Method propdown

code/rbm.py:165–177

This function propagates the hidden units activation downwards to the visible units Note that we return also the pre_sigmoid_activation of the layer. As it will turn out later, due to how Theano deals with optimizations, this symbolic variable will be needed to write

(self, hid)

        return [pre_sigmoid_h1, h1_mean, h1_sample]

    def propdown(self, hid):
        '''This function propagates the hidden units activation downwards to
        the visible units

        Note that we return also the pre_sigmoid_activation of the
        layer. As it will turn out later, due to how Theano deals with
        optimizations, this symbolic variable will be needed to write
        down a more stable computational graph (see details in the
        reconstruction cost function)

        '''
        pre_sigmoid_activation = T.dot(hid, self.W.T) + self.vbias
        return [pre_sigmoid_activation, T.nnet.sigmoid(pre_sigmoid_activation)]

    def sample_v_given_h(self, h0_sample):
        ''' This function infers state of visible units given hidden units '''