如何定义一个成本函数在诺莱恩，宽面条？

Question

我在nolearn做一个神经网络，这是一个基于Theano的图书馆，使用宽面条。在

我不明白如何定义自己的成本函数。在

输出层只有3个神经元[0, 1, 2]，我希望它在给出1或2时基本确定，否则——如果它不能确定1、2——只返回0。在

因此，我提出了一个成本函数（需要调整），其中1和2的成本是0的两倍，但我不知道如何告诉网络。在

# optimization method:
from lasagne.updates import sgd
update=sgd,
update_learning_rate=0.0001

我的代码更新是怎么用的？在

编辑： 完整的网络代码是：

^{pr2}$

编辑 使用^{时出错

Got 99960 testing datasets.
# Neural Network with 18403 learnable parameters

## Layer information

  #  name       size
---  -------  ------
  0  input       180
  1  hidden1     100
  2  output        3

Traceback (most recent call last):
  File "/Users/morgado/anaconda/lib/python3.4/site-packages/theano/compile/function_module.py", line 607, in __call__
    outputs = self.fn()
ValueError: GpuElemwise. Input dimension mis-match. Input 1 (indices start at 0) has shape[1] == 1, but the output's size on that axis is 3.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "train_nolearn_simple.py", line 272, in <module>
    main(**kwargs)
  File "train_nolearn_simple.py", line 239, in main
    nn_loss_fit = nn_loss(data, x_period, columns, num_epochs, batchsize)
  File "train_nolearn_simple.py", line 217, in nn_loss
    net1.fit(data['X_train'], data['y_train'])
  File "/Users/morgado/anaconda/lib/python3.4/site-packages/nolearn/lasagne/base.py", line 416, in fit
    self.train_loop(X, y)
  File "/Users/morgado/anaconda/lib/python3.4/site-packages/nolearn/lasagne/base.py", line 462, in train_loop
    self.train_iter_, Xb, yb)
  File "/Users/morgado/anaconda/lib/python3.4/site-packages/nolearn/lasagne/base.py", line 516, in apply_batch_func
    return func(Xb) if yb is None else func(Xb, yb)
  File "/Users/morgado/anaconda/lib/python3.4/site-packages/theano/compile/function_module.py", line 618, in __call__
    storage_map=self.fn.storage_map)
  File "/Users/morgado/anaconda/lib/python3.4/site-packages/theano/gof/link.py", line 297, in raise_with_op
    reraise(exc_type, exc_value, exc_trace)
  File "/Users/morgado/anaconda/lib/python3.4/site-packages/six.py", line 658, in reraise
    raise value.with_traceback(tb)
  File "/Users/morgado/anaconda/lib/python3.4/site-packages/theano/compile/function_module.py", line 607, in __call__
    outputs = self.fn()
ValueError: GpuElemwise. Input dimension mis-match. Input 1 (indices start at 0) has shape[1] == 1, but the output's size on that axis is 3.
Apply node that caused the error: GpuElemwise{Sub}[(0, 1)](GpuElemwise{Composite{scalar_sigmoid((i0 + i1))}}[(0, 0)].0, GpuFromHost.0)
Toposort index: 22
Inputs types: [CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, matrix)]
Inputs shapes: [(200, 3), (200, 1)]
Inputs strides: [(3, 1), (1, 0)]
Inputs values: ['not shown', 'not shown']
Outputs clients: [[GpuCAReduce{pre=sqr,red=add}{1,1}(GpuElemwise{Sub}[(0, 1)].0), GpuElemwise{Mul}[(0, 0)](GpuElemwise{Sub}[(0, 1)].0, GpuElemwise{Composite{scalar_sigmoid((i0 + i1))}}[(0, 0)].0, GpuElemwise{sub,no_inplace}.0), GpuElemwise{mul,no_inplace}(CudaNdarrayConstant{[[ 2.]]}, GpuElemwise{Composite{(inv(i0) / i1)},no_inplace}.0, GpuElemwise{Sub}[(0, 1)].0, GpuElemwise{Composite{scalar_sigmoid((i0 + i1))}}[(0, 0)].0, GpuElemwise{sub,no_inplace}.0)]]

HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

Answer 1

请参见以下示例（取自here），该示例指定了它自己的损失函数：

import lasagne
import theano.tensor as T
import theano
from lasagne.nonlinearities import softmax
from lasagne.layers import InputLayer, DenseLayer, get_output
from lasagne.updates import sgd, apply_momentum
l_in = InputLayer((100, 20))
l1 = DenseLayer(l_in, num_units=3, nonlinearity=softmax)
x = T.matrix('x')  # shp: num_batch x num_features
y = T.ivector('y') # shp: num_batch
l_out = get_output(l1, x)
params = lasagne.layers.get_all_params(l1)
loss = T.mean(T.nnet.categorical_crossentropy(l_out, y))
updates_sgd = sgd(loss, params, learning_rate=0.0001)
updates = apply_momentum(updates_sgd, params, momentum=0.9)
train_function = theano.function([x, y], updates=updates)

巧合的是，这个代码在输出层也有三个单元。在