给出一个三输入三输出的resnet预测实例

以下是一个简单的三输入三输出的resnet预测实例：

import tensorflow as tf

def residual_block(inputs):
    x = tf.keras.layers.Conv2D(64, (3, 3), padding='same')(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.Conv2D(64, (3, 3), padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Add()([x, inputs])
    x = tf.keras.layers.Activation('relu')(x)
    return x

input1 = tf.keras.layers.Input(shape=(256, 256, 3))
input2 = tf.keras.layers.Input(shape=(256, 256, 3))
input3 = tf.keras.layers.Input(shape=(256, 256, 3))

x1 = residual_block(input1)
x1 = residual_block(x1)
x1 = residual_block(x1)

x2 = residual_block(input2)
x2 = residual_block(x2)
x2 = residual_block(x2)

x3 = residual_block(input3)
x3 = residual_block(x3)
x3 = residual_block(x3)

output1 = tf.keras.layers.Conv2D(1, (1, 1))(x1)
output1 = tf.keras.layers.Activation('sigmoid')(output1)

output2 = tf.keras.layers.Conv2D(1, (1, 1))(x2)
output2 = tf.keras.layers.Activation('sigmoid')(output2)

output3 = tf.keras.layers.Conv2D(1, (1, 1))(x3)
output3 = tf.keras.layers.Activation('sigmoid')(output3)

model = tf.keras.models.Model(inputs=[input1, input2, input3], outputs=[output1, output2, output3])

这个模型有三个输入，每个输入是一张 256x256 的 RGB 图像。模型使用 resnet 的残差块来提取特征，然后在每个输出上都进行了 1x1 的卷积并进行 sigmoid 激活以获得相应的预测。