这是我首次尝试创建LSTM RNN。我对生成的训练损失与验证损失图的含义感到困惑。以下是图表的图像：
以下是训练数据的样本：
lat long trip_id mode_catdatetime id 2011-08-27 06:13:01 20 39.979973 116.305745 1 12011-08-27 06:13:02 20 39.979957 116.305688 1 12011-08-27 06:13:03 20 39.979960 116.305693 1 12011-08-27 06:13:04 20 39.979970 116.305717 1 12011-08-27 06:13:05 20 39.979985 116.305732 1 1日期时间和ID（用户ID）被设置为索引。
以下是创建移动窗口和LSTM的代码：
def moving_window(dataset_x, dataset_y, past_history): data, labels = [], [] for i in range(past_history, len(dataset_x)): indices = range(i-past_history, i) data.append(dataset_x[indices]) labels.append(dataset_y[i]) return np.array(data), np.array(labels)past_history = 60x_train_single, y_train_single = moving_window(dataset_train_x, dataset_train_y, past_history)x_test_single, y_test_single = moving_window(dataset_test_x, dataset_test_y, past_history)buffer_size = len(x_train_single)//10batch_size = 256train_data_single = tf.data.Dataset.from_tensor_slices((x_train_single, y_train_single))train_data_single = train_data_single.cache().shuffle(buffer_size).batch(batch_size).repeat()test_data_single = tf.data.Dataset.from_tensor_slices((x_test_single, y_test_single))test_data_single = test_data_single.batch(batch_size).repeat()single_step_model = tf.keras.models.Sequential()single_step_model.add(tf.keras.layers.LSTM(32, input_shape=x_train_single.shape[-2:]))single_step_model.add(tf.keras.layers.Dense(1))single_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss='mae')evaluation_interval = len(x_train_single)//batch_sizeepochs = 10single_step_history = single_step_model.fit(train_data_single, epochs=epochs, steps_per_epoch=evaluation_interval, validation_data=test_data_single, validation_steps=50)回答：
如果训练损失和验证损失之间的差距很大，说明你的模型出现了过拟合。如果训练损失很大，说明你的模型出现了欠拟合。如果你的训练损失和验证损失重叠或彼此接近，说明你的模型现在适合用于预测。
这里的模型出现了过拟合
