模型更新
This commit is contained in:
kevinding1125
parent
9d72cb9592
commit
a35ccc2f63
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@ -14,6 +14,8 @@ import matplotlib.pyplot as plt
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from keras.callbacks import EarlyStopping
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from model.LossFunction.FTMSE import FTMSE
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from model.ChannelAttention.DCT_channelAttention import DCTChannelAttention
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from model.ChannelAttention.Light_channelAttention import LightChannelAttention1 as LightChannelAttention
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import math
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from sklearn.metrics import mean_absolute_error, mean_squared_error
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from pylab import *
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@ -27,7 +29,7 @@ batch_size = 32
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EPOCH = 1000
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unit = 512 # LSTM的维度
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predict_num = 50 # 预测个数
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model_name = "FTLSTM"
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model_name = "FC_FTLSTM"
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save_name = r"selfMulti_{0}_hidden{1}_unit{2}_feature{3}_predict{4}.h5".format(model_name, hidden_num, unit,
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feature,
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predict_num)
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@ -134,6 +136,8 @@ def splitValData(data, label, label_single, predict_num=50):
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def predict_model_multi(filter_num, dims):
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tf.config.experimental_run_functions_eagerly(True)
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input = tf.keras.Input(shape=[filter_num, dims])
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input = tf.cast(input, tf.float32)
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@ -143,7 +147,9 @@ def predict_model_multi(filter_num, dims):
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#### 自己
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LSTM = LSTMLayer(units=512, return_sequences=True)(input)
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# LSTM = LightChannelAttention()(LSTM)
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LSTM = LSTMLayer(units=256, return_sequences=True)(LSTM)
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LSTM = LightChannelAttention()(LSTM)
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### flatten
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x = tf.keras.layers.Flatten()(LSTM)
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@ -161,30 +167,7 @@ def predict_model_multi(filter_num, dims):
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return model
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def split_data(train_data, train_label):
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return train_data[:1150, :, :], train_label[:1150, :], train_data[-70:, :, :], train_label[-70:, :]
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# 仅使用预测出来的最新的一个点预测以后
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def predictOneByOne(newModel, train_data, predict_num=50):
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# 取出训练数据的最后一条
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each_predict_data = np.expand_dims(train_data[-1, :, :], axis=0)
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predicted_list = np.empty(shape=(predict_num, 1)) # (5,filter_num,30)
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# all_data = total_data # (1201,)
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for each_predict in range(predict_num):
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# predicted_data.shape : (1,1)
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predicted_data = newModel.predict(each_predict_data) # (batch_size,filer_num,1)
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predicted_list[each_predict] = predicted_data
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# (1,1) => (10,1)
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temp1 = np.transpose(np.concatenate([each_predict_data[:, 1:, -1], predicted_data], axis=1), [1, 0])
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each_predict_data = np.expand_dims(
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np.concatenate([np.squeeze(each_predict_data[:, :, 1:], axis=0), temp1], axis=1), axis=0)
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return predicted_list
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# 使用最后预测出来的一整行与之前的拼接
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def predictContinueByOne(newModel, train_data, predict_num=50):
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# 取出训练数据的最后一条
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each_predict_data = np.expand_dims(train_data[-1, :, :], axis=0)
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@ -221,13 +204,13 @@ def predictByEveryData(trained_model: tf.keras.Model, predict_data):
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if __name__ == '__main__':
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# 数据读取
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# 数据读入 --> 所有的原始数据;所有的训练数据;所有的训练标签(预测一个序列);所有的训练标签(预测一个点)
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total_data, train_data, train_label, train_label_single = getData(hidden_num, feature, if_norm=False)
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total_data, train_data, train_label, train_label_single = getData(hidden_num, feature)
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# 根据预测的点数划分训练集和测试集(验证集)
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train_data, val_data, train_label, val_label, train_label_single, val_label_single = splitValData(train_data,
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train_label,
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train_label_single,
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predict_num=predict_num)
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# # #### TODO 训练
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# #### TODO 训练
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model = predict_model_multi(hidden_num, feature)
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checkpoint = tf.keras.callbacks.ModelCheckpoint(
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filepath=save_name,
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@ -237,7 +220,8 @@ if __name__ == '__main__':
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mode='min')
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lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=20, min_lr=0.001)
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model.compile(optimizer=tf.optimizers.SGD(), loss=FTMSE())
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model.compile(optimizer=tf.optimizers.SGD(), loss=tf.losses.mse)
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# model.compile(optimizer=tf.optimizers.SGD(learning_rate=0.001), loss=FTMSE())
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model.summary()
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early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=100, mode='min', verbose=1)
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@ -247,7 +231,11 @@ if __name__ == '__main__':
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#### TODO 测试
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trained_model = tf.keras.models.load_model(save_name, custom_objects={'LSTMLayer': LSTMLayer})
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# trained_model = tf.keras.models.load_model(save_name, custom_objects={'LSTMLayer': LSTMLayer, 'FTMSE': FTMSE})
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# todo 解决自定义loss无法导入的问题
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trained_model = tf.keras.models.load_model(save_name, compile=False, custom_objects={'LSTMLayer': LSTMLayer,'DCTChannelAttention':DCTChannelAttention})
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trained_model.compile(optimizer=tf.optimizers.SGD(), loss=FTMSE())
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# 使用已知的点进行预测
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predicted_data = predictByEveryData(trained_model, train_data)
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@ -205,7 +205,7 @@ if __name__ == '__main__':
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#### TODO 测试
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trained_model = tf.keras.models.load_model(save_name, custom_objects={'LSTMLayer': LSTMLayer, 'FTMSE': FTMSE})
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trained_model = tf.keras.models.load_model(save_name, custom_objects={'LSTMLayer': LSTMLayer})
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# 使用已知的点进行预测
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predicted_data = predictByEveryData(trained_model, train_data)
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@ -13,62 +13,139 @@ import tensorflow.keras.layers as layers
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
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from tensorflow.keras.layers import Dense, Dropout, ReLU, BatchNormalization
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from scipy.fftpack import dct
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# def dct(x, norm=None):
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# """
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# Discrete Cosine Transform, Type II (a.k.a. the DCT)
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#
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# For the meaning of the parameter `norm`, see:
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# https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
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#
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# :param x: the input signal
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# :param norm: the normalization, None or 'ortho'
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# :return: the DCT-II of the signal over the last dimension
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# """
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# x_shape = x.shape
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# N = x_shape[-1]
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# x = x.contiguous().view(-1, N)
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#
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#
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# v = tf.concat([x[:, ::2], x[:, 1::2].flip([1])], axis=1)
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#
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# # Vc = torch.fft.rfft(v, 1, onesided=False)
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# Vc = tf.signal.fft(v, 1)
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#
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#
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# k = - tf.range(N, dtype=x.dtype, device=x.device)[None, :] * np.pi / (2 * N)
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#
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# W_r = tf.cos(k)
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# W_i = tf.sin(k)
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#
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# V = Vc[:, :, 0] * W_r - Vc[:, :, 1] * W_i
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#
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# if norm == 'ortho':
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# V[:, 0] /= np.sqrt(N) * 2
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# V[:, 1:] /= np.sqrt(N / 2) * 2
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#
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# V = 2 * V.view(*x_shape)
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#
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# return V
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import tensorflow as tf
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def sdct_tf(signals, frame_length, frame_step, window_fn=tf.signal.hamming_window):
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"""Compute Short-Time Discrete Cosine Transform of `signals`.
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No padding is applied to the signals.
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Parameters
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----------
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signal : Time-domain input signal(s), a `[..., n_samples]` tensor.
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frame_length : Window length and DCT frame length in samples.
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frame_step : Number of samples between adjacent DCT columns.
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window_fn : See documentation for `tf.signal.stft`.
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Default: hamming window. Window to use for DCT.
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Returns
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-------
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dct : Real-valued T-F domain DCT matrix/matrixes, a `[..., n_frames, frame_length]` tensor.
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"""
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framed = tf.signal.frame(signals, frame_length, frame_step, pad_end=False)
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if window_fn is not None:
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window = window_fn(frame_length, dtype=framed.dtype)
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framed = framed * window[tf.newaxis, :]
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return tf.signal.dct(framed, norm="ortho", axis=-1)
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def isdct_tf(dcts, *, frame_step, frame_length=None, window_fn=tf.signal.hamming_window):
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"""Compute Inverse Short-Time Discrete Cosine Transform of `dct`.
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Parameters other than `dcts` are keyword-only.
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Parameters
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----------
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dcts : DCT matrix/matrices from `sdct_tf`
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frame_step : Number of samples between adjacent DCT columns (should be the
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same value that was passed to `sdct_tf`).
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frame_length : Ignored. Window length and DCT frame length in samples.
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Can be None (default) or same value as passed to `sdct_tf`.
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window_fn : See documentation for `tf.signal.istft`.
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Default: hamming window. Window to use for DCT.
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Returns
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-------
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signals : Time-domain signal(s) reconstructed from `dcts`, a `[..., n_samples]` tensor.
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Note that `n_samples` may be different from the original signals' lengths as passed to `sdct_torch`,
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because no padding is applied.
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"""
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*_, n_frames, frame_length2 = dcts.shape
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assert frame_length in {None, frame_length2}
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signals = tf.signal.overlap_and_add(
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tf.signal.idct(dcts, norm="ortho", axis=-1), frame_step
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)
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if window_fn is not None:
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window = window_fn(frame_length2, dtype=signals.dtype)
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window_frames = tf.tile(window[tf.newaxis, :], (n_frames, 1))
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window_signal = tf.signal.overlap_and_add(window_frames, frame_step)
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signals = signals / window_signal
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return signals
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class DCTChannelAttention(layers.Layer):
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def __init__(self):
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# 调用父类__init__()方法
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super(DCTChannelAttention, self).__init__()
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self.DWC = DepthwiseConv1D(kernel_size=1, padding='SAME')
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def build(self, input_shape):
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if len(input_shape) != 3:
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raise ValueError('Inputs to `DynamicChannelAttention` should have rank 3. '
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'Received input shape:', str(input_shape))
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# print(input_shape)
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# GAP
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self.GAP = tf.keras.layers.GlobalAvgPool1D()
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self.c1 = tf.keras.layers.Conv1D(filters=input_shape[2], kernel_size=1, padding='SAME')
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# s1 = tf.nn.sigmoid(c1)
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# GMP
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self.GMP = tf.keras.layers.GlobalMaxPool1D()
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self.c2 = tf.keras.layers.Conv1D(filters=input_shape[2], kernel_size=1, padding='SAME')
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# s2 = tf.nn.sigmoid(c2)
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# weight
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self.weight_kernel = self.add_weight(
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shape=(1, input_shape[2]),
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initializer='glorot_uniform',
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name='weight_kernel')
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_, hidden, channel = input_shape
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self.l1 = Dense(channel * 2, use_bias=False)
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self.drop1 = Dropout(0.1)
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self.relu = ReLU(0.1)
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self.l2 = Dense(channel, use_bias=False)
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def call(self, inputs, **kwargs):
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batch_size, length, channel = inputs.shape
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# print(batch_size,length,channel)
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DWC1 = self.DWC(inputs)
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batch_size, hidden, channel = inputs.shape
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list = []
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stack_dct = tf.signal.dct(inputs, norm="ortho",axis=-1)
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# for i in range(channel):
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# freq = tf.signal.dct(inputs[:, i, :], norm="ortho", axis=-1)
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# # print("freq-shape:",freq.shape)
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# list.append(freq)
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# stack_dct = tf.stack(list, dim=1)
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# GAP
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GAP = self.GAP(DWC1)
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GAP = tf.expand_dims(GAP, axis=1)
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c1 = self.c1(GAP)
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c1 = tf.keras.layers.BatchNormalization()(c1)
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s1 = tf.nn.sigmoid(c1)
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lr_weight = self.l1(stack_dct)
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lr_weight = self.drop1(lr_weight)
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lr_weight = self.relu(lr_weight)
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lr_weight = self.l2(lr_weight)
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# GMP
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GMP = self.GMP(DWC1)
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GMP = tf.expand_dims(GMP, axis=1)
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c2 = self.c2(GMP)
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c2 = tf.keras.layers.BatchNormalization()(c2)
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s2 = tf.nn.sigmoid(c2)
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lr_weight = BatchNormalization()(lr_weight)
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# print(self.weight_kernel)
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weight_kernel = tf.broadcast_to(self.weight_kernel, shape=[length, channel])
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weight_kernel = tf.broadcast_to(weight_kernel, shape=[batch_size, length, channel])
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s1 = tf.broadcast_to(s1, shape=[batch_size, length, channel])
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s2 = tf.broadcast_to(s2, shape=[batch_size, length, channel])
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output = tf.add(weight_kernel * s1 * inputs, (tf.ones_like(weight_kernel) - weight_kernel) * s2 * inputs)
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return output
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return inputs * lr_weight
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@ -64,15 +64,6 @@ class LightChannelAttention(layers.Layer):
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c1 = tf.keras.layers.BatchNormalization()(c1)
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s1 = tf.nn.sigmoid(c1)
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# # GMP
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# GMP = self.GMP(DWC1)
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# GMP = tf.expand_dims(GMP, axis=1)
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# c2 = self.c2(GMP)
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# c2 = tf.keras.layers.BatchNormalization()(c2)
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# s2 = tf.nn.sigmoid(c2)
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# print(self.weight_kernel)
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# weight_kernel = tf.broadcast_to(self.weight_kernel, shape=[length, channel])
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# weight_kernel = tf.broadcast_to(weight_kernel, shape=[batch_size, length, channel])
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s1 = tf.broadcast_to(s1, shape=[batch_size, length, channel])
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@ -82,6 +73,42 @@ class LightChannelAttention(layers.Layer):
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return s1
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class LightChannelAttention1(layers.Layer):
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def __init__(self):
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# 调用父类__init__()方法
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super(LightChannelAttention1, self).__init__()
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self.DWC = DepthwiseConv1D(kernel_size=1, padding='SAME')
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# self.DWC = DepthwiseConv1D(kernel_size=1, padding='causal',dilation_rate=4,data_format='channels_last')
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def build(self, input_shape):
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if len(input_shape) != 3:
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raise ValueError('Inputs to `DynamicChannelAttention` should have rank 3. '
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'Received input shape:', str(input_shape))
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print(input_shape)
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# GAP
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self.GAP = tf.keras.layers.GlobalAvgPool1D()
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self.c1 = tf.keras.layers.Conv1D(filters=input_shape[2], kernel_size=1, padding='SAME')
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def call(self, inputs, **kwargs):
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batch_size, length, channel = inputs.shape
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DWC1 = self.DWC(inputs)
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# GAP
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GAP = self.GAP(DWC1)
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GAP = tf.expand_dims(GAP, axis=1)
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c1 = self.c1(GAP)
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c1 = tf.keras.layers.BatchNormalization()(c1)
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s1 = tf.nn.sigmoid(c1)
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print(s1)
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s1 = tf.broadcast_to(s1, [batch_size, length, channel])
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return s1 * inputs
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class DynamicPooling(layers.Layer):
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def __init__(self, pool_size=2):
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@ -12,35 +12,42 @@ import tensorflow.keras.backend as K
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class FTMSE(tf.keras.losses.Loss):
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def call(self, y_true, y_pred):
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y_true = tf.cast(y_true, tf.float32)
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y_pred = tf.cast(y_pred, tf.float32)
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# y_true = tf.cast(y_true, tf.float32)
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# y_pred = tf.cast(y_pred, tf.float32)
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# tf.print(y_true)
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# tf.print(y_pred)
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# 需要转为复数形式
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yt_fft = tf.signal.fft(tf.cast(y_true, tf.complex64))
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yp_fft = tf.signal.fft(tf.cast(y_pred, tf.complex64))
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_, length = y_pred.shape
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print("yt_amp",yt_fft)
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print("yp_fft",yp_fft)
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# 打印精确的实部和虚部
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yt_fft = tf.signal.fft(tf.complex(y_true, tf.zeros_like(y_true)))
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yp_fft = tf.signal.fft(tf.complex(y_pred, tf.zeros_like(y_pred)))
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epoch, length = yp_fft.shape
|
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# 幅值
|
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yt_amp = tf.abs(yt_fft/length)
|
||||
yp_amp = tf.abs(yp_fft/length)
|
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# yt_amp = tf.abs(yt_fft)
|
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# yp_amp = tf.abs(yp_fft)
|
||||
# 相角
|
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yt_angle = tf.math.angle(yt_fft)
|
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yp_angle = tf.math.angle(yp_fft)
|
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|
||||
# tf.print("yt_amp",yt_amp)
|
||||
# tf.print("yp_amp",yp_amp)
|
||||
# tf.print("yt_angle",yt_angle)
|
||||
# tf.print("yp_angle",yp_angle)
|
||||
|
||||
time_loss = K.mean(tf.keras.losses.mean_squared_error(y_true, y_pred),axis=-1)
|
||||
amp_loss = K.mean(tf.keras.losses.mean_squared_error(yt_amp, yp_amp),axis=-1)
|
||||
angle_loss = K.mean(tf.keras.losses.mean_squared_error(yt_angle, yp_angle),axis=-1)
|
||||
tf.print("time_loss:", time_loss, "amp_loss", amp_loss, "angle_loss", angle_loss)
|
||||
|
||||
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 + amp_loss*5
|
||||
# ftLoss = time_loss + 5 * amp_loss + 0.25 * angle_loss
|
||||
# ftLoss = time_loss
|
||||
|
||||
#
|
||||
return ftLoss
|
||||
|
|
|
|||
Loading…
Reference in New Issue