import numpy as np import pandas as pd from keras.models import Sequential from keras.layers import Dense, LSTM # 读取csv文件 data = pd.read_csv('3c_left_1-6.csv') # 将数据转换为numpy数组 data = np.array(data) data = data.reshape((data.shape[0], 1, data.shape[1])) # 获取数据的维度信息 n_samples, n_timesteps, n_features = data.shape # 定义模型 model = Sequential() model.add(LSTM(64, input_shape=(n_timesteps, n_features), return_sequences=True)) model.add(Dense(n_features)) # 编译模型 model.compile(loss='mse', optimizer='adam') # 训练模型 model.fit(data, data, epochs=1, batch_size=32) # 对数据进行去噪 denoised_data = model.predict(data) # 计算去噪后的SNR,MSE,PSNR snr = np.mean(np.power(data, 2)) / np.mean(np.power(data - denoised_data, 2)) mse = np.mean(np.power(data - denoised_data, 2)) psnr = 10 * np.log10((np.power(data.max(), 2) / mse)) print("Signal-to-Noise Ratio (SNR): {:.2f} dB".format(snr)) print("Mean Squared Error (MSE): {:.2f}".format(mse)) print("Peak Signal-to-Noise Ratio (PSNR): {:.2f} dB".format(psnr)) data = {'SNR': [snr], 'MSE': [mse], 'PSNR': [psnr]} df = pd.DataFrame(data) df.to_csv('indicator_lstm.csv', index=False) # 将结果保存为csv文件 denoised_data = pd.DataFrame(denoised_data.reshape(n_samples, n_timesteps * n_features)) denoised_data.to_csv('denoised_data_lstm.csv', index=False)为该代码添加防止过拟合

在LSTM层后添加一个Dropout层，可以帮助防止过拟合。例如，将代码修改为：

import numpy as np 
import pandas as pd 
from keras.models import Sequential 
from keras.layers import Dense, LSTM, Dropout 

# 读取csv文件 
data = pd.read_csv('3c_left_1-6.csv') 

# 将数据转换为numpy数组 
data = np.array(data) 
data = data.reshape((data.shape[0], 1, data.shape[1])) 

# 获取数据的维度信息 
n_samples, n_timesteps, n_features = data.shape 

# 定义模型 
model = Sequential() 
model.add(LSTM(64, input_shape=(n_timesteps, n_features), return_sequences=True)) 
model.add(Dropout(0.2)) # 添加Dropout层
model.add(Dense(n_features)) 

# 编译模型 
model.compile(loss='mse', optimizer='adam') 

# 训练模型 
model.fit(data, data, epochs=1, batch_size=32) 

# 对数据进行去噪 
denoised_data = model.predict(data) 

# 计算去噪后的SNR,MSE,PSNR 
snr = np.mean(np.power(data, 2)) / np.mean(np.power(data - denoised_data, 2)) 
mse = np.mean(np.power(data - denoised_data, 2)) 
psnr = 10 * np.log10((np.power(data.max(), 2) / mse)) 

print("Signal-to-Noise Ratio (SNR): {:.2f} dB".format(snr)) 
print("Mean Squared Error (MSE): {:.2f}".format(mse)) 
print("Peak Signal-to-Noise Ratio (PSNR): {:.2f} dB".format(psnr)) 

data = {'SNR': [snr], 'MSE': [mse], 'PSNR': [psnr]} 
df = pd.DataFrame(data) 
df.to_csv('indicator_lstm.csv', index=False) 

# 将结果保存为csv文件 
denoised_data = pd.DataFrame(denoised_data.reshape(n_samples, n_timesteps * n_features)) 
denoised_data.to_csv('denoised_data_lstm.csv', index=False)

在这个例子中，我们添加了一个Dropout层，设置了dropout概率为0.2。这意味着在每个训练批次中，该层的20%神经元将随机失活。这有助于防止过拟合，提高模型的泛化能力。您还可以尝试不同的dropout概率来获得最佳的性能。