Commit a6e36694 authored by lr119628's avatar lr119628
Browse files

[update] new version

parent 2f3ed7d5
......@@ -26,8 +26,7 @@ class DefaultBlock(gluon.HybridBlock): = nn.Dense(units=units, activation=activation)
def hybrid_forward(self, F, x):
x =
return x
class CandidateHull(gluon.HybridBlock):
......@@ -38,22 +37,12 @@ class CandidateHull(gluon.HybridBlock):
stack: int,
name: str,
<#if outBlock.isArtificial()>
output_shape =<#list outBlock.outputTypes as type>(${tc.join(type.dimensions, ",")})</#list>,
output_shape =None,
<#if inBlock.isArtificial()>
input_shape =<#list inBlock.outputTypes as type>(${tc.join(type.dimensions, ",")})</#list>,
input_shape =None,
super(CandidateHull, self).__init__(**kwargs)
assert issubclass(output, gluon.HybridBlock), f'output should inherit from {gluon.HybridBlock} got {output}'
# assert issubclass(type(output), gluon.HybridBlock), f'output should inherit from {gluon.HybridBlock} got {type(output)}'
self.name_ = name
self.names = []
self.stack = stack
......@@ -70,8 +59,8 @@ class CandidateHull(gluon.HybridBlock):
self.body = body
if input:
self.input = input()
if inBlock:
self.input = inBlock()
self.input = None
if output:
......@@ -113,59 +102,17 @@ class CandidateHull(gluon.HybridBlock):
x = self.out(x)
return x
class AdaOut(gluon.HybridBlock):
def __init__(self, op_nums=1, classes=1, softmax=True, dtype='float32', flatten=True, weight_initializer=None,
super(AdaOut, self).__init__(**kwargs)
self._flatten = flatten
self.data_shape = <#list element.element.outputTypes as type>(${tc.join(type.dimensions, ",")})</#list>
classes = prod(list(self.data_shape))
with self.name_scope():
self.softmax = softmax
self.op_nums = op_nums
self.classes = classes
self.weight = self.params.get('weight',
shape=(classes, op_nums),
self.act = None
def __repr__(self):
s = '{name}({layout}, {act})'
shape = self.weight.shape
return s.format(name=self.__class__.__name__,
act=self.act if self.act else 'linear',
layout='{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0]))
# noinspection PyMethodOverriding
def hybrid_forward(self, F, x, weight, bias=None):
if self.softmax:
weight = F.softmax(weight)
# fc = F.npx.fully_connected if is_np_array() else F.FullyConnected
fc = F.FullyConnected
act = fc(x, weight, bias, no_bias=bias is None, num_hidden=self.classes,
flatten=self._flatten, name='fwd')
re = F.reshape(act,shape=self.data_shape)# add shape of output
# ToDo: reshape the output to the correct output shape
# ToDO: add trainling output block that gets after the AdaNet
return re
class BuildingBlock(gluon.HybridBlock):
the essential building block which gets stacked
def __init__(self,
<#if Block.isArtificial()>
operation = ${tc.include(Block,"ADANET_CONSTRUCTION")},
operation = DefaultBlock
operation = DefaultBlock,
#operation=None, # ToDo: put here the Block construction
super(BuildingBlock, self).__init__(**kwargs)
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