autoencoder.py 文件源码

def train_ae(self, train_X, optimizer, epochs, verbose=True):
        N = train_X.data.size()[0]
        num_batches = N / self.batch_size
        for e in range(epochs):
            agg_cost = 0.
            for k in range(num_batches):
                start, end = k * self.batch_size, (k + 1) * self.batch_size
                bX = train_X[start:end]
                optimizer.zero_grad()
                Z = self.forward(bX)
                Z = self.decode(Z)
                loss = -torch.sum(bX * torch.log(Z) + (1.0 - bX) * torch.log(1.0 - Z), 1)
                cost = torch.mean(loss)
                cost.backward()
                optimizer.step()
                agg_cost += cost
            agg_cost /= num_batches
            if verbose:
                print("Epoch:", e, "cost:", agg_cost.data[0])