def loss(self, trg_batch, train):
losses = []
for i in range(len(trg_batch)-1):
y = self.decode_step(trg_batch[i], train)
loss = F.softmax_cross_entropy(y, trg_batch[i+1], 0)
losses.append(loss)
return F.batch.mean(F.sum(losses))
def loss(self, trg_batch, train):
"""Calculates loss values."""
losses = []
for i in range(len(trg_batch) - 1):
y = self.decode_step(trg_batch[i], train)
losses.append(F.softmax_cross_entropy(y, trg_batch[i + 1], 0))
return F.batch.mean(F.sum(losses))
def loss(self, outputs, inputs):
losses = [
F.softmax_cross_entropy(outputs[i], inputs[i + 1], 0)
for i in range(len(outputs))
]
return F.batch.mean(F.sum(losses))