本文主要講解一下dense層的原始碼，dense層即最常用的全連線層，**很簡單，主要是重寫了build與call方法，在我們自定義layer時，也可以參考該層的實現。
class
dense
(layer)
:def
__init__
(self, units,
activation=
none
, use_bias=
true
, kernel_initializer=
'glorot_uniform'
, bias_initializer=
'zeros'
, kernel_regularizer=
none
, bias_regularizer=
none
, activity_regularizer=
none
, kernel_constraint=
none
, bias_constraint=
none
,**kwargs):if
'input_shape'
notin kwargs and
'input_dim'
in kwargs:
kwargs[
'input_shape']=
(kwargs.pop(
'input_dim'),
)super
(dense, self)
.__init__(
**kwargs)
self.units = units
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = inputspec(min_ndim=2)
self.supports_masking =
true
def
build
(self, input_shape)
:assert
len(input_shape)
>=
2 input_dim = input_shape[-1
] self.kernel = self.add_weight(shape=
(input_dim, self.units)
, initializer=self.kernel_initializer,
name=
'kernel'
, regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=
(self.units,),
initializer=self.bias_initializer,
name=
'bias'
, regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else
: self.bias =
none
self.input_spec = inputspec(min_ndim=
2, axes=
) self.built =
true
def
call
(self, inputs)
: output = k.dot(inputs, self.kernel)
if self.use_bias:
output = k.bias_add(output, self.bias, data_format=
'channels_last'
)if self.activation is
notnone
: output = self.activation(output)
return output
def
compute_output_shape
(self, input_shape)
:assert input_shape and
len(input_shape)
>=
2assert input_shape[-1
] output_shape =
list
(input_shape)
output_shape[-1
]= self.units
return
tuple
(output_shape)
def
get_config
(self)
: config =
base_config =
super
(dense, self)
.get_config(
)return
dict
(list
(base_config.items())
+list
(config.items())
)
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