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 Recurrence(over, _inf=None, go_backwards=False, initial_state=None):
# helper to compute previous value
# can take a single Variable/Function or a tuple
if go_backwards:
UntestedBranchError("Recurrence, go_backwards option")
def previous_hook(state):
if hasattr(state, 'outputs'):
outputs = state.outputs
if len(outputs) > 1: # if multiple then apply to each element
return tuple([previous_hook(s) for s in outputs])
# not a tuple: must be a 'scalar', i.e. a single element
return past_value (state, initial_state) if not go_backwards else \
future_value(state, initial_state)
x = Placeholder(_inf=_inf, name='recurrence_arg')
prev_state_forward = over.create_placeholder() # create a placeholder or a tuple of placeholders
f_x_h_c = over(x, prev_state_forward) # apply the recurrent over
# this returns a Function (x, (h_prev, c_prev)) -> (h, c)
h = f_x_h_c.outputs[0] # 'h' is a Variable (the output of a Function that computed it)
if _trace_layers:
_log_node(h)
_log_node(combine([h.owner]))
prev_state = previous_hook(f_x_h_c) # delay (h, c)
replacements = { value_forward: value.output for (value_forward, value) in zip(list(prev_state_forward), list(prev_state)) }
f_x_h_c.replace_placeholders(replacements) # binds _h_c := prev_state
apply_x = combine([h.owner]) # the Function that yielded 'h', so we get to know its inputs
# apply_x is a Function x -> h
_name_and_extend_Function(apply_x, 'Recurrence')
if _trace_layers:
_log_node(apply_x)
return apply_x
def Dense(shape, _inf, bias=True, init=_default_initializer, init_bias=0, input_rank=None, map_rank=None, activation=None):
if activation is None: # TODO: change default to identity once we no longer need _inf
activation = Identity(_inf=shape)
apply_x = Linear(shape, _inf, bias=bias, init=init, init_bias=init_bias, input_rank=input_rank, map_rank=map_rank) \
>> activation
# TODO: Any way to do some similar pattern ^^ without backslash?
_name_and_extend_Function(apply_x, 'Dense')
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
def Stabilizer(_inf, steepness=4):
# sharpened Softplus: 1/steepness ln(1+e^{steepness*beta})
# this behaves linear for weights around 1, yet guarantees positiveness
# parameters
param = Parameter((1), init=0.99537863, name='stabilizer_param') # 1/steepness*ln (e^steepness-1) for steepness==4
# TODO: compute this strange value directly in Python
# application
x = Placeholder(_inf=_inf, name='stabilizer_arg')
# TODO: risk of confusion; can these functions be namespaced?
beta = log (1 + exp (steepness * param)) / steepness
apply_x = beta * x
_name_and_extend_Function(apply_x, 'Stabilizer')
return apply_x
