Как сохранять эпохи каждые 20 раз и загружать модель снова? ⇐ Python

[Anonymous]

Я хочу обучить эту модель на Colab, и у нее 1000 эпох .. но это занимает слишком долго. < /p>
Это мой код с Python: < /p>
его обучение модели для 1000 эпох,
, и я хочу сохранять эпохи каждые 20 раз (например) и снова загружать модель и продолжить с последней эпохи. < Br /> Пример: поезда от 1 до 20, затем сохраните модель ... затем загрузите модель и продолжайте от 20 до 40 и так. < /p>
import argparse
import numpy as np
import pandas as pd
import sys, os
from random import shuffle
import torch
import torch.nn as nn
from models.gcn import GCNNet
from utils import *

# training function at each epoch
def train(model, device, train_loader, optimizer, epoch,hidden,cell):
print('Training on {} samples...'.format(len(train_loader.dataset)))
model.train()
for batch_idx, data in enumerate(train_loader):
data = data.to(device)
optimizer.zero_grad()
output = model(data,hidden,cell)
loss = loss_fn(output, data.y.view(-1, 1).float().to(device))
loss.backward()
optimizer.step()
if batch_idx % LOG_INTERVAL == 0:
print('Train epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch,
batch_idx * len(data.x),
len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.item()))

def predicting(model, device, loader,hidden,cell):
model.eval()
total_preds = torch.Tensor()
total_labels = torch.Tensor()
print('Make prediction for {} samples...'.format(len(loader.dataset)))
with torch.no_grad():
for data in loader:
data = data.to(device)
output = model(data,hidden,cell)
total_preds = torch.cat((total_preds, output.cpu()), 0)
total_labels = torch.cat((total_labels, data.y.view(-1, 1).cpu()), 0)
return total_labels.numpy().flatten(),total_preds.numpy().flatten()

loss_fn = nn.MSELoss()
LOG_INTERVAL = 20

def main(args):
dataset = args.dataset
modeling = [GCNNet]
model_st = modeling[0].__name__

cuda_name = "cuda:0"
print('cuda_name:', cuda_name)

TRAIN_BATCH_SIZE = args.batch_size
TEST_BATCH_SIZE = args.batch_size
LR = args.lr

NUM_EPOCHS = args.epoch

print('Learning rate: ', LR)
print('Epochs: ', NUM_EPOCHS)

# Main program: iterate over different datasets
print('\nrunning on ', model_st + '_' + dataset )
processed_data_file_train = 'data/processed/' + dataset + '_train.pt'
processed_data_file_test = 'data/processed/' + dataset + '_test.pt'
if ((not os.path.isfile(processed_data_file_train)) or (not os.path.isfile(processed_data_file_test))):
print('please run create_data.py to prepare data in pytorch format!')
else:
train_data = TestbedDataset(root='data', dataset=dataset+'_train')
test_data = TestbedDataset(root='data', dataset=dataset+'_test')

# make data PyTorch mini-batch processing ready
train_loader = DataLoader(train_data, batch_size=TRAIN_BATCH_SIZE, shuffle=True,drop_last=True)
test_loader = DataLoader(test_data, batch_size=TEST_BATCH_SIZE, shuffle=False,drop_last=True)

# training the model
device = torch.device(cuda_name if torch.cuda.is_available() else "cpu")
model = modeling[0](k1=1,k2=2,k3=3,embed_dim=128,num_layer=1,device=device).to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=LR)
best_mse = 1000
best_ci = 0
best_epoch = -1
#model_file_name = 'model' + model_st + '_' + dataset + '.model'
result_file_name = 'result' + model_st + '_' + dataset + '.csv'

##TRAIN _ NUM OF EPOCHES
for epoch in range(NUM_EPOCHS):
hidden,cell = model.init_hidden(batch_size=TRAIN_BATCH_SIZE)
train(model, device, train_loader, optimizer, epoch+1,hidden,cell)
G,P = predicting(model, device, test_loader,hidden,cell)
ret = [rmse(G,P),mse(G,P),pearson(G,P),spearman(G,P),ci(G,P),get_rm2(G.reshape(G.shape[0],-1),P.reshape(P.shape[0],-1))]
if ret[1]

Что я делаю для моего кода? < /p>
< /blockquote>

Подробнее здесь: https://stackoverflow.com/questions/788 ... odel-again