def train_main(part, train_loader, train_loader_check, test_loader, args):
"""
Conducts main training procedure for particular part of k-fold splitting.
*Iterates over the set of hyperparameters.
*After training "scans" all the thresholds, generating the dimension values [d1,d2,d3,d4,d5] of particles.
*Calls an object of DataStore class to save the results and the best network states.
"""
device = args.device
np.random.seed(args.seed)
torch.manual_seed(args.seed)
records = pd.DataFrame(columns=['Part_Num','Learning_rate', 'Layer_size', 'best_val(MSE)',\
'last_val(MARE)', 'stopping epoch', 'counter',\
'positive num', 'all'])
# sets of learning rates and numbers of nodes in layers.
lr_arr = np.array([0.0001, 0.001, 0.005, 0.01, 0.05, 0.1]).reshape(-1, 1)
N_size = np.array([100, 200, 300, 400, 500, 600]).reshape(-1, 1)
for i, LR in enumerate(lr_arr):
for j, LS in enumerate(N_size):
Learning_rate = LR[0]
Layer_size = LS[0]
# Data storing object
ds = DataStore(part, Learning_rate, Layer_size)
# Initialize
loss_main = nn.MSELoss()
loss_secondaty = MARE
model, optimizer, train_step, counter = params_init(
Layer_size, Learning_rate, loss_main) #parameters
complexity = model_complexity(model)
print("%-10s\n %-15s %-4.6f \n %-15s %-4.2f\n %10s "%("////////////////////////",\
"Learning Rate:", Learning_rate,\
"Layer Size:", Layer_size,\
"////////////////////////"))
# #Train #number of trainable parameters
bestmodel, best_val_error_main, best_val_error_secondary, counter, epoch = train_procedure(model, \
train_step,loss_main,
loss_secondaty, \
train_loader, train_loader_check, \
test_loader,\
args)#training
#save the model
ds.net_saver(bestmodel)
# "Scanning" over the constant(threshold) scattering power values.
vals_arr, threshold_arr = decrease(bestmodel)
val_temp, threshold_temp = extract_positive(
vals_arr, threshold_arr)
# save thresholds and dimensions' values of a "scan" in the form: [d1, d2, d3, d4, d5]
ds.dimensions_saver(val_temp, threshold_temp)
new_row = {'Part_Num': part,
'Learning_rate': Learning_rate, \
'Layer_size': Layer_size,\
'best_val(MSE)': best_val_error_main,\
'last_val(MARE)': best_val_error_secondary,
'stopping epoch': epoch, \
'counter': counter,\
'positive num':val_temp.shape[0] ,\
'all': vals_arr.shape[0]}
records = records.append(new_row, ignore_index=True)
ds.records_saver(records)
