poollayer.py 文件源码

def __init__(self, rng, inputVar, cfgParams, copyLayer=None, layerNum=None):
        """
        Allocate a PoolLayer with shared variable internal parameters.

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type inputVar: theano.tensor.dtensor4
        :param inputVar: symbolic image tensor, of shape image_shape

        :type cfgParams: PoolLayerParams
        """

        floatX = theano.config.floatX  # @UndefinedVariable

        outputDim = cfgParams.outputDim
        poolsize = cfgParams.poolsize
        inputDim = cfgParams.inputDim
        activation = cfgParams.activation
        poolType = cfgParams.poolType

        self.cfgParams = cfgParams
        self.layerNum = layerNum

        self.inputVar = inputVar

        if inputVar.type.ndim != 4:
            raise TypeError()

        self.params = []
        self.weights = []

        # downsample each feature map individually, using maxpooling
        if poolType == 0:
            # use maxpooling
            pooled_out = pool_2d(input=self.inputVar, ds=poolsize, ignore_border=True)
        elif poolType == 1:
            # use average pooling
            pooled_out = theano.sandbox.neighbours.images2neibs(ten4=self.inputVar, neib_shape=poolsize, mode='ignore_borders').mean(axis=-1)
            new_shape = T.cast(T.join(0, self.inputVar.shape[:-2], T.as_tensor([self.inputVar.shape[2]//poolsize[0]]), T.as_tensor([self.inputVar.shape[3]//poolsize[1]])), 'int64')
            pooled_out = T.reshape(pooled_out, new_shape, ndim=4)
        elif poolType == 3:
            # use subsampling and ignore border
            pooled_out = self.inputVar[:, :, :(inputDim[2]//poolsize[0])*poolsize[0], :(inputDim[3]//poolsize[1])*poolsize[1]][:, :, ::poolsize[0], ::poolsize[1]]
        elif poolType == -1:
            # no pooling at all
            pooled_out = self.inputVar
        else:
            raise ValueError("Unknown pool type!")

        self.output = (pooled_out if activation is None
                       else activation(pooled_out))

        self.output.name = 'output_layer_{}'.format(self.layerNum)