InvalidType:执行了无效的操作

本文介绍了InvalidType:执行了无效的操作的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我正在尝试编写一个堆叠式自动编码器.由于这是一个堆叠式自动编码器，我们需要训练第一个自动编码器并将权重传递给第二个自动编码器.因此，在训练过程中，我们需要定义train_data_for_next_layer.在这里我得到了错误:

I am trying to write a stacked autoencoder. Since this a stacked autoencoder we need to train the first autoencoder and pass the weights to the second autoencoder. So during training we need to define train_data_for_next_layer. Here I am getting error:

InvalidType: Invalid operation is performed in: LinearFunction (Forward) Expect: x.shape[1] == W.shape[1] Actual: 784 != 250

我对最后一行有疑问.是由于模型层不正确而引起的此问题，我想知道这里的问题是什么.我之前曾多次遇到此问题，欢迎提供任何详细说明.代码如下:

I am having issue with the last line. Is this problem due to incorrect model layer, I want to know what is the issue here. I have faced this problem several times before and any detailed explanation is welcome. The code is as follows:

class AutoEncoder(chainer.Chain): def __init__(self, n_in, n_out, activation='relu', tied=True): if tied: super(AutoEncoder, self).__init__( l1 = L.Linear(n_in, n_out) ) self.add_param('decoder_bias', n_in) self.decoder_bias.data[...] = 0 else: super(AutoEncoder, self).__init__( l1 = L.Linear(n_in, n_out), l2 = L.Linear(n_out, n_in) ) self.tied = tied self.n_in = n_in self.n_out = n_out self.activation = {'relu': F.relu, 'sigmoid': F.sigmoid, 'identity': F.identity}[activation] def __call__(self, x, train=True): h1 = F.dropout(self.activation(self.l1(x)), train=train) if self.tied: return self.activation(F.linear(h1, F.transpose(self.l1.W), self.decoder_bias)) else: return self.activation(self.l2(h1)) def encode(self, x, train=True): return F.dropout(self.activation(self.l1(x)), train=train) def decode(self, x, train=True): if self.tied: return self.activation(F.linear(x, F.transpose(self.l1.W), self.decoder_bias)) else: return self.activation(self.l2(x)) class StackedAutoEncoder(chainer.ChainList): def __init__(self, autoencoders): super(StackedAutoEncoder, self).__init__() for ae in autoencoders: self.add_link(ae) def __call__(self, x, train=True, depth=0): if depth == 0: depth = len(self) h = x for i in range(depth): h = self[i].encode(h, train=train) for i in range(depth): if i == depth-1: # do not use dropout in the output layer train = False h = self[depth-1-i].decode(h, train=train) return h def encode(self, x, train=True, depth=0): if depth == 0: depth = len(self) h = x for i in range(depth): h = self[i].encode(h, train=train) return h def decode(self, x, train=True, depth=0): if depth == 0: depth = len(self) h = x for i in range(depth): if i == depth-1: # do not use dropout in the output layer train = False h = self[depth-1-i].decode(h, train=train) return h class Regression(chainer.Chain): def __init__(self, predictor): super(Regression, self).__init__(predictor=predictor) def __call__(self, x, t): y = self.predictor(x, True) self.loss = F.mean_squared_error(y, t) return self.loss def dump(self, x): return self.predictor(x, False) initmodel = ''resume = '' gpu = -1 epoch_pre = 20 epoch_fine = 20 batchsize = 100 noise = 0 optimizer = 'adam' learningrate = 0.01 alpha = 0.001 unit = '1000, 500, 250, 2' activation = 'sigmoid' untied = False batchsize = batchsize n_epoch = epoch_pre n_epoch_fine = epoch_fine n_units = list(map(int, unit.split(','))) activation = activation mnist = fetch_mldata('MNIST original', data_home='.') perm = np.random.permutation(len(mnist.data)) mnist.data = mnist.data.astype(np.float32) / 255 train_data = mnist.data[perm][:60000] test_data = mnist.data[perm][60000:] # prepare layers aes = [] for idx in range(len(n_units)): n_in = n_units[idx-1] if idx > 0 else 28*28 n_out = n_units[idx] ae = AutoEncoder(n_in, n_out, activation, tied = True) aes.append(ae) # prepare train data for next layer x = chainer.Variable(np.array(train_data)) train_data_for_next_layer = cuda.to_cpu(ae.encode(x, train=False))

推荐答案

InvalidType错误表示赋予F.linear的数组的输入形状是错误的.

The InvalidType error indicates that the input shape of the array given to F.linear is wrong.

Expect: x.shape[1] == W.shape[1] Actual: 784 != 250

在这种情况下，对于给定的输入x和W，F.linear期望 x.shape[1]与W.shape[1]相同，但不相同.

In this case, for the given input x and W, F.linear expects that x.shape[1] is the same as W.shape[1], but it does not.

有关错误消息的更多详细说明，请参见 docs.chainer/en/stable/tips.html#how-do-i-fix-invalidtype-error 了解如何解释该错误消息.

For more detailed description of the error message, see docs.chainer/en/stable/tips.html#how-do-i-fix-invalidtype-error to understand how to interpret that error message.