def __init__(self, rng, hiddenLayerList, 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 hiddenLayerList: [HiddenLayer instances]
:param hiddenLayerList: A list of hidden layers
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# connect hidden layers (no need to, they're already connected outside when building them)
self.hiddenLayers=hiddenLayerList
# prevLy=hiddenLayerList[0]
# prevLy.input=input
# for ly in hiddenLayerList[1:]:
# ly.input=prevLy.output
# prevLy=ly
# The logistic regression layer gets as input the hidden units of the hidden layer
self.logRegressionLayer = LogisticRegression(
input=hiddenLayerList[-1].output,
n_in=hiddenLayerList[-1].inOutDim[1],
n_out=n_out)
# symbolic variables for data
self.X=self.hiddenLayers[0].input # training data
self.y=T.bvector('y') # labels for training data
# L1 norm ; one regularization option is to enforce L1 norm to be small
self.L1 = abs(self.logRegressionLayer.W).sum()
for ly in self.hiddenLayers:
self.L1 += abs(ly.W).sum()
# square of L2 norm ; one regularization option is to enforce square of L2 norm to be small
self.L2_sqr = (self.logRegressionLayer.W ** 2).sum()
for ly in self.hiddenLayers:
self.L2_sqr += (ly.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 all layers
self.params=self.logRegressionLayer.params
for ly in self.hiddenLayers:
self.params+=ly.params
