请提供一个三输入三输出的resnet实例
时间: 2023-05-22 15:05:48 浏览: 64
以下是一个三输入三输出的ResNet实例的Python代码:
```python
from keras.layers import Input, Conv2D, BatchNormalization, Activation, Add
from keras.models import Model
def res_block(inputs, filters, kernel_size=3, strides=1, padding='same'):
x = Conv2D(filters, kernel_size, strides=strides, padding=padding)(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters, kernel_size, strides=1, padding=padding)(x)
x = BatchNormalization()(x)
x = Add()([x, inputs])
x = Activation('relu')(x)
return x
input_shape = (224, 224, 3)
input1 = Input(input_shape)
input2 = Input(input_shape)
input3 = Input(input_shape)
x = Conv2D(64, 7, strides=2, padding='same')(input1)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = res_block(x, 64)
x = res_block(x, 64)
x = Conv2D(128, 3, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = res_block(x, 128)
x = res_block(x, 128)
x = Conv2D(256, 3, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = res_block(x, 256)
x = res_block(x, 256)
x = Conv2D(512, 3, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = res_block(x, 512)
x = res_block(x, 512)
x1 = Conv2D(1024, 3, strides=2, padding='same')(x)
x1 = BatchNormalization()(x1)
x1 = Activation('relu')(x1)
x1 = res_block(x1, 1024)
x1 = res_block(x1, 1024)
x1 = Conv2D(2048, 3, strides=2, padding='same')(x1)
x1 = BatchNormalization()(x1)
x1 = Activation('relu')(x1)
x1 = res_block(x1, 2048)
x1 = res_block(x1, 2048)
output1 = x1
x2 = Conv2D(1024, 3, strides=2, padding='same')(x)
x2 = BatchNormalization()(x2)
x2 = Activation('relu')(x2)
x2 = res_block(x2, 1024)
x2 = res_block(x2, 1024)
x2 = Conv2D(2048, 3, strides=2, padding='same')(x2)
x2 = BatchNormalization()(x2)
x2 = Activation('relu')(x2)
x2 = res_block(x2, 2048)
x2 = res_block(x2, 2048)
output2 = x2
x3 = Conv2D(1024, 3, strides=2, padding='same')(x)
x3 = BatchNormalization()(x3)
x3 = Activation('relu')(x3)
x3 = res_block(x3, 1024)
x3 = res_block(x3, 1024)
x3 = Conv2D(2048, 3, strides=2, padding='same')(x3)
x3 = BatchNormalization()(x3)
x3 = Activation('relu')(x3)
x3 = res_block(x3, 2048)
x3 = res_block(x3, 2048)
output3 = x3
model = Model(inputs=[input1, input2, input3], outputs=[output1, output2, output3])
```
这个实例定义了一个包含三个输入和三个输出的ResNet模型,每个输入都是一个形状为(224, 224, 3)的图像,分别被传入不同的ResNet分支进行处理。每个分支都包含多个Residual Block,最终输出一个2048维的向量作为该分支的输出。最终输出三个2048维的向量,分别对应三个输入的处理结果。