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ANN for EIS prediction

notebook

微信用户DiH0

更新于 2024-12-25

推荐镜像 :Third-party software:ai4s-cup-0.1

推荐机型 :c2_m4_cpu

数据集

AI4S-Cup-PulseEIS(v5)

[90]

# 载入各类包

import torch

import math

import torch.nn as nn

import torch.optim as optim

from torch.nn.utils.rnn import pad_sequence

import torch.nn.functional as F

import random

import os

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import pickle

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[91]

# 定义随机种子，保证每次训练、预测结果固定

def setup_seed(seed):

np.random.seed(seed) # Numpy module.

random.seed(seed) # Python random module.

os.environ['PYTHONHASHSEED'] = str(seed)

torch.manual_seed(seed)

if torch.cuda.is_available():

torch.cuda.manual_seed(seed)

torch.cuda.manual_seed_all(seed)

torch.backends.cudnn.benchmark = False

torch.backends.cudnn.deterministic = True

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[92]

# 初始化随机种子

setup_seed(0)

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[93]

# 多层感知器模型建立

class MLPModel(nn.Module):

def __init__(self):

super(MLPModel, self).__init__()

self.fc1 = nn.Linear(99, 128)

self.fc2 = nn.Linear(128, 102)

self.relu = nn.ReLU()

def forward(self, x):

x = x.view(-1, 99)

x = self.relu(self.fc1(x))

x = self.fc2(x)

x = x.view(-1, 51, 2)

return x

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[94]

# 模型函数建立

model = MLPModel()

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[95]

# 定义输入函数

def input_mlp(datasets, mode):

input_data = []

soc_lst = [f'{i*2}%SOC' for i in range(49)]

for i in datasets:

with open(f'/bohr/ai4spulseeis-lr97/v5/{mode}_datasets/{mode}_pulse_{i}.pkl', 'rb') as fp:

pulse_data = pickle.load(fp, encoding='bytes')

for soc in soc_lst:

Vol = pulse_data[soc]['Voltage']

tensor_vol = torch.tensor(Vol).view(1,99,1)

input_data.append(tensor_vol)

return input_data

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[96]

# 准备输入变量

train_baty = [1,2,3,4,5,6]

test_baty = [1,2]

input_train = input_mlp(datasets = train_baty, mode = 'train')

input_test = input_mlp(datasets = test_baty, mode = 'test')

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[97]

# 定义目标函数

def target_mlp(datasets, mode):

target_data = []

soc_lst = [f'{i*2}%SOC' for i in range(49)]

EIS_list = []

for k in datasets:

with open(f'/bohr/ai4spulseeis-lr97/v5/{mode}_datasets/{mode}_eis_{k}.pkl', 'rb') as fp:

eis_data = pickle.load(fp, encoding='bytes')

for soc in soc_lst:

EIS_tot = [[],[]]

re = eis_data[soc]['Real']

im = eis_data[soc]['Imaginary']

EIS_tot[0] = re

EIS_tot[1] = im

EIS_list.append(EIS_tot)

EIS_list = [np.array(t).squeeze().T for t in EIS_list]

target_data = [torch.tensor(t).float().view(1, 51, 2) for t in EIS_list]

return target_data

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[98]

# 准备目标函数

target_train = target_mlp(datasets = train_baty, mode = 'train')

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[99]

# 规定模型的基本参量

criterion = nn.MSELoss()

optimizer = optim.Adam(model.parameters(), lr=1e-4)

scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.8, patience=40, verbose=True, min_lr=5e-6)

num_epochs = 5000

batch_size = 8

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[100]

#测试模型epoch的大小对RMSE的影响

train_losses = [] # 记录每个epoch的训练损失

for epoch in range(num_epochs):

epoch_loss = 0.0

train_size = len(input_train)

for batch_idx in range(0, train_size, batch_size):

batch_input = torch.cat(input_train[batch_idx:batch_idx+batch_size], dim=0)

batch_target = torch.cat(target_train[batch_idx:batch_idx+batch_size], dim=0)

optimizer.zero_grad()

outputs = model(batch_input)

loss = criterion(outputs, batch_target)

loss.backward()

optimizer.step()

epoch_loss += loss.item() * batch_input.shape[0]

scheduler.step(epoch_loss / (train_size / batch_size))

train_losses.append(epoch_loss / (train_size / batch_size))

if (epoch + 1) % 50 == 0:

mean_mse = epoch_loss / (train_size / batch_size)

rmse = mean_mse ** 0.5

print(f"Epoch [{epoch + 1}/{num_epochs}] | Loss: {mean_mse:.4f} | RMSE: {rmse:.4f}")

Epoch [50/5000] | Loss: 42.0119 | RMSE: 6.4817
Epoch 00062: reducing learning rate of group 0 to 8.0000e-05.
Epoch [100/5000] | Loss: 41.9762 | RMSE: 6.4789
Epoch 00103: reducing learning rate of group 0 to 6.4000e-05.
Epoch 00146: reducing learning rate of group 0 to 5.1200e-05.
Epoch [150/5000] | Loss: 41.7950 | RMSE: 6.4649
Epoch [200/5000] | Loss: 41.7837 | RMSE: 6.4640
Epoch [250/5000] | Loss: 41.7694 | RMSE: 6.4629
Epoch [300/5000] | Loss: 41.7548 | RMSE: 6.4618
Epoch [350/5000] | Loss: 41.7400 | RMSE: 6.4607
Epoch [400/5000] | Loss: 41.7252 | RMSE: 6.4595
Epoch [450/5000] | Loss: 41.7105 | RMSE: 6.4584
Epoch [500/5000] | Loss: 41.6957 | RMSE: 6.4572
Epoch [550/5000] | Loss: 41.6809 | RMSE: 6.4561
Epoch [600/5000] | Loss: 41.6661 | RMSE: 6.4549
Epoch [650/5000] | Loss: 41.6513 | RMSE: 6.4538
Epoch [700/5000] | Loss: 41.6365 | RMSE: 6.4526
Epoch [750/5000] | Loss: 41.6217 | RMSE: 6.4515
Epoch [800/5000] | Loss: 41.6069 | RMSE: 6.4503
Epoch [850/5000] | Loss: 41.5921 | RMSE: 6.4492
Epoch [900/5000] | Loss: 41.5772 | RMSE: 6.4480
Epoch [950/5000] | Loss: 41.5624 | RMSE: 6.4469
Epoch [1000/5000] | Loss: 41.5475 | RMSE: 6.4457
Epoch [1050/5000] | Loss: 41.5326 | RMSE: 6.4446
Epoch [1100/5000] | Loss: 41.5178 | RMSE: 6.4434
Epoch [1150/5000] | Loss: 41.5029 | RMSE: 6.4423
Epoch [1200/5000] | Loss: 41.4880 | RMSE: 6.4411
Epoch [1250/5000] | Loss: 41.4730 | RMSE: 6.4400
Epoch [1300/5000] | Loss: 41.4581 | RMSE: 6.4388
Epoch [1350/5000] | Loss: 41.4431 | RMSE: 6.4376
Epoch [1400/5000] | Loss: 41.4282 | RMSE: 6.4365
Epoch [1450/5000] | Loss: 41.4132 | RMSE: 6.4353
Epoch [1500/5000] | Loss: 41.3981 | RMSE: 6.4341
Epoch [1550/5000] | Loss: 41.3831 | RMSE: 6.4330
Epoch [1600/5000] | Loss: 41.3681 | RMSE: 6.4318
Epoch [1650/5000] | Loss: 41.3530 | RMSE: 6.4306
Epoch [1700/5000] | Loss: 41.3379 | RMSE: 6.4295
Epoch [1750/5000] | Loss: 41.3228 | RMSE: 6.4283
Epoch [1800/5000] | Loss: 41.3076 | RMSE: 6.4271
Epoch [1850/5000] | Loss: 41.2925 | RMSE: 6.4259
Epoch [1900/5000] | Loss: 41.2773 | RMSE: 6.4247
Epoch [1950/5000] | Loss: 41.2621 | RMSE: 6.4236
Epoch [2000/5000] | Loss: 41.2468 | RMSE: 6.4224
Epoch [2050/5000] | Loss: 41.2315 | RMSE: 6.4212
Epoch [2100/5000] | Loss: 41.2162 | RMSE: 6.4200
Epoch [2150/5000] | Loss: 41.2009 | RMSE: 6.4188
Epoch [2200/5000] | Loss: 41.1855 | RMSE: 6.4176
Epoch [2250/5000] | Loss: 41.1701 | RMSE: 6.4164
Epoch [2300/5000] | Loss: 41.1547 | RMSE: 6.4152
Epoch [2350/5000] | Loss: 41.1393 | RMSE: 6.4140
Epoch [2400/5000] | Loss: 41.1238 | RMSE: 6.4128
Epoch [2450/5000] | Loss: 41.1082 | RMSE: 6.4116
Epoch [2500/5000] | Loss: 41.0927 | RMSE: 6.4104
Epoch [2550/5000] | Loss: 41.0771 | RMSE: 6.4091
Epoch [2600/5000] | Loss: 41.0614 | RMSE: 6.4079
Epoch [2650/5000] | Loss: 41.0458 | RMSE: 6.4067
Epoch [2700/5000] | Loss: 41.0300 | RMSE: 6.4055
Epoch [2750/5000] | Loss: 41.0143 | RMSE: 6.4042
Epoch [2800/5000] | Loss: 40.9985 | RMSE: 6.4030
Epoch [2850/5000] | Loss: 40.9826 | RMSE: 6.4018
Epoch [2900/5000] | Loss: 40.9667 | RMSE: 6.4005
Epoch [2950/5000] | Loss: 40.9508 | RMSE: 6.3993
Epoch [3000/5000] | Loss: 40.9348 | RMSE: 6.3980
Epoch [3050/5000] | Loss: 40.9188 | RMSE: 6.3968
Epoch [3100/5000] | Loss: 40.9027 | RMSE: 6.3955
Epoch [3150/5000] | Loss: 40.8866 | RMSE: 6.3943
Epoch [3200/5000] | Loss: 40.8704 | RMSE: 6.3930
Epoch [3250/5000] | Loss: 40.8541 | RMSE: 6.3917
Epoch [3300/5000] | Loss: 40.8379 | RMSE: 6.3905
Epoch [3350/5000] | Loss: 40.8215 | RMSE: 6.3892
Epoch [3450/5000] | Loss: 40.7886 | RMSE: 6.3866
Epoch [3500/5000] | Loss: 40.7721 | RMSE: 6.3853
Epoch [3550/5000] | Loss: 40.7555 | RMSE: 6.3840
Epoch [3600/5000] | Loss: 40.7389 | RMSE: 6.3827
Epoch [3650/5000] | Loss: 40.7222 | RMSE: 6.3814
Epoch [3700/5000] | Loss: 40.7054 | RMSE: 6.3801
Epoch [3750/5000] | Loss: 40.6886 | RMSE: 6.3788
Epoch [3800/5000] | Loss: 40.6717 | RMSE: 6.3774
Epoch [3850/5000] | Loss: 40.6547 | RMSE: 6.3761
Epoch [3900/5000] | Loss: 40.6376 | RMSE: 6.3748
Epoch [3950/5000] | Loss: 40.6205 | RMSE: 6.3734
Epoch [4000/5000] | Loss: 40.6033 | RMSE: 6.3721
Epoch [4050/5000] | Loss: 40.5860 | RMSE: 6.3707
Epoch [4100/5000] | Loss: 40.5687 | RMSE: 6.3694
Epoch [4150/5000] | Loss: 40.5512 | RMSE: 6.3680
Epoch [4200/5000] | Loss: 40.5337 | RMSE: 6.3666
Epoch [4250/5000] | Loss: 40.5161 | RMSE: 6.3652
Epoch [4300/5000] | Loss: 40.4984 | RMSE: 6.3638
Epoch [4350/5000] | Loss: 40.4806 | RMSE: 6.3624
Epoch [4400/5000] | Loss: 40.4627 | RMSE: 6.3610
Epoch [4450/5000] | Loss: 40.4448 | RMSE: 6.3596
Epoch [4500/5000] | Loss: 40.4267 | RMSE: 6.3582
Epoch [4550/5000] | Loss: 40.4085 | RMSE: 6.3568
Epoch [4600/5000] | Loss: 40.3903 | RMSE: 6.3553
Epoch [4650/5000] | Loss: 40.3719 | RMSE: 6.3539
Epoch [4700/5000] | Loss: 40.3534 | RMSE: 6.3524
Epoch [4750/5000] | Loss: 40.3348 | RMSE: 6.3510
Epoch [4800/5000] | Loss: 40.3161 | RMSE: 6.3495
Epoch [4850/5000] | Loss: 40.2973 | RMSE: 6.3480
Epoch [4900/5000] | Loss: 40.2784 | RMSE: 6.3465
Epoch [4950/5000] | Loss: 40.2593 | RMSE: 6.3450
Epoch [5000/5000] | Loss: 40.2401 | RMSE: 6.3435

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[101]

# 绘制训练过程中loss的变化曲线(对数作图)

losses_log = [math.log(t) for t in train_losses]

plt.plot(losses_log, label='Training loss')

plt.xlabel('Epochs')

plt.ylabel('MSE Loss')

plt.legend()

plt.show()

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[102]

# 使用测试数据进行预测

def evaluation():

model.eval()

outputs_eval = []

for batch_idx in range(0, len(input_test), batch_size):

batch_input = torch.cat(input_test[batch_idx:batch_idx+batch_size], dim=0)

with torch.no_grad():

output = model(batch_input)

outputs_eval.append(output)

return outputs_eval

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[103]

outputs_eval = evaluation()

predict_outputs = torch.cat(outputs_eval, dim=0).view(1, len(input_test)*51, 2).tolist()

predict_data = np.array(predict_outputs).squeeze().T

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[104]

# 给出需要提交的数据结果

results_data = {}

results_data['test_data_number'] = [1 for i in range(49*51)] + [2 for i in range(49*51)]

results_data['SOC(%)'] = []

for i in range(49):

results_data['SOC(%)'] += [i*2 for j in range(51)]

for i in range(49):

results_data['SOC(%)'] += [i*2 for j in range(51)]

results_data['EIS_real'] = predict_data[0].tolist()

results_data['EIS_imaginary'] = predict_data[1].tolist()

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[105]

# 对EIS预测结果作图

for i in range(49):

plt.plot(predict_data[0][51*i:51*(i+1)],predict_data[1][51*i:51*(i+1)],'o-')

plt.title('predict EIS RMSE: 6.3435')

plt.xlabel('Z_re')

plt.ylabel('Z_im')

plt.show()

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