在深度学习中，可以使用卷积神经网络（CNN）或循环神经网络（RNN）来实现多参数恢复。以下是使用CNN的实现示例：

import numpy as np import tensorflow as tf

数据集

data = np.random.rand(100, 10, 10, 3) labels = np.random.rand(100, 3)

构建模型

input_layer = tf.keras.layers.Input(shape=(10, 10, 3)) conv_layer = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(input_layer) flatten_layer = tf.keras.layers.Flatten()(conv_layer) dense_layer = tf.keras.layers.Dense(units=64, activation='relu')(flatten_layer) output_layer = tf.keras.layers.Dense(units=3, activation='linear')(dense_layer) model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)

编译模型

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='mse')

训练模型

model.fit(x=data, y=labels, epochs=10, batch_size=10)

使用RNN的实现示例：

数据集

data = np.random.rand(100, 10, 3) labels = np.random.rand(100, 3)

构建模型

input_layer = tf.keras.layers.Input(shape=(10, 3)) lstm_layer = tf.keras.layers.LSTM(units=32, return_sequences=True)(input_layer) flatten_layer = tf.keras.layers.Flatten()(lstm_layer) dense_layer = tf.keras.layers.Dense(units=64, activation='relu')(flatten_layer) output_layer = tf.keras.layers.Dense(units=3, activation='linear')(dense_layer) model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)

编译模型

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='mse')

训练模型

model.fit(x=data, y=labels, epochs=10, batch_size=10)