def run(args, env, agent, ram, env_params):
if args.mode == 'train' and (not args.HER):
training(args, env, agent, ram, env_params)
elif args.mode == 'train' and args.HER:
her_training(args, env, agent, ram, env_params)
else:
evaluating(args, env, agent, args.max_episodes - 1)
def main(args):
start_time = time.time()
if args.training:
training(args)
if args.testing:
testing(args)
end_time = round((time.time() - start_time) / 60, 4)
print(f'Done!; {end_time}min spend')
def main(args):
# Time setting
total_start_time = time.time()
if args.preprocessing:
preprocessing(args)
if args.training:
training(args)
# Time calculate
print(f'Done! ; {round((time.time()-total_start_time)/60, 3)}min spend')
def train(params,id):
"this is to run the training once and get the final loss"
# make the folders
save_model_path = os.path.join(root_save_dir, str(id))
save_result_path = os.path.join(save_model_path,"test_results")
save_log_path = os.path.join(root_log_dir, "{}.txt".format(id))
if (not os.path.exists(save_model_path)): os.makedirs(save_model_path)
if (not os.path.exists(save_result_path)): os.makedirs(save_result_path)
# perform training
train_results = training(params=params, logger=LoggerGenerator.get_logger(
log_file_path=save_log_path), save_model_to=save_model_path,train_func=do_forward_pass)
# plot loss vs. iterations
lines = [str(l) for l in train_results["code_loss_records"]]
plot_trend_graph(var_names=["code loss"], var_indexes=[-1], var_types=["float"], var_colors=["r"], lines=lines,
title="code loss",save_to=os.path.join(save_result_path,"train-code_loss.png"),show_fig=False)
lines = [str(l) for l in train_results["discr_loss_records"]]
plot_trend_graph(var_names=["discriminator loss"], var_indexes=[-1], var_types=["float"], var_colors=["r"], lines=lines,
title="discriminator loss", save_to=os.path.join(save_result_path, "train-discriminator_loss.png"), show_fig=False)
lines = [str(l) for l in train_results["discr_acc_records"]]
plot_trend_graph(var_names=["discriminator acc"], var_indexes=[-1], var_types=["float"], var_colors=["r"], lines=lines,
title="discriminator acc", save_to=os.path.join(save_result_path, "train-discriminator_acc.png"), show_fig=False)
with open(os.path.join(save_result_path,"train_records.txt"),"w") as f:
f.write(str(train_results))
print("finish training for parameter set #{}".format(id))
# perform testing
results = run_simple_test(params=params, saved_model_path=save_model_path,model_def=test_model)
# save test results
results["records"]["precision-recall-curve.jpg"].save(os.path.join(save_result_path,"precision-recall.png"))
with open(os.path.join(save_result_path,"metrics.txt"),"w") as f:
f.write(str(results["results"]))
print("finish testing for parameter set #{}".format(id))
def main(args):
# Time setting
total_start_time = time.time()
preprocessing
if args.preprocessing:
preprocessing(args)
# Augmentation by NER_Masking
if args.augmenting:
augmenting(args)
# training
if args.training:
training(args)
# Time calculate
print(f'Done! ; {round((time.time()-total_start_time)/60, 3)}min spend')
def main():
dataTrain, labelsTrain = train.train_imgToArray()
cowsTrain, labelsTrain = train.train_convertToNumpy(dataTrain, labelsTrain)
x_train, y_train = train.train_createData(cowsTrain, labelsTrain)
model = train.kerasModel()
model = train.training(model, x_train, y_train)
dataTest, labelsTest = test.test_imgToArray()
cowsTest, labelsTest = test.test_convertToNumpy(dataTest, labelsTest)
x_test, y_test = test.test_createData(cowsTest, labelsTest)
test.testing(model, x_test, y_test)
def union(dataset_dict, args, dump_path):
print('start data load domain-union')
union_train_x = []
union_test_x = []
union_train_ga = []
union_test_ga = []
union_train_o = []
union_test_o = []
union_train_ni = []
union_test_ni = []
for domain in domain_dict:
size = math.ceil(
len(dataset_dict['{0}_x'.format(domain)]) * args.train_test_ratio)
union_train_x += dataset_dict['{0}_x'.format(domain)][:size]
union_test_x += dataset_dict['{0}_x'.format(domain)][size:]
union_train_ga += dataset_dict['{0}_y_ga'.format(domain)][:size]
union_test_ga += dataset_dict['{0}_y_ga'.format(domain)][size:]
train_data = tuple_dataset.TupleDataset(union_train_x, union_train_ga)
test_data = tuple_dataset.TupleDataset(union_test_x, union_test_ga)
training(train_data, test_data, 'union', 'ga', dump_path, args)
def main(args):
print("+++ main")
# Prepare experiment
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Create dataset
dataset = Dataset(args.batch_size, args.nopad, device)
train_iterator, valid_iterator, test_iterator = dataset.create_iterator()
args.input_size = len(dataset.source_field.vocab)
args.output_size = len(dataset.target_field.vocab)
print(f"Source vocab size = {len(dataset.source_field.vocab)}")
print(f"Target vocab size = {len(dataset.target_field.vocab)}")
args.sos_idx = dataset.target_field.vocab.stoi[args.sos]
args.eos_idx = dataset.target_field.vocab.stoi[args.eos]
args.pad_idx = dataset.target_field.vocab.stoi[args.pad]
# Create model
model = create_model(args.model_type, args, device).to(device)
init_function = create_init_function(args.init_type)
model.apply(init_function)
total_params = model.count_parameters()
print(f"Total number of parameters = {total_params}")
# Prepare training
optimizer = create_optimizer(args.optim_type, args.learning_rate,
model.parameters(), args)
criterion = nn.CrossEntropyLoss(ignore_index=args.pad_idx)
# Training
training(model, train_iterator, valid_iterator, optimizer, args.optim_type,
criterion, args.num_epochs, args.clip, args.nopad, device)
print("--- main")
def upload():
if request.method == 'POST' and 'photo' in request.files:
filename = photos.save(request.files['photo'])
print("file has been saved")
pickle_in = open("uploads/training_data.pickle", "rb")
data = pickle.load(pickle_in)
print("Daya take")
i = 0
for image, imgClass in data:
if not os.path.exists("dataset/" + imgClass):
os.makedirs("dataset/" + imgClass)
ap(imgClass)
path = "dataset/" + str(imgClass) + "/" + str(i) + ".jpg"
print(path)
cv2.imwrite(path, image)
i = i + 1
training()
cv2.destroyAllWindows()
return render_template('upload.html')
def main():
trainGenerator, valGenerator, testGenerator = create_generators()
if args.phase == 'train':
model, history = training(trainGenerator, valGenerator)
elif args.phase == 'test':
file_path = "./checkpoint"
model = tf.keras.models.load_model(file_path)
testing(testGenerator, model)
else:
print("/!\ Unknown phase : type 'train' or 'test'")
exit(0)
def filter_retrain(net, dirname):
checkpoint_path = os.path.join(CHECK_POINT_PATH, args.net)
time = str(datetime.date.today())
most_recent_path = os.path.join(checkpoint_path, 'retrain', "most_recnet",
dirname)
if not os.path.exists(most_recent_path):
os.makedirs(most_recent_path)
retrain_checkpoint_path = os.path.join(checkpoint_path, 'retrain', time,
dirname)
if not os.path.exists(retrain_checkpoint_path):
os.makedirs(retrain_checkpoint_path)
train_loader = get_train_loader(args)
test_loader = get_test_loader(args)
net = training(net, 20, train_loader, test_loader, True, 0.001, 'SGD',
retrain_checkpoint_path, most_recent_path)
return net
def run_experiment(epochs, model_name, training_type, configs):
""" Runs the basic experiment"""
print(epochs, "CONFIGS: ", configs)
# set seed for reproducibility.
seed = configs.seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
# gpu training specific seed settings.
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# load data
loaders = load_dataset(configs)
# load model
model = load_model(model_name, training_type, configs)
# loss
criterion = nn.CrossEntropyLoss().cuda()
# optimizer
# optimizer = optim.SGD(model.parameters(), configs.lr,
# momentum=configs.momentum,
# weight_decay=configs.weight_decay)
optimizer = optim.Adam(model.parameters(), configs.lr)
# get tracking dictionaries
model_weights, layer_dict = setup_delta_tracking(model)
# train model
rmae_delta_dict, train_acc_arr, test_acc_arr = training(
epochs, loaders, model, optimizer, criterion, model_weights,
layer_dict, configs)
return rmae_delta_dict, train_acc_arr, test_acc_arr
reviews = createParts(reviews_file.read().splitlines())
accuracy = 0
for i in range(10):
actual_values = []
test_set = []
for review in reviews[i]:
test_set.append(review[1:])
actual_values.append(review[0])
training_set = []
for j in range(10):
if i != j:
training_set.extend(reviews[j])
P1, P2, pp, np = training(training_set, vocabulary)
# print P1
# print P2
# print pp
# print np
# input('training')
result_values = testing(test_set, vocabulary, P1, P2, pp, np)
# print result_values
# input('result values')
current_accuracy = getAccuracy(actual_values, result_values)
print current_accuracy
# devide to train90% and vaild10% on labeled training set
X_train, X_val, y_train, y_val = train_x[:190000], train_x[
190000:], y[:190000], y[190000:]
# devide to train90% and vaild10% on both labeled and predicted labeled training set
#X_train, X_val, y_train, y_val = train_x[20000:], train_x[:20000], y[20000:], y[:20000]
# devide to train90% and vaild10% on both labeled and predicted labeled training set
#X_train, y_train = train_x[:20000], y[:20000]
#X_val, y_val = train_x[190000:200000], y[190000:200000]
#X_train = torch.cat((X_train, train_x[200000:]), 0)
#y_train = torch.cat((y_train, y[200000:]), 0)
# to dataset
train_dataset = TwitterDataset(X=X_train, y=y_train)
val_dataset = TwitterDataset(X=X_val, y=y_val)
# to dataloader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=8)
val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=8)
# training
training(batch_size, epoch, lr, model_dir, train_loader, val_loader, model,
device)
from __future__ import division from train import training from test import testing filtbankN = 13 codebooks = training(filtbankN) testing(codebooks, filtbankN)
if args.low_ratio == 0:
print('\nTraining High Resolution images')
print('\t on ',args.dataset.upper(),' dataset, with hyper parameters above')
print('\nTraining starts')
logger = getlogger(args.log_dir + '/DATASET_{}_HIGH_RES'.format(args.dataset))
for arg in vars(args):
logger.debug('{} - {}'.format(str(arg), str(getattr(args, arg))))
logger.debug('\nTraining High Resolution images')
logger.debug('\t on '+args.dataset.upper()+' dataset, with hyper parameters above\n\n')
training(net, optimizer,
args.lr, args.lr_decay, args.epochs, args.ten_batch_eval,
train_loader, eval_train_loader, eval_validation_loader, num_training, num_validation,
args.low_ratio, args.result,
logger,
args.vgg_gap,
args.save,
args.writer
)
else:
print('\nTraining Low Resolution images')
print('\t on ',args.dataset,' with hyper parameters above')
print('\tLow resolution scaling = {} x {}'.format(args.low_ratio, args.low_ratio))
print('\nTraining starts')
logger = getlogger(args.log_dir + '/DATASET_{}_LOW_{}'.format(args.dataset, str(args.low_ratio)))
for arg in vars(args):
logger.debug('{} - {}'.format(str(arg), str(getattr(args, arg))))
logger.debug('\nTraining Low Resolution images, Low resolution of {}x{}'.format(str(args.low_ratio), str(args.low_ratio)))
logger.debug('\t on '+args.dataset.upper()+' dataset, with hyper parameters above\n\n')
from train import training
import pickle
reviewed_file = open('new_data.txt', 'r')
vocabulary_file = open('vocabulary.txt', 'r')
model_file = open('model', 'wb')
reviews = reviewed_file.read().splitlines()
vocabulary = vocabulary_file.read().splitlines()
(P1, P2, pp, np) = training(reviews, vocabulary)
a = (P1, P2, vocabulary, pp, np)
pickle.dump(a, model_file)
model_file.close()
def run(args, env, agent, ram, env_params):
if(args.mode == 'train'):
training(args, env, agent, ram, env_params)
else:
evaluating(args, env, agent, args.max_episodes - 1)
from __future__ import division
import numpy as np
from scipy.io.wavfile import read
from LBG import EUDistance
from MFCC_algorithm import mfcc
from train import training
import os
#total number of speakers and filters required
totalspeakers = 4
nfilters = 16
#assigning the location of testing data
directory = os.getcwd() + '/test'
fname = str()
codebooks = training(nfilters)
#counter to count the number of speakers correctly identified
nCorrect_MFCC = 0
#calculating the minimum distance between neighbours
def minDistance(features, codebooks):
speaker = 0
distmin = np.inf
for k in range(np.shape(codebooks)[0]):
D = EUDistance(features, codebooks[k, :, :])
dist = np.sum(np.min(D, axis=1)) / (np.shape(D)[0])
if dist < distmin:
distmin = dist
speaker = k
args.start_model)[0] + '_rngstate.pth'
if os.path.exists(start_model_rngstate_path):
torch.set_rng_state(
torch.load(start_model_rngstate_path)['torch_rng_state'])
torch.cuda.set_rng_state_all(
torch.load(start_model_rngstate_path)['cuda_rng_state'])
logging('-' * 30, f_log=f_log)
logging('Loading saved rng states.', f_log=f_log)
logging('-' * 30, f_log=f_log)
train_ppl, val_ppl = training(
train_iter,
val_iter,
args.epochs,
LMModel,
optimizer,
scheduler,
args.gradclip,
args.save,
## shard_size=args.shardsz,
LMModel_parallel=LMModel_parallel,
f_log=f_log)
## subvocab_size=args.subvocabsz)
######### test the trained model
##test_ppl = validating(test_iter, LMModel, shard_size=args.shardsz, LMModel_parallel=LMModel_parallel, f_log=f_log)
test_ppl = validating(test_iter,
LMModel,
LMModel_parallel=LMModel_parallel,
f_log=f_log)
logging('-' * 30, f_log=f_log)
logging('Test ppl: %f' % test_ppl, f_log=f_log)
bivocab = file2.read().splitlines()
accuracy = 0
for i in range(10):
actual_values = []
test_set = []
for review in reviews[i]:
test_set.append(review[1:])
actual_values.append(review[0])
training_set = []
for j in range(10):
if i != j:
training_set.extend(reviews[j])
P1, P2, upp, unp, bpp, bnp = training(training_set, univocab, bivocab)
# print P1
# print P2
# print pp
# print np
# input data('training')
result_values = testing(test_set, univocab, bivocab, P1, P2, upp, unp, bpp, bnp)
# print result_values
# input data('result values')
current_accuracy = getAccuracy(actual_values, result_values)
print current_accuracy
def run_all(RESOLUTION):
convert(RESOLUTION)
model = training(RESOLUTION)
predictions(RESOLUTION)
while True:
print("1) Resize and convert images to array\n2) Train Model\n"
"3) Load Model\n4) Test Model\n5) Run all\n6) Quit\n")
choice = int(input("Choice: "))
choice_dict = {
1: convert,
2: training,
3: load_model,
4: predictions,
5: run_all,
6: exit
}
if choice == 2:
model = training(RESOLUTION)
elif choice == 3:
name = input("Enter model name: ")
print("Loading model..." + name)
try:
model = load_model(name,
custom_objects={
'smooth_l1_loss': smooth_l1_loss,
'my_metric': my_metric
})
except:
print("\nError in loading model\n")
else:
choice_dict[choice](RESOLUTION)
if __name__ == "__main__":
# 参数配置
config = configparser.ConfigParser()
config.read('config/config.ini')
# 读取参数
training_path = config['BASE']['training_path']
validation_path = config['BASE']['validation_path']
input_size = int(config['TRAINING']['input_size'])
batch_size = int(config['TRAINING']['batch_size'])
max_epoch = int(config['TRAINING']['max_epoch'])
learning_rate = float(config['TRAINING']['learning_rate'])
# 读取图片数据，并存放到numpy array中
training_x, training_y = common_util.read_data(training_path, True, input_size)
validation_x, validation_y = common_util.read_data(validation_path, True, input_size)
# 加载图片数据
training_loader = ImageDataSet(training_x, training_y, TRAINING_TRANSFORM).get_loader(batch_size)
validation_loader = ImageDataSet(validation_x, validation_y, TESTING_TRANSFORM).get_loader(batch_size, False)
# 训练模型
model = CNN128().cuda()
loss = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
train = train.Train(model, loss, optimizer)
train.training(training_loader, validation_loader, max_epoch)
train.plot()
pass
neg_dict = get_matrices.get_entries(neg_entries, -1)
pos_dict = get_matrices.get_entries(pos_entries, 1)
product_feature_matrix = get_matrices.get_product_feature_matrix(product_dict, product_index, lookup_dict, aspect_index, 5,
neg_dict, pos_dict)
product_feature_matrix2 = get_matrices.get_product_feature_matrix(product_dict2, product_index2, lookup_dict, aspect_index, 5,
neg_dict, pos_dict)
#product_feature_matrix3 = get_matrices.get_product_feature_matrix(product_dict3, product_index3, lookup_dict, aspect_index, 5,
#neg_dict, pos_dict)
#product_feature_matrix4 = get_matrices.get_product_feature_matrix(product_dict4, product_index4, lookup_dict, aspect_index, 5,
#neg_dict, pos_dict)
#u_id = user_index["A10UHQH1YL5Q6B"]
#p_id = product_index["B00HH7MAUW"]
#p_id = product_index["B00BGG5LO2"]
#user_item_matrix[u_id, p_id] = 0
[U1, U2, V, H1, H2] = train.training(user_item_matrix, user_feature_matrix, product_feature_matrix, 50, 50, 0.01, 0.01, 0.01, 0.01, 0.01, 5000, 0.002)
#[U1x, U2x, Vx, H1x, H2x] = train.training(user_item_matrix2, user_feature_matrix2, product_feature_matrix2, 50, 50, 0.01, 0.01, 0.01, 0.01, 0.01, 5000, 0.002)
#[U1y, U2y, Vy, H1y, H2y] = train.training(user_item_matrix3, user_feature_matrix3, product_feature_matrix3, 50, 50, 0.01, 0.01, 0.01, 0.01, 0.01, 5000, 0.002)
#[U1z, U2z, Vz, H1z, H2z] = train.training(user_item_matrix4, user_feature_matrix4, product_feature_matrix4, 50, 50, 0.01, 0.01, 0.01, 0.01, 0.01, 5000, 0.002)
X_ = U1.dot(V.T)
Y_ = U2.dot(V.T)
A_ = U1.dot(U2.T) + H1.dot(H2.T)
'''
X_x = U1x.dot(Vx.T)
Y_x = U2x.dot(Vx.T)
A_x = U1x.dot(U2x.T) + H1x.dot(H2x.T)
X_y = U1y.dot(Vy.T)
Y_y = U2y.dot(Vy.T)
def reading():
'''
Reading the dataset
'''
df = pd.read_csv('database.csv',
usecols=[
*range(0, 10000), *range(400000, 410000),
*range(790000, 800003)
])
print(df.head())
return df
if __name__ == "__main__":
print("Carregando os dados...")
df = reading()
print("\nAnálise da estrutura dos dados: ")
analysis(df)
print("\nTransformações necessárias no dataset: ")
df_train, df_test = transform(df)
print("\nTreinando o modelo...: ")
prediction, y_test = training(df_train, df_test)
print("\nAnálise de resultados: ")
results(y_test, prediction)
import torch
import argparse
import data_proc
import train
parser = argparse.ArgumentParser()
parser.add_argument('--cuda', type=str, default='cuda:6')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--num_epoch', type=int, default=50)
parser.add_argument('--lr', type=float, default=0.0004)
parser.add_argument('--weight_decay', type=float, default=0)
parser.add_argument('--ckp', type=str, default='ckp/model_3.pt')
parser.add_argument('--max_acc', type=float, default=0.5)
args = parser.parse_args()
if torch.cuda.is_available():
print("using cuda......")
device = torch.device(args.cuda)
w2v_model, train_iter, dev_iter, test_iter = data_proc.data(
device, args.batch_size)
train.training(device, w2v_model, train_iter, dev_iter, test_iter,
args.batch_size, args.num_epoch, args.lr, args.weight_decay,
args.ckp, args.max_acc)
y = preprocess.labels_to_tensor(y)
# 定义model
model = LSTM_Net(embedding,
embedding_dim=250,
hidden_dim=150,
num_layers=1,
dropout=0.5,
fix_embedding=fix_embedding)
model = model.to(device)
# 将一部分训练集当做验证集
X_train, X_val, y_train, y_val = train_x[:180000], train_x[
180000:], y[:180000], y[180000:]
# 加载数据
train_dataset = TwitterDataset(X=X_train, y=y_train)
val_dataset = TwitterDataset(X=X_val, y=y_val)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=8)
val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=8)
# 开始训练
training(batch_size, epoch, lr, './data', train_loader, val_loader, model,
device)
print('vocab_size is :', len(vocab))
train_corpus_ids = train_corpus.tokenize(
vocab) # contains all tokenized corpus
train_set = CorpusDataset(train_corpus_ids)
train_dl = DataLoader(train_set,
batch_size=config.batch_size,
shuffle=True,
collate_fn=collate_fn)
# todo load test dataset
# ---- model&optim&etc... ---- #
model = GatedCNN(vocab_size=len(vocab),
embed_dim=config.embed_dim,
kernel_width=config.kernel_width,
out_channel=config.out_channel,
n_layers=config.n_layers,
res_block_cnt=config.res_block_cnt,
dropout=config.dropout)
if config.use_cuda:
model = model.cuda()
optimizer = torch.optim.Adam(model.parameters())
criterion = torch.nn.CrossEntropyLoss(
ignore_index=0) # very important to set ignore_index to 0
# train
print('start time is', get_time_dif(start_time))
loss = training(model, optimizer, criterion, train_dl)
print('loss for this epoch is', loss)
print('time used for this epoch', get_time_dif(start_time))
teacher_net1 = VGG16().cuda()
teacher_net1.load_state_dict(torch.load('model/vgg16.model'))
teacher_net2 = VGG13().cuda()
teacher_net2.load_state_dict(torch.load('model/vgg13.model'))
# teacher_net3 = VGG19().cuda()
# teacher_net3.load_state_dict(torch.load('teacher_model/vgg19.model'))
teacher_net.append(teacher_net1)
teacher_net.append(teacher_net2)
# teacher_net.append(teacher_net3)
student_net = StudentNet(base=16).cuda()
#student_net = MobileNetV2(n_class=11).cuda()
print('Start Training')
training(teacher_net,
student_net,
train_loader,
val_loader,
test_loader,
total_epoch=400)
models = []
# Deep Mutual Learning
# model1 = VGG16().cuda()
# smodel1.load_state_dict(torch.load('teacher_model/vgg16.model'))
# model2 = VGG13().cuda()
# model2.load_state_dict(torch.load('teacher_model/vgg13.model'))
# model3 = StudentNet(base=16).cuda()
# model3.load_state_dict(torch.load('student_model.bin'))
# # models.append(model1)
# models.append(model2)
# models.append(model3)
from __future__ import division
import numpy as np
from scipy.io.wavfile import read
from LBG import EUDistance
from mel_coefficients import mfcc
from LPC import lpc
from train import training
import os
nSpeaker = 8
nfiltbank = 12
orderLPC = 15
(codebooks_mfcc, codebooks_lpc) = training(nfiltbank, orderLPC)
directory = os.getcwd() + '/test'
fname = str()
nCorrect_MFCC = 0
nCorrect_LPC = 0
def minDistance(features, codebooks):
speaker = 0
distmin = np.inf
for k in range(np.shape(codebooks)[0]):
D = EUDistance(features, codebooks[k, :, :])
dist = np.sum(np.min(D, axis=1)) / (np.shape(D)[0])
if dist < distmin:
distmin = dist
speaker = k
return speaker
args.n_mode - 1, p_path + '/train')
tr_batch = tr_bc.db_load()
val_path = glob(p_path + '/validation/**')
val_batch = []
for path in val_path:
val_bc = Create_Batch(args.batch_size, int(args.patch_size / 2),
args.n_mode - 1, path)
val_batch.append(val_bc.db_load())
# Training & Validation
cnt = 1
for ep in range(args.n_epoch):
# Training
models, cnt = training(args, tr_batch, models, loss_func, optimizer,
cnt, model_path)
# Training
validation(args, tr_batch, models, ep)
# Validation
for b in val_batch:
validation(args, b, models, ep)
else:
# Real MR data test (Segmentation)
if args.data_name == 'YS':
test_bc = Create_Batch(args.batch_size, int(args.patch_size / 2),
args.n_mode - 1, p_path + '/test_ys/0')
test_batch = test_bc.db_load()
