Example #1
0
    def get_monitoring_channels(self, V):

        theano_rng = RandomStreams(42)

        norms = theano_norms(self.weights)

        H = self.mean_h_given_v(V)

        h = H.mean(axis=0)

        return { 'bias_hid_min' : T.min(self.bias_hid),
                 'bias_hid_mean' : T.mean(self.bias_hid),
                 'bias_hid_max' : T.max(self.bias_hid),
                 'bias_vis_min' : T.min(self.bias_vis),
                 'bias_vis_mean' : T.mean(self.bias_vis),
                 'bias_vis_max': T.max(self.bias_vis),
                 'h_min' : T.min(h),
                 'h_mean': T.mean(h),
                 'h_max' : T.max(h),
                 'W_min' : T.min(self.weights),
                 'W_max' : T.max(self.weights),
                 'W_norms_min' : T.min(norms),
                 'W_norms_max' : T.max(norms),
                 'W_norms_mean' : T.mean(norms),
                'reconstruction_error' : self.reconstruction_error(V, theano_rng) }
Example #2
0
    def get_monitoring_channels(self, V):

        theano_rng = RandomStreams(42)

        norms = theano_norms(self.weights)

        H = self.mean_h_given_v(V)

        h = H.mean(axis=0)

        return {
            'bias_hid_min': T.min(self.bias_hid),
            'bias_hid_mean': T.mean(self.bias_hid),
            'bias_hid_max': T.max(self.bias_hid),
            'bias_vis_min': T.min(self.bias_vis),
            'bias_vis_mean': T.mean(self.bias_vis),
            'bias_vis_max': T.max(self.bias_vis),
            'h_min': T.min(h),
            'h_mean': T.mean(h),
            'h_max': T.max(h),
            'W_min': T.min(self.weights),
            'W_max': T.max(self.weights),
            'W_norms_min': T.min(norms),
            'W_norms_max': T.max(norms),
            'W_norms_mean': T.mean(norms),
            'reconstruction_error': self.reconstruction_error(V, theano_rng)
        }
    def get_monitoring_channels(self, V):

        vb, hb, weights = self.get_params()
        norms = theano_norms(weights)
        return {'W_min': tensor.min(weights),
                'W_max': tensor.max(weights),
                'W_norm_mean': tensor.mean(norms),
                'bias_hid_min' : tensor.min(hb),
                'bias_hid_mean' : tensor.mean(hb),
                'bias_hid_max' : tensor.max(hb),
                'bias_vis_min' : tensor.min(vb),
                'bias_vis_mean' : tensor.mean(vb),
                'bias_vis_max': tensor.max(vb),
        }
    def get_monitoring_channels(self, V):

        vb, hb, weights = self.get_params()
        norms = theano_norms(weights)
        return {
            'W_min': tensor.min(weights),
            'W_max': tensor.max(weights),
            'W_norm_mean': tensor.mean(norms),
            'bias_hid_min': tensor.min(hb),
            'bias_hid_mean': tensor.mean(hb),
            'bias_hid_max': tensor.max(hb),
            'bias_vis_min': tensor.min(vb),
            'bias_vis_mean': tensor.mean(vb),
            'bias_vis_max': tensor.max(vb),
        }