I implemented my own custom parameter search for LSTM, from the code I have attached below is there a better way I could initialize the model and optimizer for each set of parameters without retraining everytime? 
def lstm_random_parameter_search(parameters):
train_data, test_data = trainloader, valloader
epochs = 100
# number of inputs nodes to the reservoir
n_inputs = image_dimension**2
# number of output nodes from the reservoir
n_outputs = image_dimension**2
n_layers = 1
criterion = weighted_binary_cross_entropy
lag = 5
learning_rate = parameters['learning_rate']
hidden_size = parameters['hidden_size']
bidirectional = parameters['bidirectional']
max_iou = 0
trails = 0
# model = torch.load('final_results/rnn_best_model_100.pt').to(device)
for lr_id, lr in enumerate(learning_rate):
for hs_id, hs in enumerate(hidden_size):
save_grid_parameters(parameters, lr_id, hs_id, bi_id)
model = ImageRNN(n_inputs, n_outputs, n_hidden=hs).to(device)
# initliaze optimizer along with data loaders
optimizer=torch.optim.SGD(model.parameters(), lr = lr, momentum=0.9, weight_decay=0.001)
for epoch in np.arange(1, epochs+1):
for batch_idx, (inputs, labels, names) in enumerate(train_data):
trails +=1
# load data and move data to GPU's
inputs = inputs.to(device)
labels = labels.to(device)
# shift the time series images
inputs, labels = lag_data(inputs.float(), labels, lag=lag)
# initialize the hidden state on the GPU's
model.init_hidden(inputs, device)
# forward-propogation
outputs = model(inputs)
loss = criterion(labels.view(-1, image_dimension*image_dimension),
outputs.view(-1, image_dimension*image_dimension))
loss.backward()
optimizer.step()
optimizer.zero_grad()
# test on validation-testing data
iou, f1, psnr, val_loss = get_accuracy(model, test_data)
if iou > max_iou:
best_parameters = dict()
m = 'optimal parameters found after {} trails'.format(trails)
print(m)
best_parameters['learning_rate']=lr
best_parameters['reservoir_size']=hs
best_parameters['iou']=iou
best_parameters['f1_score']=f1
best_parameters['psnr']=psnr
best_parameters['loss']=loss.detach().item()
best_parameters['val_loss']=val_loss
torch.save(model, 'final_results/rnn_best_model_100.pt')
json.dump( best_parameters, open("final_results/rnn_best_parameters_100.json", 'w'))
max_iou = iou
# clear variables from memory
del inputs, labels, outputs
torch.cuda.empty_cache()
msg ='Epoch: {}, Training Loss: {:.3f}, Validation Loss: {:.3f}, IoU: {:.3f}, f1: {:.3f}, PSNR: {:.6f}'.format(epoch, val_loss,
loss.detach().item(), iou, f1, psnr)
print(msg)
print(best_parameters)
return best_parameters, model