给我搭建一个生活质量的深度学习预测模型，给出具体的代码

我可以为您提供一个基于深度学习的生活质量预测模型的代码： import tensorflow as tf from tensorflow.keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, BatchNormalization, Flatten from tensorflow.keras import Model # 设置超参数 learning_rate = 0.001 num_epochs = 50 batch_size = 32 # 定义模型结构 class Model(Model): def __init__(self): super(Model, self).__init__() self.conv1 = Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=(32, 32, 3)) self.pool1 = MaxPooling2D(pool_size=(2,2)) self.conv2 = Conv2D(64, kernel_size=(3,3), activation='relu') self.pool2 = MaxPooling2D(pool_size=(2,2)) self.conv3 = Conv2D(128, kernel_size=(3,3), activation='relu') self.pool3 = MaxPooling2D(pool_size=(2,2)) self.flatten = Flatten() self.d1 = Dense(512, activation='relu') self.dropout = Dropout(0.5) self.d2 = Dense(2, activation='softmax') def call(self, x): x = self.conv1(x) x = self.pool1(x) x = self.conv2(x) x = self.pool2(x) x = self.conv3(x) x = self.pool3(x) x = self.flatten(x) x = self.d1(x) x = self.dropout(x) x = self.d2(x) return x # 实例化模型 model = Model() # 定义优化器 optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate) # 定义损失函数 loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) # 定义损失追踪器 train_loss = tf.keras.metrics.Mean(name='train_loss') # 定义训练步骤 @tf.function def train_step(images, labels): with tf.GradientTape() as tape: predictions = model(images) loss = loss_object(labels, predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) train_loss(loss) # 编译模型 model.compile(optimizer=optimizer, loss=loss_object, metrics=['accuracy']) #训练模型 model.fit(train_data, epochs=num_epochs, batch_size=batch_size)