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57 lines
1.7 KiB
57 lines
1.7 KiB
from typing import Callable, Optional, Tuple, cast
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from ..config import registry
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from ..model import Model
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from ..types import Floats1d, Floats2d
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from ..util import get_width
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InT = Floats2d
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OutT = Floats2d
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@registry.layers("MultiSoftmax.v1")
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def MultiSoftmax(nOs: Tuple[int, ...], nI: Optional[int] = None) -> Model[InT, OutT]:
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"""Neural network layer that predicts several multi-class attributes at once.
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For instance, we might predict one class with 6 variables, and another with 5.
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We predict the 11 neurons required for this, and then softmax them such
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that columns 0-6 make a probability distribution and columns 6-11 make another.
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"""
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return Model(
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"multisoftmax",
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forward,
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init=init,
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dims={"nO": sum(nOs), "nI": nI},
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attrs={"nOs": nOs},
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params={"W": None, "b": None},
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)
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def forward(model: Model[InT, OutT], X: InT, is_train: bool) -> Tuple[OutT, Callable]:
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nOs = model.attrs["nOs"]
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W = cast(Floats2d, model.get_param("W"))
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b = cast(Floats1d, model.get_param("b"))
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def backprop(dY: OutT) -> InT:
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model.inc_grad("W", model.ops.gemm(dY, X, trans1=True))
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model.inc_grad("b", dY.sum(axis=0))
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return model.ops.gemm(dY, W)
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Y = model.ops.gemm(X, W, trans2=True)
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Y += b
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i = 0
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for out_size in nOs:
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model.ops.softmax(Y[:, i : i + out_size], inplace=True)
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i += out_size
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return Y, backprop
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def init(
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model: Model[InT, OutT], X: Optional[InT] = None, 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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nO = model.get_dim("nO")
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nI = model.get_dim("nI")
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model.set_param("W", model.ops.alloc2f(nO, nI))
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model.set_param("b", model.ops.alloc1f(nO))
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