1
我正在接近神經網絡實現，試圖建立一個使用Theano工作MLP。在教程之後，我試圖通過添加一個圖層來增強網絡，總共兩個隱藏層，每個隱藏層具有相同數量的單元（250）。問題是，當我運行腳本時遇到「形狀不匹配」ValueError。我的代碼是教程代碼的修改版本，可以在這裏找到http://deeplearning.net/tutorial/mlp.html。Theano MLP與2隱藏層引發形狀不匹配錯誤
我修改的部分是片段-2，即MLP對象，如下所示：
class MLP(object):
def __init__(self, rng, input, n_in, n_hidden, n_out):
"""Initialize the parameters for the multilayer perceptron
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_hidden: int
:param n_hidden: number of hidden units
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
self.hiddenLayer1 = HiddenLayer(
rng=rng,
input=input,
n_in=n_in,
n_out=n_hidden,
activation=T.tanh
)
#try second hidden layer
self.hiddenLayer2 = HiddenLayer(
rng=rng,
input=self.hiddenLayer1.output,
n_in=n_in,
n_out=n_hidden,
activation=T.tanh
)
# The logistic regression layer gets as input the hidden units
# of the hidden layer
self.logRegressionLayer = LogisticRegression(
input=self.hiddenLayer2.output,
n_in=n_hidden,
n_out=n_out
)
# end-snippet-2 start-snippet-3
# L1 norm ; one regularization option is to enforce L1 norm to
# be small
self.L1 = (
abs(self.hiddenLayer1.W).sum()
+ abs(self.hiddenLayer2.W).sum()
+ abs(self.logRegressionLayer.W).sum()
)
# square of L2 norm ; one regularization option is to enforce
# square of L2 norm to be small
self.L2_sqr = (
(self.hiddenLayer1.W ** 2).sum()
+ (self.hiddenLayer2.W ** 2).sum()
+ (self.logRegressionLayer.W ** 2).sum()
)
# negative log likelihood of the MLP is given by the negative
# log likelihood of the output of the model, computed in the
# logistic regression layer
self.negative_log_likelihood = (
self.logRegressionLayer.negative_log_likelihood
)
# same holds for the function computing the number of errors
self.errors = self.logRegressionLayer.errors
# the parameters of the model are the parameters of the two layer it is
# made out of
self.params = self.hiddenLayer1.params + self.hiddenLayer2.params + self.logRegressionLayer.params
# end-snippet-3
# keep track of model input
self.input = input
我還除去可讀性一些評論。輸出誤差我得到的是：
ValueError: Shape mismatch: x has 250 cols (and 20 rows) but y has 784 rows (and 250 cols) Apply node that caused the error: Dot22(Elemwise{Composite{tanh((i0 + i1))}}[(0, 0)].0, W) Inputs types: [TensorType(float64, matrix), TensorType(float64, matrix)] Inputs shapes: [(20, 250), (784, 250)] Inputs strides: [(2000, 8), (2000, 8)] Inputs values: ['not shown', 'not shown']