions as a numpy array of shape (AAs, sites, outputs).

Return type: ndarray

parameters(recurse=True)¶

Returns an iterator over module parameters.

This is typically passed to an optimizer.

Parameters: recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
Yields: Parameter – module parameter

Example:

>>> for param in model.parameters():
>>>     print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

Return type: Iterator[Parameter]

randomize_parameters()¶: Randomize model parameters.

register_backward_hook(hook)¶

Registers a backward hook on the module.

This function is deprecated in favor of register_full_backward_hook() and the behavior of this function will change in future versions.

Returns: a handle that can be used to remove the added hook by calling handle.remove()
Return type: torch.utils.hooks.RemovableHandle

register_buffer(name, tensor, persistent=True)¶

Adds a buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’s state_dict.

Buffers can be accessed as attributes using given names.

Parameters

name (string) – name of the buffer. The buffer can be accessed from this module using the given name
tensor (Tensor or None) – buffer to be registered. If None, then operations that run on buffers, such as cuda, are ignored. If None, the buffer is not included in the module’s state_dict.
persistent (bool) – whether the buffer is part of this module’s state_dict.

Example:

>>> self.register_buffer('running_mean', torch.zeros(num_features))

Return type: None

register_forward_hook(hook)¶

Registers a forward hook on the module.

The hook will be called every time after forward() has computed an output. It should have the following signature:

hook(module, input, output) -> None or modified output

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called.

Returns: a handle that can be used to remove the added hook by calling handle.remove()
Return type: torch.utils.hooks.RemovableHandle

register_forward_pre_hook(hook)¶

Registers a forward pre-hook on the module.

The hook will be called every time before forward() is invoked. It should have the following signature:

hook(module, input) -> None or modified input

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple).

Returns: a handle that can be used to remove the added hook by calling handle.remove()
Return type: torch.utils.hooks.RemovableHandle

register_full_backward_hook(hook)¶

Registers a backward hook on the module.

The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and grad_output are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and grad_output will be None for all non-Tensor arguments.

For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module’s forward function.

Warning

Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.

Returns: a handle that can be used to remove the added hook by calling handle.remove()
Return type: torch.utils.hooks.RemovableHandle

register_parameter(name, param)¶

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters

name (string) – name of the parameter. The parameter can be accessed from this module using the given name
param (Parameter or None) – parameter to be added to the module. If None, then operations that run on parameters, such as cuda, are ignored. If None, the parameter is not included in the module’s state_dict.

Return type

None

requires_grad_(requires_grad=True)¶

Change if autograd should record operations on parameters in this module.

This method sets the parameters’ requires_grad attributes in-place.

This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).

See Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.

Parameters: requires_grad (bool) – whether autograd should record operations on parameters in this module. Default: True.
Returns: self
Return type: Module

seq_to_binary(seq)¶: Takes a string of amino acids and creates an appropriate one-hot encoding.

property sequence_length¶: input amino acid sequence length

set_extra_state(state)¶

This function is called from load_state_dict() to handle any extra state found within the state_dict. Implement this function and a corresponding get_extra_state() for your module if you need to store extra state within its state_dict.

Parameters: state (dict) – Extra state from the state_dict

set_require_grad_for_all_parameters(value)¶

Set require_grad for all parameters.

Parameters: value (bool) – require_grad=value for all parameters

share_memory()¶

See torch.Tensor.share_memory_()

Return type: ~T

single_mutant_predictions()¶

Single mutant predictions as a list (across output dimensions) of Pandas dataframes.

Return type: List[DataFrame]

state_dict(destination=None, prefix='', keep_vars=False)¶

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Returns: a dictionary containing a whole state of the module
Return type: dict

Example:

>>> module.state_dict().keys()
['bias', 'weight']

to(*args, **kwargs)¶

Moves and/or casts the parameters and buffers.

This can be called as

to(device=None, dtype=None, non_blocking=False)

to(dtype, non_blocking=False)

to(tensor, non_blocking=False)

to(memory_format=torch.channels_last)

Its signature is similar to torch.Tensor.to(), but only accepts floating point or complex dtypes. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters

device (torch.device) – the desired device of the parameters and buffers in this module
dtype (torch.dtype) – the desired floating point or complex dtype of the parameters and buffers in this module
tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (torch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)

Returns

self

Return type

Module

Examples:

>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)

>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)

to_empty(*, device)¶

Moves the parameters and buffers to the specified device without copying storage.

Parameters: device (torch.device) – The desired device of the parameters and buffers in this module.
Returns: self
Return type: Module

train(mode=True)¶

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters: mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns: self
Return type: Module

type(dst_type)¶

Casts all parameters and buffers to dst_type.

Note

This method modifies the module in-place.

Parameters: dst_type (type or string) – the desired type
Returns: self
Return type: Module

xpu(device=None)¶

Moves all model parameters and buffers to the XPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.

Note

This method modifies the module in-place.

Parameters: device (int, optional) – if specified, all parameters will be copied to that device
Returns: self
Return type: Module

zero_grad(set_to_none=False)¶

Sets gradients of all model parameters to zero. See similar function under torch.optim.Optimizer for more context.

Parameters: set_to_none (bool) – instead of setting to zero, set the grads to None. See torch.optim.Optimizer.zero_grad() for details.
Return type: None