thinc の API リファレンス

thinc (explosion/thinc) の公開 API 101 件 —— クラス 22、関数 41、メソッド 38。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

種別	件数
クラス	22
関数	41
メソッド	38

API 一覧

classthinc.backends._param_server.ParamServer

Serve parameters for a single process.

functhinc.backends.get_current_ops() -> Ops

Get the current backend object.

functhinc.backends.get_ops(name:str, **kwargs) -> Ops

Get a backend object.

classthinc.backends.mps_ops.MPSOps

Ops class for Metal Performance shaders.

functhinc.backends.ops.gaussian_cdf(ops:Ops, X:FloatsXdT) -> FloatsXdT

Gaussian CDF for distribution with mean 0 and stdev 1.

functhinc.backends.ops.gaussian_pdf(ops:Ops, X:FloatsXdT) -> FloatsXdT

Gaussian PDF for distribution with mean 0 and stdev 1.

functhinc.backends.set_current_ops(ops:Ops) -> None

Change the current backend object.

functhinc.backends.set_gpu_allocator(allocator:str) -> None

Route GPU memory allocation via PyTorch or tensorflow.

functhinc.backends.use_pytorch_for_gpu_memory() -> None

Route GPU memory allocation via PyTorch.

functhinc.backends.use_tensorflow_for_gpu_memory() -> None

Route GPU memory allocation via TensorFlow.

methodthinc.config.registry.create(registry_name:str, entry_points:bool=False) -> None

Create a new custom registry.

functhinc.layers.array_getitem.array_getitem(index:Index) -> Model[ArrayTXd, ArrayTXd]

Index into input arrays, and return the subarrays.

functhinc.layers.array_getitem.floats_getitem(index:Index) -> Model[FloatsXd, FloatsXd]

Index into input arrays, and return the subarrays.

functhinc.layers.array_getitem.ints_getitem(index:Index) -> Model[IntsXd, IntsXd]

Index into input arrays, and return the subarrays.

functhinc.layers.bidirectional.bidirectional(l2r:Model[InT, OutT], r2l:Optional[Model[InT, OutT]]=None) -> Model[InT, OutT]

Stitch two RNN models into a bidirectional layer.

functhinc.layers.clone.clone(orig:Model[InT, OutT], n:int) -> Model[InT, OutT]

Construct `n` copies of a layer, with distinct weights.

functhinc.layers.map_list.map_list(layer:Model[InT, OutT]) -> Model[List[InT], List[OutT]]

Create a model that maps a child layer across list inputs.

func

thinc.layers.mish.Mish(nO:Optional[int]=None, nI:Optional[int]=None, *init_W:Optional[Callable]=None, *init_b:Optional[Callable]=None, *dropout:Optional[float]=None, *normalize:bool=False) -> Model[InT, OutT]

Dense layer with mish activation.

functhinc.layers.noop.noop(*layers:Model) -> Model[InOutT, InOutT]

Transform a sequences of layers into a null operation.

functhinc.layers.parametricattention.ParametricAttention(nO:Optional[int]=None) -> Model[InT, OutT]

Weight inputs by similarity to a learned vector

functhinc.layers.ragged2list.ragged2list() -> Model[InT, OutT]

Transform sequences from a ragged format into lists.

functhinc.layers.resizable.resizable(layer, resize_layer:Callable) -> Model[InT, OutT]

Container that holds one layer that can change dimensions.

functhinc.layers.with_reshape.with_reshape(layer:Model[OutT, OutT]) -> Model[InT, InT]

Reshape data on the way into and out from a layer.

classthinc.loss.Loss

Base class for classes computing the loss / gradient.

methodthinc.loss.Loss.get_grad(guesses:GuessT, truths:TruthT) -> GradT

Get the gradient of the loss.

classthinc.model.Model

Class for implementing Thinc models and layers.

methodthinc.model.Model.attrs() -> Dict[str, Any]

A dict of the model's attrs.

methodthinc.model.Model.can_from_bytes(bytes_data:bytes, *strict:bool=True) -> bool

Check whether the bytes data is compatible with the model.

methodthinc.model.Model.can_from_dict(msg:Dict, *strict:bool=True) -> bool

Check whether a dictionary is compatible with the model.

methodthinc.model.Model.copy() -> SelfT

Create a copy of the model, its attributes, and its parameters.

methodthinc.model.Model.dim_names() -> Tuple[str, ...]

Get the names of registered dimensions (including unset).

methodthinc.model.Model.finish_update(optimizer:Optimizer) -> None

Update parameters with current gradients.

methodthinc.model.Model.from_bytes(bytes_data:bytes) -> 'Model'

Deserialize the model from a bytes representation.

methodthinc.model.Model.from_disk(path:Union[Path, str]) -> 'Model'

Deserialize the model from disk.

methodthinc.model.Model.get_dim(name:str) -> int

Retrieve the value of a dimension of the given name.

methodthinc.model.Model.get_grad(name:str) -> FloatsXd

Get a gradient from the model.

methodthinc.model.Model.get_param(name:str) -> FloatsXd

Retrieve a weights parameter by name.

methodthinc.model.Model.get_ref(name:str) -> 'Model'

Retrieve the value of a reference of the given name.

methodthinc.model.Model.has_dim(name:str) -> Optional[bool]

Check whether the model has a dimension of a given name.

methodthinc.model.Model.has_grad(name:str) -> bool

Check whether the model has a non-zero gradient for a parameter.

methodthinc.model.Model.has_param(name:str) -> Optional[bool]

Check whether the model has a weights parameter of the given name.

methodthinc.model.Model.has_ref(name:str) -> Optional[bool]

Check whether the model has a reference of a given name.

methodthinc.model.Model.inc_grad(name:str, value:FloatsXd) -> None

Increment the gradient of a parameter by a value.

methodthinc.model.Model.layers() -> List['Model']

A list of child layers of the model.

methodthinc.model.Model.maybe_get_dim(name:str) -> Optional[int]

Retrieve the value of a dimension of the given name, or None.

methodthinc.model.Model.maybe_get_grad(name:str) -> Optional[FloatsXd]

Retrieve a gradient by name, or None.

methodthinc.model.Model.maybe_get_param(name:str) -> Optional[FloatsXd]

Retrieve a weights parameter by name, or None.

methodthinc.model.Model.maybe_get_ref(name:str) -> Optional['Model']

Retrieve the value of a reference if it exists, or None.

methodthinc.model.Model.param_names() -> Tuple[str, ...]

Get the names of registered parameter (including unset).

methodthinc.model.Model.remove_node(node:'Model') -> None

Remove a node from all layers lists, and then update references.

methodthinc.model.Model.replace_node(old:'Model', new:'Model') -> bool

Replace a node anywhere it occurs within the model.

methodthinc.model.Model.set_dim(name:str, value:int, *force:bool=False) -> None

Set a value for a dimension.

methodthinc.model.Model.set_grad(name:str, value:FloatsXd) -> None

Set a gradient value for the model.

methodthinc.model.Model.set_param(name:str, value:Optional[FloatsXd]) -> None

Set a weights parameter's value.

methodthinc.model.Model.set_ref(name:str, value:Optional['Model']) -> None

Set a value for a reference.

methodthinc.model.Model.to_bytes() -> bytes

Serialize the model to a bytes representation.

methodthinc.model.Model.to_cpu() -> None

Transfer the model to CPU.

methodthinc.model.Model.to_dict() -> Dict

Serialize the model to a dict representation.

methodthinc.model.Model.to_disk(path:Union[Path, str]) -> None

Serialize the model to disk.

methodthinc.model.Model.to_gpu(gpu_id:int) -> None

Transfer the model to a given GPU device.

methodthinc.model.Model.walk(*order:str='bfs') -> Iterable['Model']

Iterate out layers of the model.

functhinc.model.deserialize_attr(_:Any, value:Any, name:str, model:Model) -> Any

Deserialize an attribute value (defaults to msgpack).

functhinc.model.serialize_attr(_:Any, value:Any, name:str, model:Model) -> bytes

Serialize an attribute value (defaults to msgpack).

functhinc.model.set_dropout_rate(model:_ModelT, drop:float, attrs=['dropout_rate']) -> _ModelT

Walk over the model's nodes, setting the dropout rate.

functhinc.model.wrap_model_recursive(model:Model, wrapper:Callable[[Model], _ModelT]) -> _ModelT

Recursively wrap a model and its submodules.

functhinc.mypy.get_reducers_type(ctx:FunctionContext) -> Type

Determine a more specific model type for functions that combine models.

functhinc.schedules.compounding(start:float, stop:float, compound:float, *t:float=0.0) -> Iterable[float]

Yield an infinite series of compounding values.

functhinc.schedules.constant(rate:float) -> Iterable[float]

Yield a constant rate.

classthinc.shims.mxnet.MXNetShim

Interface between a MXNet model and a Thinc Model.

classthinc.shims.pytorch.PyTorchShim

Interface between a PyTorch model and a Thinc Model.

functhinc.shims.pytorch.default_serialize_torch_model(model:Any) -> bytes

Serializes the parameters of the wrapped PyTorch model to bytes.

classthinc.shims.pytorch_grad_scaler.PyTorchGradScaler

Gradient scaler for the PyTorch shim.

methodthinc.shims.pytorch_grad_scaler.PyTorchGradScaler.unscale(tensors)

Unscale the given tensors.

classthinc.shims.shim.Shim

Define a basic interface for external models.

classthinc.shims.tensorflow.TensorFlowShim

Interface between a TensorFlow model and a Thinc Model.

classthinc.shims.torchscript.TorchScriptShim

A Thinc shim that wraps a TorchScript module.

classthinc.types.Floats1d

1-dimensional array of floats.

classthinc.types.Floats2d

2-dimensional array of floats

classthinc.types.Floats3d

3-dimensional array of floats

classthinc.types.Floats4d

4-dimensional array of floats.

classthinc.types.Generator

Custom generator type.

classthinc.types.Ints1d

1-dimensional array of ints.

classthinc.types.Ints2d

2-dimensional array of ints.

classthinc.types.Ints3d

3-dimensional array of ints.

classthinc.types.Ints4d

4-dimensional array of ints.

classthinc.types.Padded

A batch of padded sequences, sorted by decreasing length.

classthinc.types.Unserializable

Wrap a value to prevent it from being serialized by msgpack.

classthinc.util.ArrayInfo

Container for info for checking array compatibility.

functhinc.util.assert_mxnet_installed() -> None

Raise an ImportError if MXNet is not installed.

functhinc.util.assert_pytorch_installed() -> None

Raise an ImportError if PyTorch is not installed.

functhinc.util.assert_tensorflow_installed() -> None

Raise an ImportError if TensorFlow is not installed.

functhinc.util.fix_random_seed(seed:int=0) -> None

Set the random seed across random, numpy.random and cupy.random.

functhinc.util.is_cupy_array(obj:Any) -> bool

Check whether an object is a cupy array.

functhinc.util.is_numpy_array(obj:Any) -> bool

Check whether an object is a numpy array.

functhinc.util.is_xp_array(obj:Any) -> bool

Check whether an object is a numpy or cupy array.

functhinc.util.mxnet2xp(mx_tensor:'mx.nd.NDArray', *ops:Optional['Ops']=None) -> ArrayXd

Convert a MXNet tensor to a numpy or cupy tensor.

functhinc.util.prefer_gpu(gpu_id:int=0) -> bool

Use GPU if it's available.

functhinc.util.require_cpu() -> bool

Use CPU through best available backend.

functhinc.util.set_active_gpu(gpu_id:int) -> 'cupy.cuda.Device'

Set the current GPU device for cupy and torch (if available).

functhinc.util.xp2mxnet(xp_tensor:ArrayXd, requires_grad:bool=False) -> 'mx.nd.NDArray'

Convert a numpy or cupy tensor to a MXNet tensor.

functhinc.util.xp2torch(xp_tensor:ArrayXd, requires_grad:bool=False, device:Optional['torch.device']=None) -> 'torch.Tensor'

Convert a numpy or cupy tensor to a PyTorch tensor.

この情報について

掲載しているシグネチャは explosion/thinc の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。詳しくは仕組みの解説をご覧ください。

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