给出一个具有三个特征输入和三个输出的ResNet网络，其中输入数据是一个二维数组，表示三个特征的取值。输出数据是一个二维数组，表示三个预测值。模型使用均方误差作为损失函数，并使用Adam优化器进行训练，且在训练过程中，模型采用训练集进行训练，并在测试集上进行验证。最后，模型使用测试数据集进行评估，并输出精度结果

该ResNet网络的结构示意图如下：

                  Input
                   |
                   V
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
                  ReLU
                   |
                   V
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
                  ReLU
                   |
                   V
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
  Shortcut Path--> Add --> ReLU
                   ^
                   |
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
                  ReLU
                   |
                   V
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
                  ReLU
                   |
                   V
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
  Shortcut Path--> Add --> ReLU
                   ^
                   |
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
                  ReLU
                   |
                   V
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
                  ReLU
                   |
                   V
                Conv2D
                   |
                   V
            BatchNormalization
                   |
                   V
  Shortcut Path--> Add --> ReLU
                   ^
                   |
                Dense
                   |
                   V
                Dense
                   |
                   V
                Dense
                   |
                   V
                Output

其中，每个Conv2D层均采用3x3的卷积核，每个Conv2D层和Shortcut Path上的Add操作均使用线性激活函数。每个Dense层均使用ReLU激活函数，最后一个输出层不使用激活函数。

模型的代码实现如下：

import tensorflow as tf
from tensorflow.keras import layers

def identity_block(input_tensor, filters):
    filters1, filters2, filters3 = filters
    x = layers.Conv2D(filters1, (1, 1))(input_tensor)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)

    x = layers.Conv2D(filters2, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)

    x = layers.Conv2D(filters3, (1, 1))(x)
    x = layers.BatchNormalization()(x)

    x = layers.Add()([x, input_tensor])
    x = layers.ReLU()(x)
    return x

def resnet(input_shape=(3,), output_shape=(3,)):
    inputs = layers.Input(shape=input_shape, name='inputs')
    x = layers.Reshape(target_shape=(1, 1, 3))(inputs)

    x = layers.Conv2D(64, (7, 7), strides=2, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)
    x = layers.MaxPooling2D((3, 3), strides=2, padding='same')(x)

    x = identity_block(x, [64, 64, 256])
    x = identity_block(x, [64, 64, 256])

    x = identity_block(x, [128, 128, 512])
    x = identity_block(x, [128, 128, 512])

    x = identity_block(x, [256, 256, 1024])
    x = identity_block(x, [256, 256, 1024])

    x = identity_block(x, [512, 512, 2048])
    x = identity_block(x, [512, 512, 2048])

    x = layers.Flatten()(x)

    x = layers.Dense(512, activation='relu')(x)
    x = layers.Dense(128, activation='relu')(x)
    x = layers.Dense(64, activation='relu')(x)

    outputs = layers.Dense(output_shape[0])(x)

    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    return model

model = resnet(input_shape=(3,), output_shape=(3,))
model.compile(optimizer='adam', loss='mse', metrics=['mse'])

模型的训练和测试代码如下：

import numpy as np

# 生成随机数据作为训练集和测试集
train_X = np.random.rand(1000, 3)
train_y = np.random.rand(1000, 3)
test_X = np.random.rand(100, 3)
test_y = np.random.rand(100, 3)

# 训练模型
model.fit(train_X, train_y, epochs=10, batch_size=32, validation_data=(test_X, test_y))

# 评估模型
loss, mse = model.evaluate(test_X, test_y)
print('Mean Squared Error:', mse)

备注：由于ResNet网络的层数较深，因此在训练过程中可能需要较长的时间。