Resnet50如何禁用池化层
时间: 2024-02-26 21:26:39 浏览: 24
在ResNet50模型中,池化层是在每个残差块之间添加的,可以通过修改代码来禁用池化层。具体来说,可以在每个残差块的代码中将池化层替换为步幅为1的卷积层或者直接删除池化层。以下是一段示例代码,展示了如何在ResNet50中禁用池化层:
```python
from keras.layers import Input, Conv2D, BatchNormalization, Activation, Add, ZeroPadding2D, AveragePooling2D, Flatten, Dense
from keras.models import Model
# 定义卷积块函数
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
filters1, filters2, filters3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = BatchNormalization(name=bn_name_base + '2c')(x)
shortcut = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(name=bn_name_base + '1')(shortcut)
x = Add()([x, shortcut])
x = Activation('relu')(x)
return x
# 定义恒等块函数
def identity_block(input_tensor, kernel_size, filters, stage, block):
filters1, filters2, filters3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = BatchNormalization(name=bn_name_base + '2c')(x)
x = Add()([x, input_tensor])
x = Activation('relu')(x)
return x
# 定义ResNet50模型
def ResNet50(input_shape=(224, 224, 3), classes=1000):
input_tensor = Input(shape=input_shape)
x = ZeroPadding2D((3, 3))(input_tensor)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = BatchNormalization(name='bn_conv1')(x)
x = Activation('relu')(x)
x = ZeroPadding2D((1, 1))(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
x = AveragePooling2D((7, 7), name='avg_pool')(x)
x = Flatten()(x)
x = Dense(classes, activation='softmax', name='fc1000')(x)
model = Model(inputs=input_tensor, outputs=x, name='resnet50')
return model
# 禁用池化层
def disable_pooling(model):
for layer in model.layers:
if 'pool' in layer.name:
layer.trainable = False
return model
# 创建ResNet50模型并禁用池化层
model = ResNet50()
model = disable_pooling(model)
```
在上述代码中,我们定义了一个`disable_pooling`函数,它遍历了ResNet50模型中的所有层,并将所有名称带有`pool`的层的`trainable`属性设置为False,这样就禁用了所有的池化层。最后,我们用`disable_pooling`函数来禁用池化层。