helper_image_rnn.py

import torch.optim as optim

# Device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Model instance
model = ImageRNN(BATCH_SIZE, N_STEPS, N_INPUTS, N_NEURONS, N_OUTPUTS)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

def get_accuracy(logit, target, batch_size):
    ''' Obtain accuracy for training round '''
    corrects = (torch.max(logit, 1)[1].view(target.size()).data == target.data).sum()
    accuracy = 100.0 * corrects/batch_size
    return accuracy.item()