3 3 keras模型構建的三種方式

model = sequential(

)#卷積層conv_1_1
model.add(cov2d(input_shape =(64
,64,3），filters =
32, 
kernel_size =
3, activation =
'relu'
, kernel_initializer =
'he_uniform'
, name = 'conv_1_1)
#卷積層 conv_1_2
model.add(cov2d(filters =
32, kernel_size =3, 
activation =
'relu'
, kernel_initializer =
'he_uniform'
, name = 'conv_1_2)
#池化層max_pool_1
model.add(maxpool2d(pool_size =
32, name = 'max_pool_1)
)#展平層
model.add(flatten(name =
'flatten'))
#全連線層
model.add(dense(unit =
6, activation =
'softmax'
, name =
'logit'))
#設定損失函式loss、優化器optimizer、評價指標metrics
model.
compile
(loss=
"categorical_crossentropy"
, optimizer = tf.keras.optimizers.sgd(learning_rate =
0.001), 
metrics =
["accuracy"
])

model = sequential(

[ conv2d(input_shape =(64
,64,3
), filters =32,
kernel_size =
3, activation =
'relu'
, name =
'conv_1_1'),
conv2d(filters =
32, kernel_size =
3, activation =
'relu'
, name =
'conv_1_2'),
maxpool2d(poo_size =
2, anme =
'max_pool_1'),
flatten(name =
'flatten'),
dense(units =
6, activation =
"softmax"
, name =
'logit')]
)#設定損失函式loss、優化器optimizer、評價標準metrics
model.
compile
(loss=
"categorical_crossentropy"
, optimizer = tf.keras.optimizers.sgd(learning_rate =
0.001), 
metrics =
["accuracy"
])

缺點：不能建立以下模型結構

#輸入層input

input
=input
(shape =(64
,64,3
), name =
'input'
)#卷積層conv_1_2
x = conv2d(filters =
32, kernel_size =
3, activation =
'relu'
, name =
'conv_1_1')(
input
)#卷積層con_1_2
x = conv2d(filters =
32, kernel_size =
3, activation =
'relu'
, name = 'conv_1_2)
(x)#池化層max_pool_1
x = maxpool2d(pool_size =
2, name = 'max_pool_1)
(x)#展平層
x = flatten(name =
'flatten'
)(x)
#全連線層
output = dense(units =
6, activation =
"softmax"
, name =
'logit'
)(x)
model = model(inputs =
input
, outputs = output)
#設定損失函式loss、優化器optimizer、評價標準metrics
model.
compile
(loss=
"categorical_crossentropy"
, optimizer = tf.keras.optimizers.sgd(learning_rate =
0.001), 
metrics =
["accuracy"
])

#定義乙個子類來搭建模型

class
convmodel
(model)
:def
__init__
(self)
:#父類初始化
super
(convmodel, self)
.__init__(
)#卷積層conv_1_1
self.conv_1_1 = conv2d(input_shape =(64
,64,3
),filters =
32, kernel_size =
3, activation =
'relu'
, name =
'con_1_1'
)#卷積層conv_1_2
self.conv_1_2 = conv2d(filters =
32, kernel_size =3,
activation =
'relu'
, name =
'conv_1_2'
)#池化層max_pool_1
self.max_pool_1 = maxpool2d(pool.size =
2, name =
'max_pool_1'
)#展平層flatten
self.dense = dense(units =
6, activation =
"softmax"
, name =
'logit'
)def
call
(selfm, x)
: x = self.conv_1_1(x)
x = self.conv_1_2(x)
x = self.max_pool_1(x)
x = self.conv_2_1(x)
x = self.conv_2_2(x)
x = self.max_pool_2(x)
x = self.flatten(x)
x = self.dense(x)
return x
#類例項化
model = convmodel(
)

__init__方法中定義我們要使用的層，這裡可以使用keras自帶的層；

call方法中實現模型的網路層。

適用場合：需要編寫自定義的模型，如在網路中使用自定義的層、自定義的損失函式、自定義的啟用函式等。

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