def Linear(shape, _inf, bias=True, init=_default_initializer, init_bias=0, input_rank=None, map_rank=None):
out_shape = _as_tuple(shape)
# TODO: implement the full semantics of the BrainScript code
#inputShape =
# if BS.Constants.IsNone (inputRank) then Inferred # not given: one Inferred, which will get expanded
# else if !BS.Constants.IsNone (mapRank) then Fail ("'inputRank' and 'mapRank' cannot be specified at the same time.")
# else Repeat (inputRank, Inferred)
#W = ParameterTensor {_ConcatArrays (outDim, inputShape), init=init, initValueScale=initValueScale}
#b = ParameterTensor {outDim, initValue=0}
#outputRank = Length (_AsArray (outDim)) # support outputs with tensor layouts
#inferInputRankToMap =
# if !BS.Constants.IsNone (inputRank) then -1 # means not specified
# else if BS.Constants.IsNone (mapRank) then 0 # default to 'use all input dims'
# else mapRank
#apply (x) =
# if bias
# then Times (W, x, outputRank=outputRank, inferInputRankToMap=inferInputRankToMap) + b
# else Times (W, x, outputRank=outputRank, inferInputRankToMap=inferInputRankToMap)
W = Parameter(_inf.shape + out_shape, init=init , name='W')
b = Parameter( out_shape, init=init_bias, name='b') if bias else None
x = Placeholder(_inf=_inf, name='linear_arg')
apply_x = Function.__matmul__(x, W) + b if bias else \
Function.__matmul__(x, W)
_name_and_extend_Function(apply_x, 'Linear')
return apply_x
def Embedding(shape, _inf, weights=None, init=_default_initializer, transpose=False):
shape = _as_tuple(shape)
full_shape = (shape + _inf.shape) if transpose else (_inf.shape + shape)
if weights is None: # no weights given: learn the embedding
E = Parameter(full_shape, init=init, name='E')
else: # weights given: use them as constant
UntestedBranchError("Embedding, from constant")
E = Constant(full_shape, init=weights, name='E') # TODO: can 'weights' be a CNTK object already? Then how to do this?
x = Placeholder(_inf=_inf, name='embedding_arg')
apply_x = Function.__matmul__(E, x) if transpose else \
Function.__matmul__(x, E) # x is expected to be sparse one-hot
_name_and_extend_Function(apply_x, 'Embedding')
return apply_x