def train(do_k_fold, out_dir, log_f):
if do_k_fold:
utils.print_out("# do k_fold k=%d" % k_fold, log_f)
k_fold_val = 0
k_fold_tra = 0
[k_TP, k_TN, k_FP, k_FN, k_SE, k_SP, k_MCC, k_ACC] = [0, 0, 0, 0, 0, 0, 0, 0]
for i in range(k_fold):
trX_=[]
trY_=[]
for j in range(k_fold):
if j == i: continue
trX_.append(k_fold_X[j])
trY_.append(k_fold_Y[j])
trX_ = np.concatenate(trX_)
trY_ = np.concatenate(trY_)
utils.print_out("#k_fold %d" % i, log_f)
utils.print_out("#do DBN ...", log_f)
dbn = DBN()
dbn.train(trX_)
utils.print_out("#end DBN", log_f)
utils.print_out("#do caps ...", log_f)
capsNet = CapsNet(is_training=True, dbn=dbn)
i_k_fold_val, i_k_fold_tra = capsNet.train(trX_, trY_, k_fold_X[i], k_fold_Y[i], None, log_f)
TP, TN, FP, FN, SE, SP, MCC, ACC = eva(capsNet, k_fold_X[i], k_fold_Y[i])
print(i,", TP:", TP)
print(i,", TN:", TN)
print(i,", FP:", FP)
print(i,", FN:", FN)
print(i,", SE:", SE)
print(i,", SP:", SP)
print(i,", MCC:", MCC)
print(i,", ACC: ", ACC)
k_TP += TP
k_TN += TN
k_FP += FP
k_FN += FN
k_SE += SE
k_SP += SP
k_MCC += MCC
k_ACC += ACC
print("TP :", k_TP / 5)
print("TN :", k_TN / 5)
print("FP :", k_FP / 5)
print("FN :", k_FN / 5)
print("SE :", k_SE / 5)
print("SP :", k_SP / 5)
print("MCC: ", k_MCC / 5)
print("ACC: ", k_ACC / 5)
else:
utils.print_out("#do DBN ...", log_f)
dbn = DBN()
dbn.train(trX)
utils.print_out("#end DBN", log_f)
utils.print_out("#do caps ...", log_f)
utils.print_out("#test instead val set for test ...", log_f)
capsNet = CapsNet(is_training=isTraining, dbn=dbn)
if isTraining:
i_k_fold_val, i_k_fold_tra = capsNet.train(trX, trY, teX, teY, "./board", log_f)
utils.print_out("#end caps", log_f)
tr_TP, tr_TN, tr_FP, tr_FN, tr_SE, tr_SP, tr_MCC, tr_ACC = eva(capsNet, trX, trY)
val_TP, val_TN, val_FP, val_FN, val_SE, val_SP, val_MCC, val_ACC = eva(capsNet, vaX, vaY)
te_P, te_TN, te_FP, te_FN, te_SE, te_SP, te_MCC,te_ACC = eva(capsNet, teX, teY)
utils.print_out('train : TP:%.3f; TN:%.3f; FP:%.3f; FN:%.3f; SE:%.3f SP:%.3f MCC:%.3f P:%.3f' \
%(tr_TP, tr_TN, tr_FP, tr_FN, tr_SE, tr_SP, tr_MCC, tr_ACC), log_f)
utils.print_out('val : TP:%.3f; TN:%.3f; FP:%.3f; FN:%.3f; SE:%.3f SP:%.3f MCC:%.3f P:%.3f' \
% (val_TP, val_TN, val_FP, val_FN, val_SE, val_SP, val_MCC, val_ACC), log_f)
utils.print_out('test : TP:%.3f; TN:%.3f; FP:%.3f; FN:%.3f; SE:%.3f SP:%.3f MCC:%.3f P:%.3f' \
% (te_P, te_TN, te_FP, te_FN, te_SE, te_SP, te_MCC, te_ACC), log_f)
else:
import csv
csvFile = open("./"+train_datadir+"/"+setFileNames[1], "r")
reader = csv.reader(csvFile) # 返回的是迭代类型
data = []
for item in reader:
data.append(item[0])
csvFile.close()
data = data[1:]
utils.print_out("#end caps", log_f)
pre_Y= pre(capsNet, vaX).tolist()[0]
import pandas as pd
dataFrame = pd.DataFrame({ "0_name": data,"1_class": pre_Y})
dataFrame.to_csv('./data_set/test_dir/180831-result.csv', index=False, sep=",")
dataset=dataset,
data_path='data/char74k_preprocessed')
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
capsNet = CapsNet(conv_in=3, num_classes=num_classes).to(device)
cseloss = torch.nn.CrossEntropyLoss()
adamOptimizer = torch.optim.Adam(params=capsNet.parameters())
best_val_acc = -1
best_val_epoch = 0
for epoch in tqdm(range(epochs)):
epoch_loss = 0
for batch_id, (data, target) in enumerate(dataProcessor.train_loader):
capsNet.train()
target = torch.eye(num_classes).index_select(dim=0, index=target)
data = data.to(device)
target = target.to(device)
adamOptimizer.zero_grad()
# output,reconstructions,masked = capsNet(data)
output = capsNet(data)
# print(output.shape)
# loss = capsNet.loss(data,reconstructions,target,output)
loss = cseloss(output, target.argmax(dim=-1))
loss.backward()
adamOptimizer.step()
epoch_loss += loss