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Copy pathTrain_seperate_pNN_semi.py
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Train_seperate_pNN_semi.py
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#!/usr/bin/env python
#SBATCH --job-name=SeperateSemi
#SBATCH --error=%x.%j.err
#SBATCH --output=%x.%j.out
#SBATCH --mail-type=END,FAIL
#SBATCH [email protected]
#SBATCH --export=ALL
#SBATCH --time=48:00:00
#SBATCH --partition=sdil
#SBATCH --gres=gpu:1
import importlib
import torch
import pickle
import os
import sys
sys.path.append('/pfs/data5/home/kit/tm/px3192/Split_Manufacturing_One_Mask/')
import matplotlib.pyplot as plt
import numpy as np
import pNN_Split as pnn
import random
import config
import evaluation as E
import training
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
# create file
topology_name = ''
for t in config.semi_topology:
topology_name += str(t)
whole_file_path = f'./result/seperate pNN semi {topology_name}'
if not os.path.exists(whole_file_path):
os.mkdir(whole_file_path)
if not os.path.exists(whole_file_path + '/model'):
os.mkdir(whole_file_path + '/model')
if not os.path.exists(whole_file_path + '/results'):
os.mkdir(whole_file_path + '/results')
# Device
device = 'cpu'
# Prepare data
datasets = os.listdir('./Datasets/datasets/')
datasets = [d for d in datasets if d.endswith('.p')]
datasets.sort()
results = open(f'{whole_file_path}/summary.txt', 'w')
results.close()
results = open(f'{whole_file_path}/acc_factor.txt', 'w')
results.close()
results = open(f'{whole_file_path}/train_factor.txt', 'w')
results.close()
results = open(f'{whole_file_path}/valid_factor.txt', 'w')
results.close()
for dataset in datasets:
train_loss_cross_seed = []
valid_loss_cross_seed = []
test_acc_cross_seed = []
test_maa_cross_seed = []
for seed in range(10):
# Load data
datapath = os.path.join('./Datasets/datasets/' + dataset)
with open(datapath, 'rb') as f:
data = pickle.load(f)
X_train = data['X_train']
y_train = data['y_train']
X_valid = data['X_valid']
y_valid = data['y_valid']
X_test = data['X_test']
y_test = data['y_test']
data_name = data['name']
N_class = data['n_class']
N_feature = data['n_feature']
N_train = X_train.shape[0]
N_valid = X_valid.shape[0]
N_test = X_test.shape[0]
print(f'\n\nProcessing dataset: {data_name}\nN_train: {N_train}, N_valid: {N_valid}, N_test: {N_test}\nN_feature: {N_feature}, N_class: {N_class}\nseed: {seed}')
# set seed
random.seed(seed);
np.random.seed(seed);
torch.manual_seed(seed);
# define pNN
pNN = torch.nn.Sequential()
pNN.add_module(f'input pLayer', pnn.pLayer(N_feature, config.semi_topology[0]))
for l in range(len(config.semi_topology)-1):
pNN.add_module(f'pLayer {l}', pnn.pLayer(config.semi_topology[l], config.semi_topology[l+1]))
# define optimizer
optimizer = torch.optim.Adam(pNN.parameters(), lr=config.lr)
pNN, train_losses, valid_losses, _, _ = training.train_nn(pNN,
DataLoader(TensorDataset(X_train,y_train), batch_size=N_train),
DataLoader(TensorDataset(X_valid,y_valid), batch_size=N_valid),
optimizer, pnn.LossFunction, device, UUID='seperatepNNsemi')
torch.save(pNN, f'{whole_file_path}/model/{data_name}_{seed}')
test_acc_cross_seed.append(E.BASIC(pNN, X_test, y_test))
test_maa_cross_seed.append(E.MAA(pNN, X_test, y_test))
valid_loss_cross_seed.append(min(valid_losses))
train_loss_cross_seed.append(min(train_losses))
np.savetxt(f'{whole_file_path}/results/{data_name}_test_acc.txt',
np.array(test_acc_cross_seed).reshape(-1,1), fmt='%.5f')
np.savetxt(f'{whole_file_path}/results/{data_name}_test_maa.txt',
np.array(test_maa_cross_seed).reshape(-1,1), fmt='%.5f')
np.savetxt(f'{whole_file_path}/results/{data_name}_valid_loss.txt',
np.array(valid_loss_cross_seed).reshape(-1,1), fmt='%.5f')
np.savetxt(f'{whole_file_path}/results/{data_name}_train_loss.txt',
np.array(train_loss_cross_seed).reshape(-1,1), fmt='%.5f')
test_acc_mean = np.mean(test_acc_cross_seed)
test_acc_std = np.std(test_acc_cross_seed)
test_maa_mean = np.mean(test_maa_cross_seed)
test_maa_std = np.std(test_maa_cross_seed)
valid_loss_mean = np.mean(valid_loss_cross_seed)
valid_loss_std = np.std(valid_loss_cross_seed)
train_loss_mean = np.mean(train_loss_cross_seed)
train_loss_std = np.std(train_loss_cross_seed)
results = open(f'{whole_file_path}/summary.txt', 'a')
aligned_name = data_name.ljust(25,' ')
results.write(f'{aligned_name}:\t\ttest acc: {test_acc_mean:.4f} ± {test_acc_std:.4f}\t\ttest maa: {test_maa_mean:.4f} ± {test_maa_std:.4f}\t\ttrain loss: {train_loss_mean:.4f} ± {train_loss_std:.4f}\t\tvalid loss: {valid_loss_mean:.4f} ± {valid_loss_std:.4f}\n')
results.close()
results = open(f'{whole_file_path}/acc_factor.txt', 'a')
results.write(f'{test_acc_mean:.4f}\n')
results.close()
results = open(f'{whole_file_path}/train_factor.txt', 'a')
results.write(f'{train_loss_mean:.4f}\n')
results.close()
results = open(f'{whole_file_path}/valid_factor.txt', 'a')
results.write(f'{valid_loss_mean:.4f}\n')
results.close()