def l_n_loss(tensor, n, batch_norm=True):
if struct.isstruct(tensor):
all_tensors = struct.flatten(tensor)
return sum(l_n_loss(tensor, n, batch_norm) for tensor in all_tensors)
total_loss = math.sum(tensor**n) / n
if batch_norm:
batch_size = math.shape(tensor)[0]
return math.div(total_loss, math.to_float(batch_size))
else:
return total_loss
def l1_loss(tensor, batch_norm=True, reduce_batches=True):
if struct.isstruct(tensor):
all_tensors = struct.flatten(tensor)
return sum(l1_loss(tensor, batch_norm, reduce_batches) for tensor in all_tensors)
if reduce_batches:
total_loss = math.sum(math.abs(tensor))
else:
total_loss = math.sum(math.abs(tensor), axis=list(range(1, len(tensor.shape))))
if batch_norm and reduce_batches:
batch_size = math.shape(tensor)[0]
return math.div(total_loss, math.to_float(batch_size))
else:
return total_loss