我正在尝试训练一个简单的RNN模型，其目标非常简单，即无论输入如何，输出都应匹配一个固定的向量。

import torchimport torch.nn as nnfrom torch.autograd import Variableimport numpy as npclass RNN(nn.Module):    def __init__(self, input_size, hidden_size, output_size):        super(RNN, self).__init__()        self.hidden_size = hidden_size        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)        print "i2h WEIGHT size ", list(self.i2h.weight.size())        print "i2h bias size ", list(self.i2h.bias.size())        self.i2o = nn.Linear(hidden_size, output_size)        print "i2o WEIGHT size ", list(self.i2o.weight.size())        print "i2o bias size ", list(self.i2o.bias.size())        self.softmax = nn.LogSoftmax(dim=1)    def forward(self, input, hidden):        combined = torch.cat((input, hidden), 1)        hidden = self.i2h(combined)        output = self.i2o(hidden)        output = self.softmax(output)        return output, hidden    def initHidden(self):        return Variable(torch.zeros(1, self.hidden_size))n_hidden = 20rnn = RNN(10, n_hidden, 3)learning_rate = 1e-3loss_fn = torch.nn.MSELoss(size_average=False)out_target = Variable( torch.FloatTensor([[0.0 , 1.0, 0.0]] ) , requires_grad=False)print "target output::: ", out_targetdef train(category_tensor, line_tensor):    hidden = rnn.initHidden()    rnn.zero_grad()    for i in range(line_tensor.size()[0]):        #print "train iteration ", i, ": input data: ", line_tensor[i]        output, hidden = rnn(line_tensor[i], hidden)    loss = loss_fn(output, out_target)    loss.backward()    # Add parameters' gradients to their values, multiplied by learning rate    for p in rnn.parameters():        #print "parameter: ", p, " gradient: ", p.grad.data        p.data.add_(-learning_rate, p.grad.data)    return output, loss.data[0]current_loss = 0n_iters = 500for iter in range(1, n_iters + 1):    inp = Variable(torch.randn(100,1,10) + 5)    output, loss = train(out_target, inp)    current_loss += loss    if iter % 1 == 0:      print "weights: ",rnn.i2h.weight      print "LOSS: ", loss      print output

如图所示，损失值一直保持在6以上，从未下降。请注意，我对所有随机输入的正态分布进行了5的偏移，因此它们大多是正数，因此应该存在一种权重分布可以接近目标输出。

在这个例子中，我在哪里做错了，导致输出未能接近目标？

回答：

你的固定输出是：

torch.FloatTensor([[0.0, 1.0, 0.0]])

但你在RNN中使用了以下作为最后一层：

self.softmax = nn.LogSoftmax(dim=1)

LogSoftmax 返回的值是在[0, 1]范围内吗？虽然你可以使用Softmax，但我建议你使用sign函数，并将-1转换为0。