千层面回归：错误

Question

我正在尝试运行使用 lasagne/nolearn 进行回归。我无法找到如何执行此操作的文档（一般来说是深度学习的新手）。

从一个简单的网络（一个隐藏层）开始

from lasagne import layers

from lasagne.nonlinearities import softmax
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet    

print(np.shape(X)) # (137, 43)
print(np.shape(y)) # (137,)

layers_s = [('input', layers.InputLayer),
           ('dense0', layers.DenseLayer),
           ('output', layers.DenseLayer)]

net_s = NeuralNet(layers=layers_s,

                 input_shape=(None, num_features),
                 dense0_num_units=43,
                 output_num_units=1,
                 output_nonlinearity=None,

                 regression=True,

                 update=nesterov_momentum,
                 update_learning_rate=0.001,
                 update_momentum=0.9,

                 eval_size=0.2,
                 verbose=1,
                 max_epochs=100)

net_s.fit(X, y)

我收到以下错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-23-23c15ceec104> in <module>()
----> 1 net_s.fit(X, y)

/home/alex/anaconda3/lib/python3.4/site-packages/nolearn/lasagne.py in fit(self, X, y)
    148             out, self.loss, self.update,
    149             self.X_tensor_type,
--> 150             self.y_tensor_type,
    151             )
    152         self.train_iter_, self.eval_iter_, self.predict_iter_ = iter_funcs

/home/alex/anaconda3/lib/python3.4/site-packages/nolearn/lasagne.py in _create_iter_funcs(self, output_layer, loss_func, update, input_type, output_type)
    298         all_params = get_all_params(output_layer)
    299         update_params = self._get_params_for('update')
--> 300         updates = update(loss_train, all_params, **update_params)
    301 
    302         train_iter = theano.function(

/home/alex/src/lasagne/lasagne/updates.py in nesterov_momentum(loss, all_params, learning_rate, momentum)
     38 # such that the gradient can be evaluated at the current parameters.
     39 def nesterov_momentum(loss, all_params, learning_rate, momentum=0.9):
---> 40     all_grads = theano.grad(loss, all_params)
     41     updates = []
     42 

/home/alex/anaconda3/lib/python3.4/site-packages/theano/gradient.py in grad(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected)
    431 
    432     if cost is not None and cost.ndim != 0:
--> 433         raise TypeError("cost must be a scalar.")
    434 
    435     if isinstance(wrt, set):

TypeError: cost must be a scalar.

谢谢！..

Answer 1

不确定您使用的是哪个版本的 nolearn 和 lasagne。我注意到你有 y 的形状 (137,)。根据我的用法，这需要 (137, 1) 才能适用于您的情况，并且通常 dim 2 需要匹配 output_num_units。

尝试y.reshape((-1, 1))。

如果这不起作用，可能是 Python 3 兼容性问题。

Answer 2

确保您使用的是 已知可以协同工作。

的 nolearn 和 Lasagne 版本

假设您一直在关注 Using convolutional neural nets to detect facial keypoints tutorial. Then the right thing to do is to install the dependencies from this requirements.txt file，例如：

pip uninstall Lasagne
pip uninstall nolearn
pip install -r https://raw.githubusercontent.com/dnouri/kfkd-tutorial/master/requirements.txt

但是，如果您使用的是 Git master 的 nolearn，请确保安装 requirements.txt file found there:

中的 Lasagne 版本

pip uninstall Lasagne
pip install -r https://raw.githubusercontent.com/dnouri/nolearn/master/requirements.txt

千层面回归：错误

Regression with lasagne: error

python

theano

deep-learning