回答此问题可获得 20 贡献值,回答如果被采纳可获得 50 分。
<p>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</p>
<p>我不明白如何定义自己的成本函数。在</p>
<p>输出层只有3个神经元<code>[0, 1, 2]</code>,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</p>
<p>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</p>
<pre><code># optimization method:
from lasagne.updates import sgd
update=sgd,
update_learning_rate=0.0001
</code></pre>
<p>我的代码更新是怎么用的?在</p>
<p><strong>编辑:</strong>
完整的网络代码是:</p>
^{pr2}$
<p><strong>编辑</strong>
使用^{<cd2>时出错</p>
<pre><code>Got 99960 testing <a href="https://www.cnpython.com/pypi/dataset" class="inner-link">dataset</a>s.
# 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.
</code></pre>