def run_tests():
tests.test_rnn(RNN, train_on_gpu)
tests.test_forward_back_prop(RNN, forward_back_prop, train_on_gpu)
hidden = tuple([each.data for each in hidden])
# clear accumulated gradients
rnn.zero_grad()
# obtain the rnn's output
output, hidden = rnn(inp, hidden)
# loss calculation and backprop
loss = criterion(output, target)
loss.backward()
# clipping + optimization
nn.utils.clip_grad_norm_(rnn.parameters(), 5)
optimizer.step()
# mu_loss = average loss over the batch
# return the loss over a batch and the hidden state produced by our model
return loss.item(), hidden
tests.test_forward_back_prop(RNN, forward_back_prop, train_on_gpu)
#################################################
## Neural Network Training
#################################################
def train_rnn(rnn, batch_size, optimizer, criterion, n_epochs, show_every_n_batches=100):
batch_losses, epoch_losses, train_losses = [], [], []
rnn.train()
print("Training for %d epoch(s)..." % n_epochs)
for epoch_i in range(1, n_epochs + 1):
# initialize hidden state
hidden = rnn.init_hidden(batch_size)
def test_forward_and_backpropagation(self):
problem_unittests.test_forward_back_prop(
RNN=lost_episode.RNN,
forward_back_prop=lost_episode.forward_and_backpropagation,
train_on_gpu=self.train_on_gpu)
