epoch_loss, epoch_acc = [], []
for i in range(num_batches):
start_index = i * args.batch_size
epoch_x, epoch_y = reader.get_next_batch(start_index,
args.batch_size)
[loss, acc] = model.train_on_batch(epoch_x, epoch_y)
epoch_loss.append(loss)
epoch_acc.append(acc)
num_batches = int(reader.val_size / args.batch_size)
start_index = 0
val_loss, val_acc = [], []
for i in range(num_batches):
start_index = i * args.batch_size
epoch_x, epoch_y = reader.get_next_val_batch(
start_index, args.batch_size)
[loss, acc] = model.test_on_batch(epoch_x, epoch_y)
val_loss.append(loss)
val_acc.append(acc)
print(
'Epoch {}/{}: loss: {}, acc: {}, val_loss: {}, val_acc: {}'.format(
epoch + 1, args.epochs, np.average(epoch_loss),
np.average(epoch_acc), np.average(val_loss),
np.average(val_acc)))
model.save_weights(
os.path.join(
log_dir,
'ep{}-val_loss{}-val_acc{}.h5'.format(epoch + 1,
