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[英]how to solve “NameError: name 'indices' is not defined”?
[英]How to solve `NameError: name 'compression' is not defined`?
我正在尝试实现 DenseNet model,我正在使用具有 4 个类的图像数据集。
代码片段:
对于建筑 model:
def denseblock(input, num_filter = 12, dropout_rate = 0.2):
global compression
temp = input
for _ in range(l):
BatchNorm = BatchNormalization()(temp)
relu = Activation('relu')(BatchNorm)
Conv2D_3_3 =Conv2D(int(num_filter*compression), (3,3), use_bias=False ,padding='same')(relu)
if dropout_rate>0:
Conv2D_3_3 = Dropout(dropout_rate)(Conv2D_3_3)
concat = Concatenate(axis=-1)([temp,Conv2D_3_3])
temp = concat
return temp
## transition Block
def transition(input, num_filter = 12, dropout_rate = 0.2):
global compression
BatchNorm = BatchNormalization()(input)
relu = Activation('relu')(BatchNorm)
Conv2D_BottleNeck = Conv2D(int(num_filter*compression), (1,1), use_bias=False ,padding='same')(relu)
if dropout_rate>0:
Conv2D_BottleNeck = Dropout(dropout_rate)(Conv2D_BottleNeck)
avg = AveragePooling2D(pool_size=(2,2))(Conv2D_BottleNeck)
return avg
#output layer
def output_layer(input):
global compression
BatchNorm = BatchNormalization()(input)
relu = Activation('relu')(BatchNorm)
AvgPooling = AveragePooling2D(pool_size=(2,2))(relu)
flat = Flatten()(AvgPooling)
output = Dense(categories, activation='softmax')(flat)
return output
使用两个 DenseNet 块创建一个 model:
l = 7
input = Input(shape=(height, width, 3))
First_Conv2D = Conv2D(30, (3,3), use_bias=False ,padding='same')(input)
First_Block = denseblock(First_Conv2D, 30, 0.5)
First_Transition = transition(First_Block, 30, 0.5)
Last_Block = denseblock(First_Transition, 30, 0.5)
output = output_layer(Last_Block)
model = Model(inputs=[input], outputs=[output])
错误是: NameError: name 'compression' is not defined
我猜compression
是你的瓶颈宽度,但似乎没有定义,也许试试:
compression = 4
def denseblock(input, num_filter = 12, dropout_rate = 0.2):
temp = input
for _ in range(l):
BatchNorm = layers.BatchNormalization()(temp)
relu = layers.Activation('relu')(BatchNorm)
Conv2D_3_3 = layers.Conv2D(int(num_filter*compression), (3,3), use_bias=False ,padding='same')(relu)
if dropout_rate>0:
Conv2D_3_3 = layers.Dropout(dropout_rate)(Conv2D_3_3)
concat = layers.Concatenate(axis=-1)([temp,Conv2D_3_3])
temp = concat
return temp
## transition Blosck
def transition(input, num_filter = 12, dropout_rate = 0.2):
BatchNorm = layers.BatchNormalization()(input)
relu = layers.Activation('relu')(BatchNorm)
Conv2D_BottleNeck = layers.Conv2D(int(num_filter*compression), (1,1), use_bias=False ,padding='same')(relu)
if dropout_rate>0:
Conv2D_BottleNeck = layers.Dropout(dropout_rate)(Conv2D_BottleNeck)
avg = layers.AveragePooling2D(pool_size=(2,2))(Conv2D_BottleNeck)
return avg
#output layer
def output_layer(input):
BatchNorm = layers.BatchNormalization()(input)
relu = layers.Activation('relu')(BatchNorm)
AvgPooling = layers.AveragePooling2D(pool_size=(2,2))(relu)
flat = layers.Flatten()(AvgPooling)
output = layers.Dense(10, activation='softmax')(flat)
return output
l = 7
input = layers.Input(shape=(28, 28, 3,))
First_Conv2D = layers.Conv2D(30, (3,3), use_bias=False ,padding='same')(input)
First_Block = denseblock(First_Conv2D, 30, 0.5)
First_Transition = transition(First_Block, 30, 0.5)
Last_Block = denseblock(First_Transition, 30, 0.5)
output = output_layer(Last_Block)
model = Model(inputs=[input], outputs=[output])
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