keras原始碼分析之Dense

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())
)

keras原始碼之application目錄

關於 pycache 目錄請參考keras原始碼之 pycache 目錄 裡面包含了各種網路的實現模組 包括權重路徑以及設定 網路結構等 接下來是一些已經棄用的函式模組 backend backend,layers layers,models models,utils utils def set k...

原始碼分析之LayoutInflater

簡介 inflate填充的過程 viewstub,merge,include的載入過程 layoutinflater系統服務的註冊過程 systemserviceregistry類有個靜態 塊，完成了常用服務的註冊，如下 static 註冊am registerservice context.act...

原始碼分析之HashMap

首先hashmap繼承了abstractmap，並且實現了map cloneable和serializable三個介面。cloneable和serializable是比較常規的兩個介面，在這裡並不作為重點。重點將會放在abstractmap和map兩個規範上。其中abstractmap是乙個抽象類，...