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