def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42,42,1)) conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1) bn1 = layers.BatchNormalization()(conv1) pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn1) do1 = layers.Dropout(0.3)(pool1) # channel 2 inputs2 = layers.Input(shape=(42,42,1)) conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2) bn2 = layers.BatchNormalization()(conv2) pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn2) do2 = layers.Dropout(0.3)(pool2) # channel 3 inputs3 = layers.Input(shape=(42,42,1)) conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3) bn3 = layers.BatchNormalization()(conv3) pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn3) do3 = layers.Dropout(0.3)(pool3) # merge 1 merged = layers.Concatenate()([do1, do2, do3]) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model如何改为class定义形式
时间: 2023-09-05 10:07:13 浏览: 31
将函数定义改为类定义形式的代码如下:
```
class MEAN_Spot(tf.keras.Model):
def __init__(self, opt):
super(MEAN_Spot, self).__init__()
# channel 1
self.inputs1 = layers.Input(shape=(42,42,1))
self.conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(self.inputs1)
self.bn1 = layers.BatchNormalization()(self.conv1)
self.pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(self.bn1)
self.do1 = layers.Dropout(0.3)(self.pool1)
# channel 2
self.inputs2 = layers.Input(shape=(42,42,1))
self.conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(self.inputs2)
self.bn2 = layers.BatchNormalization()(self.conv2)
self.pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(self.bn2)
self.do2 = layers.Dropout(0.3)(self.pool2)
# channel 3
self.inputs3 = layers.Input(shape=(42,42,1))
self.conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(self.inputs3)
self.bn3 = layers.BatchNormalization()(self.conv3)
self.pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(self.bn3)
self.do3 = layers.Dropout(0.3)(self.pool3)
# merge 1
self.merged = layers.Concatenate()([self.do1, self.do2, self.do3])
# interpretation 1
self.merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(self.merged)
self.merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(self.merged_conv)
self.flat = layers.Flatten()(self.merged_pool)
self.flat_do = layers.Dropout(0.2)(self.flat)
# outputs
self.outputs = layers.Dense(1, activation='linear', name='spot')(self.flat_do)
# model
self.model = tf.keras.models.Model(inputs=[self.inputs1, self.inputs2, self.inputs3], outputs=[self.outputs])
self.model.compile(
loss={'spot':'mse'},
optimizer=opt,
metrics={'spot': tf.keras.metrics.MeanAbsoluteError()}
)
def call(self, inputs):
return self.model(inputs)
```
在类中,我们需要通过 `__init__` 方法来初始化模型的各个组件,即将原来函数中定义的各个变量和层定义为类的属性。同时,我们需要实现 `call` 方法,用于定义模型的前向传播过程。在这个例子中,我们通过 `self.model` 来定义模型,而在 `call` 方法中,我们直接调用 `self.model` 来实现前向传播。