def main(args):
start_time = time.time()
model = utils.build_model(args)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
criterion = torch.nn.CrossEntropyLoss()
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
args.milestone,
gamma=0.1,
last_epoch=-1)
train_loader, test_loader = datasets.dataloader_builder(args)
train_loss = np.zeros(args.epochs)
test_loss, test_accu = np.zeros(args.epochs), np.zeros(args.epochs)
print('\n\r\t#### Start Training ####')
for epoch in range(args.epochs):
train_loss[epoch] = trainer(model, train_loader, optimizer, criterion)
test_loss[epoch], test_accu[epoch] = tester(model, test_loader)
scheduler.step()
# print(scheduler.get_lr()[0])
print(
'| Epoch: {0:3d} | Training Loss: {1:.6f} | Test Accuracy: {2:.2f} | Test Loss {3:.6f} |'
.format(epoch, train_loss[epoch], test_accu[epoch],
test_loss[epoch]))
print('\t#### Time Consumed: {0:.3f} second ####\n\r'.format(time.time() -
start_time))
utils.saveCheckpoint(args.cp_dir, args.model_name, epoch, model, optimizer,
test_accu, train_loss, args.bn, args.weight_decay)
utils.plotCurve(args, train_loss / args.trn_batch, test_loss, test_accu)
def train(self, x_train_lstm, x_train_svm, y_train,
x_val_lstm = None, x_val_svm = None, y_val = None,
x_train_u = None, x_val_u = None,
n_epochs = 20, class_weight = None):
logdir = os.path.join("tensorboard_log", self.detection, self.save_model_name, self.num_set)
cmd = "mkdir -p " + logdir
os.system(cmd)
cmd = "rm -r " + logdir + "/"
os.system(cmd)
tensorboard_callback = callbacks.TensorBoard(log_dir = logdir)
h5_save_path = os.path.join("Models", self.detection, self.model_name + "_" + self.num_set + ".h5")
acc = []
loss = []
val_acc = []
val_loss = []
x_train_dict = {'lstm_features': x_train_lstm, 'svm_features': x_train_svm}
x_val_dict = {'lstm_features':x_val_lstm, 'svm_features': x_val_svm}
train_set = tf.data.Dataset.from_tensor_slices((x_train_dict, y_train)).shuffle(buffer_size = Config.BUFFER_SIZE).batch(32).prefetch(tf.data.experimental.AUTOTUNE)
val_set = tf.data.Dataset.from_tensor_slices((x_val_dict, y_val)).shuffle(buffer_size = Config.BUFFER_SIZE).batch(32).prefetch(tf.data.experimental.AUTOTUNE)
model_cp = callbacks.ModelCheckpoint(filepath = h5_save_path,
monitor = "val_loss",
verbose = 0,
save_best_only = True,
save_weights_only = False,
mode = "min",
period = 10)
"""
history = self.model.fit(x = x_train_dict,
y = y_train,
shuffle = 1,
validation_data = (x_val_dict, y_val),
batch_size = Config.BATCH_SIZE,
epochs = Config.EPOCHS,
class_weight = class_weight,
callbacks = [model_cp, tensorboard_callback],
use_multiprocessing = False)
"""
history = self.model.fit(x = train_set,
validation_data = val_set,
epochs = Config.EPOCHS,
verbose = 1,
class_weight = class_weight,
callbacks = [model_cp],
use_multiprocessing = False)
# 训练集上的损失值和准确率
loss = history.history['loss']
val_loss = history.history["val_loss"]
figfile = os.path.join("Fig", self.detection, self.model_name + "_" + self.num_set + ".png")
pickle.dump(loss, open(figfile.split(".p")[0] + "_loss.cpickle", "wb"))
pickle.dump(val_loss, open(figfile.split(".p")[0] + "_val_loss.cpickle", "wb"))
plotCurve(loss, val_loss, 'Model Loss', 'loss', figfile)