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EEGNet_training_ReLU.py
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EEGNet_training_ReLU.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jul 9 17:01:35 2021
@author: haoyuan
"""
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
from dataloader import read_bci_data
import matplotlib.pyplot as plt
from torch.utils.data import TensorDataset,DataLoader
import numpy as np
import torch.optim as optim
import pandas as pd
from torchsummary import summary
import os
import argparse
def testing(x_test,y_test,model,device,filepath):
# model.load_state_dict(torch.load(filepath))
model.eval()
with torch.no_grad():
model.cuda(0)
n = x_test.shape[0]
x_test = x_test.astype("float32")
y_test = y_test.astype("float32").reshape(y_test.shape[0],)
x_test, y_test = Variable(torch.from_numpy(x_test)),Variable(torch.from_numpy(y_test))
x_test,y_test = x_test.to(device),y_test.to(device)
y_pred_test = model(x_test)
correct_test = (torch.max(y_pred_test,1)[1]==y_test).sum().item()
test_accuracy = correct_test/n
# print("testing accuracy:",correct/n)
return test_accuracy
torch.manual_seed(1) # reproducible
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default='700', help='training epochs')
parser.add_argument('--learning_rate', type=float, default='1e-3', help='learning rate')
parser.add_argument('--save_model', action='store_true', help='check if you want to save the model.')
parser.add_argument('--save_csv', action='store_true', help='check if you want to save the training history.')
opt = parser.parse_args()
epochs = 750
lr = 1e-3
filepath = os.path.abspath(os.path.dirname(__file__))+"\checkpoint\EEGNet_checkpoint_ReLU.rar"
filepath_csv = os.path.abspath(os.path.dirname(__file__))+"\history_csv\EEGNet_ReLU.csv"
min_loss=1
max_accuracy = 0
device = torch.device("cuda:0")
train_data, train_label, test_data, test_label = read_bci_data()
n = train_data.shape[0]
train_data = train_data.astype("float32")
train_label = train_label.astype("float32").reshape(train_label.shape[0],)
# train_data.shape = (1080,1,2,750)
# train_label.shape = (1080,)
# loader = DataLoader(TensorDataset(train_data,train_label),batch_size=8)
x, y = Variable(torch.from_numpy(train_data)),Variable(torch.from_numpy(train_label))
y=torch.tensor(y, dtype=torch.long)
class EEGNet_ReLU(torch.nn.Module):
def __init__(self, n_output):
super(EEGNet_ReLU, self).__init__()
self.firstConv = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=(1,51), stride=(1,1), padding=(0,25),bias=False),
nn.BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
self.depthwiseConv = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=(2,1), stride=(1,1), groups=8,bias=False),
nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
nn.ReLU(),
nn.AvgPool2d(kernel_size=(1,4), stride=(1,4),padding=0),
nn.Dropout(p=0.35)
)
self.separableConv = nn.Sequential(
nn.Conv2d(32, 32, kernel_size=(1,15), stride=(1,1), padding=(0,7),bias=False),
nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
nn.ReLU(),
nn.AvgPool2d(kernel_size=(1,8), stride=(1,8),padding=0),
nn.Dropout(p=0.35)
)
self.classify = nn.Sequential(
nn.Flatten(),
nn.Linear(736,n_output,bias=True)
)
def forward(self, x):
out = self.firstConv(x)
out = self.depthwiseConv(out)
out = self.separableConv(out)
out = self.classify(out)
return out
model = EEGNet_ReLU(n_output=2)
print(model)
criterion = nn.CrossEntropyLoss()
# optimizer = optim.Adam(model.parameters(),lr = lr)
optimizer = optim.RMSprop(model.parameters(),lr = lr, momentum = 0.2)
# optimizer = optim.SGD(model.parameters(), lr=1, momentum=0.5, weight_decay=5e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100,300,500], gamma=0.1)
model.cuda(0)
summary(model.cuda(),(1,2,750))
loss_history = []
train_accuracy_history = []
test_accuracy_history = []
for epoch in range(epochs):
# for idx,(data,target) in enumerate(loader):
model.train()
x,y = x.to(device),y.to(device)
y_pred = model(x)
# print(y_pred.shape)
# print(y.shape)
# loss = F.mse_loss(y_pred, y)
loss = criterion(y_pred, y)
train_loss = loss.item()
loss_history.append(train_loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# scheduler.step()
if epoch%1==0:
# correct= (y_pred.ge(0.5) == y).sum().item()
n = y.shape[0]
correct = (torch.max(y_pred,1)[1]==y).sum().item()
train_accuracy = correct / n
train_accuracy_history.append(train_accuracy)
# print("epochs:",epoch,"loss:",loss.item(),"Accuracy:",(correct / n),"Learning rate:",scheduler.get_last_lr()[0])
test_accuracy = testing(test_data,test_label,model,device,filepath)
test_accuracy_history.append(test_accuracy)
print("epochs:",epoch,"loss:",train_loss,"Training Accuracy:",train_accuracy,"Testing Accuracy:",test_accuracy,"Learning rate:",scheduler.get_last_lr()[0])
if train_loss<min_loss:
min_loss = train_loss
# torch.save(model.state_dict(), filepath)
if train_accuracy>max_accuracy:
max_accuracy = train_accuracy
if opt.save_model:
torch.save(model.state_dict(), filepath)
print("最大的Accuracy為:",max_accuracy,"最小的Loss值為:",min_loss)
df = pd.DataFrame({"loss":loss_history,"train_accuracy_history":train_accuracy_history,"test_accuracy_history":test_accuracy_history})
if opt.save_csv:
df.to_csv(filepath_csv,encoding="utf-8-sig")