83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
from typing import Callable, Optional, Tuple, cast
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from ..config import registry
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from ..initializers import glorot_uniform_init, zero_init
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from ..model import Model
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from ..types import Floats2d
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from ..util import get_width, partial
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from .chain import chain
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from .dropout import Dropout
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from .layernorm import LayerNorm
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InT = Floats2d
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OutT = Floats2d
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@registry.layers("Maxout.v1")
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def Maxout(
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nO: Optional[int] = None,
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nI: Optional[int] = None,
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nP: Optional[int] = 3,
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*,
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init_W: Optional[Callable] = None,
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init_b: Optional[Callable] = None,
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dropout: Optional[float] = None,
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normalize: bool = False,
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) -> Model[InT, OutT]:
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if init_W is None:
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init_W = glorot_uniform_init
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if init_b is None:
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init_b = zero_init
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model: Model[InT, OutT] = Model(
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"maxout",
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forward,
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init=partial(init, init_W, init_b),
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dims={"nO": nO, "nI": nI, "nP": nP},
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params={"W": None, "b": None},
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)
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if normalize:
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model = chain(model, LayerNorm(nI=nO))
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if dropout is not None:
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model = chain(model, cast(Model[InT, OutT], Dropout(dropout)))
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return model
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def forward(model: Model[InT, OutT], X: InT, is_train: bool) -> Tuple[OutT, Callable]:
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nO = model.get_dim("nO")
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nP = model.get_dim("nP")
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nI = model.get_dim("nI")
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b = model.get_param("b")
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W = model.get_param("W")
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W = model.ops.reshape2f(W, nO * nP, nI)
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Y = model.ops.gemm(X, W, trans2=True)
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Y += model.ops.reshape1f(b, nO * nP)
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Z = model.ops.reshape3f(Y, Y.shape[0], nO, nP)
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best, which = model.ops.maxout(Z)
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def backprop(d_best: OutT) -> InT:
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dZ = model.ops.backprop_maxout(d_best, which, nP)
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# TODO: Add sum methods for Floats3d
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model.inc_grad("b", dZ.sum(axis=0)) # type: ignore[call-overload]
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dY = model.ops.reshape2f(dZ, dZ.shape[0], nO * nP)
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dW = model.ops.reshape3f(model.ops.gemm(dY, X, trans1=True), nO, nP, nI)
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model.inc_grad("W", dW)
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return model.ops.gemm(dY, model.ops.reshape2f(W, nO * nP, nI))
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return best, backprop
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def init(
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init_W: Callable,
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init_b: Callable,
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model: Model[InT, OutT],
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X: Optional[InT] = None,
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Y: Optional[OutT] = None,
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) -> None:
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if X is not None:
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model.set_dim("nI", get_width(X))
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if Y is not None:
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model.set_dim("nO", get_width(Y))
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W_shape = (model.get_dim("nO"), model.get_dim("nP"), model.get_dim("nI"))
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model.set_param("W", init_W(model.ops, W_shape))
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model.set_param("b", init_b(model.ops, (model.get_dim("nO"), model.get_dim("nP"))))
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