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模型更新

This commit is contained in:
kevinding1125 2023-06-18 17:55:55 +08:00
parent 2400af4a1a
commit c3e7572691
3 changed files with 37 additions and 24 deletions
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6
TensorFlow_eaxmple/Model_train_test/RUL/test/LSTMContinueTest.py
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@ -27,8 +27,8 @@ batch_size = 32
EPOCH = 1000
unit = 512 # LSTM的维度
predict_num = 50 # 预测个数
model_name = "LSTM"
save_name = r"selfMulti_norm_{0}_hidden{1}_unit{2}_feature{3}_predict{4}.h5".format(model_name, hidden_num, unit,
model_name = "FTLSTM"
save_name = r"selfMulti_{0}_hidden{1}_unit{2}_feature{3}_predict{4}.h5".format(model_name, hidden_num, unit,
feature,
predict_num)
@ -237,7 +237,7 @@ if __name__ == '__main__':
mode='min')
lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=20, min_lr=0.001)
model.compile(optimizer=tf.optimizers.SGD(), loss=tf.losses.mse)
model.compile(optimizer=tf.optimizers.SGD(), loss=FTMSE())
model.summary()
early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=100, mode='min', verbose=1)

41
TensorFlow_eaxmple/Model_train_test/RUL/test/LSTMTest.py
View File

@ -29,7 +29,7 @@ unit = 512 # LSTM的维度
predict_num = 50 # 预测个数
model_name = "LSTM"
save_name = r"self_{0}_hidden{1}_unit{2}_feature{3}_predict{4}.h5".format(model_name, hidden_num, unit, feature,
predict_num)
predict_num)
def getData(filter_num, dims):
@ -87,6 +87,7 @@ predict_data.shape: (total_dims - filter_num, filter_num) :(571,600,30)
train_label.shape: (total_dims - filter_num - 1, filter_num) :(570,600)
'''
def remove(train_data, train_label, batch_size):
epoch, _, _ = train_data.shape
size = int(epoch / batch_size)
@ -98,6 +99,8 @@ train_data.shape: (1230, 10, 10)
train_label.shape: (1230, 10)
train_label_single.shape: (1230,)
'''
def splitValData(data, label, label_single, predict_num=50):
sample, hidden, feature = data.shape
@ -182,27 +185,27 @@ if __name__ == '__main__':
train_label,
train_label_single,
predict_num=predict_num)
# # #### TODO 训练
model = predict_model(hidden_num, feature)
checkpoint = tf.keras.callbacks.ModelCheckpoint(
filepath=save_name,
monitor='val_loss',
verbose=2,
save_best_only=True,
mode='min')
lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=20, min_lr=0.001)
model.compile(optimizer=tf.optimizers.SGD(), loss=tf.losses.mse)
model.summary()
early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=100, mode='min', verbose=1)
history = model.fit(train_data, train_label_single, epochs=EPOCH,
batch_size=batch_size, validation_data=(val_data, val_label_single), shuffle=True, verbose=2,
callbacks=[checkpoint, lr_scheduler, early_stop])
# # # #### TODO 训练
# model = predict_model(hidden_num, feature)
# checkpoint = tf.keras.callbacks.ModelCheckpoint(
# filepath=save_name,
# monitor='val_loss',
# verbose=2,
# save_best_only=True,
# mode='min')
# lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=20, min_lr=0.001)
#
# model.compile(optimizer=tf.optimizers.SGD(), loss=tf.losses.mse)
# model.summary()
# early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=100, mode='min', verbose=1)
#
# history = model.fit(train_data, train_label_single, epochs=EPOCH,
# batch_size=batch_size, validation_data=(val_data, val_label_single), shuffle=True, verbose=2,
# callbacks=[checkpoint, lr_scheduler, early_stop])
#### TODO 测试
trained_model = tf.keras.models.load_model(save_name, custom_objects={'LSTMLayer': LSTMLayer})
trained_model = tf.keras.models.load_model(save_name, custom_objects={'LSTMLayer': LSTMLayer, 'FTMSE': FTMSE})
# 使用已知的点进行预测
predicted_data = predictByEveryData(trained_model, train_data)

14
TensorFlow_eaxmple/Model_train_test/model/LossFunction/FTMSE.py
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@ -20,7 +20,10 @@ class FTMSE(tf.keras.losses.Loss):
yt_fft = tf.signal.fft(tf.cast(y_true, tf.complex64))
yp_fft = tf.signal.fft(tf.cast(y_pred, tf.complex64))
epoch, length, _ = yp_fft.shape
print("yt_amp",yt_fft)
print("yp_fft",yp_fft)
epoch, length = yp_fft.shape
# 幅值
yt_amp = tf.abs(yt_fft / length)
yp_amp = tf.abs(yp_fft / length)
@ -28,9 +31,16 @@ class FTMSE(tf.keras.losses.Loss):
yt_angle = tf.math.angle(yt_fft)
yp_angle = tf.math.angle(yp_fft)
time_loss = tf.keras.losses.mean_squared_error(y_true, y_pred)
amp_loss = tf.keras.losses.mean_squared_error(yt_amp, yp_amp)
angle_loss = tf.keras.losses.mean_squared_error(yt_angle, yp_angle)
print(time_loss)
print(amp_loss)
print(angle_loss)
ftLoss = time_loss + amp_loss
# ftLoss = time_loss + amp_loss + angle_loss
# ftLoss = time_loss
ftLoss = time_loss + amp_loss + angle_loss
return ftLoss