80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
from data_load import data_format
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import tensorflow as tf
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import numpy as np
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from keras.layers import Dropout
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from keras import regularizers
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from keras.callbacks import TensorBoard, LearningRateScheduler
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import keras
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def model_train(X_train, X_test, Y_train, Y_test):
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"""_summary_
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Args:
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X_train (np.array): _description_
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X_test (np.array): _description_
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Y_train (np.array): _description_
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Y_test (np.array): _description_
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"""
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# 数据随机化
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np.random.seed(7)
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np.random.shuffle(X_train)
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np.random.seed(7)
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np.random.shuffle(Y_train)
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tf.random.set_seed(7)
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# 构建模型
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model = tf.keras.models.Sequential([
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tf.keras.layers.LSTM(100, return_sequences=True), # 第一层
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Dropout(0.2),
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tf.keras.layers.LSTM(80), # 第二层
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Dropout(0.2),
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tf.keras.layers.Dense(
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1, kernel_regularizer=regularizers.l2(0.01))
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])
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# 损失函数
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loss_fn = tf.keras.losses.MeanSquaredError()
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# 编译模型
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model.compile(
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optimizer='SGD',
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loss=loss_fn,
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metrics=[tf.keras.metrics.MeanAbsolutePercentageError()]
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)
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# 定义学习率指数递减的函数
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def lr_schedule(epoch):
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initial_learning_rate = 0.01
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decay_rate = 0.1
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decay_steps = 2000
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new_learning_rate = initial_learning_rate * \
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decay_rate ** (epoch / decay_steps)
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return new_learning_rate
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# 定义学习率调度器
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lr_scheduler = LearningRateScheduler(lr_schedule)
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# TensorBoard 回调
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log_dir = "logs/fit"
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tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)
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# 训练模型，添加 TensorBoard 回调
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model.fit(X_train, Y_train, epochs=6000,
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callbacks=[tensorboard_callback, lr_scheduler], batch_size=256)
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loss, mape = model.evaluate(X_test, Y_test)
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print("Test loss:", loss,)
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print("test mape:", mape)
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# 保存模型
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keras.models.save_model(model, 'model')
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if __name__ == "__main__":
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X_train, X_test, Y_train, Y_test = data_format(
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'data/archive/PowerQualityDistributionDataset1.csv', md = 1)
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X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
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X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)
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model_train(X_train, X_test, Y_train, Y_test)
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