class BasicBlock(layers.Layer): expansion = 1 def __init__(self, in_channels, out_channels, stride=1):定义的basicblock模块之后如何放到def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) # merge 1 inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)后面
时间: 2023-09-05 20:08:07 浏览: 120
OpenLayers-API接口文档.rar_layer.open 详解_openLayer_openlayers_openla
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可以使用`BasicBlock`类来定义一个基本块模块,然后在`MEAN_Spot`函数中调用该模块。具体实现方法如下:
```python
class BasicBlock(layers.Layer):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = layers.Conv2D(out_channels, kernel_size=3, strides=stride, padding='same', use_bias=False)
self.bn1 = layers.BatchNormalization()
self.relu = layers.ReLU()
self.conv2 = layers.Conv2D(out_channels * self.expansion, kernel_size=3, strides=1, padding='same', use_bias=False)
self.bn2 = layers.BatchNormalization()
if stride != 1 or in_channels != out_channels * self.expansion:
self.shortcut = keras.Sequential([
layers.Conv2D(out_channels * self.expansion, kernel_size=1, strides=stride, use_bias=False),
layers.BatchNormalization()
])
else:
self.shortcut = lambda x: x
def call(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x += self.shortcut(inputs)
x = self.relu(x)
return x
def MEAN_Spot(opt):
# channel 1
inputs1 = layers.Input(shape=(42, 42, 1))
inputs2 = layers.Input(shape=(42, 42, 1))
inputs3 = layers.Input(shape=(42, 42, 1))
# merge 1
inputs = layers.Concatenate()([inputs1, inputs2, inputs3])
x = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)
# basic block
x = BasicBlock(3, 16)(x)
x = BasicBlock(16, 32, stride=2)(x)
x = BasicBlock(32, 64, stride=2)(x)
# global average pooling
x = layers.GlobalAveragePooling2D()(x)
# output
outputs = layers.Dense(1, activation='sigmoid')(x)
# define model
model = keras.Model(inputs=[inputs1, inputs2, inputs3], outputs=outputs)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
return model
```
这里我们在`MEAN_Spot`函数中使用`BasicBlock`类来构建基本块模块,并且将该模块的输出作为下一个模块的输入。最后使用全局平均池化层和全连接层得到最终的输出。
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