def get_monitoring_channels(self):

        W = self.W

        assert W.ndim == 2

        sq_W = T.sqr(W)

        row_norms = T.sqrt(sq_W.sum(axis=1))
        col_norms = T.sqrt(sq_W.sum(axis=0))

        rval = OrderedDict([
            ('boltzmann_row_norms_min', row_norms.min()),
            ('boltzmann_row_norms_mean', row_norms.mean()),
            ('boltzmann_row_norms_max', row_norms.max()),
            ('boltzmann_col_norms_min', col_norms.min()),
            ('boltzmann_col_norms_mean', col_norms.mean()),
            ('boltzmann_col_norms_max', col_norms.max()),
        ])

        ising_W = self.ising_weights()

        rval['ising_W_min'] = ising_W.min()
        rval['ising_W_max'] = ising_W.max()

        ising_b = self.ising_b()

        rval['ising_b_min'] = ising_b.min()
        rval['ising_b_max'] = ising_b.max()

        if hasattr(self, 'noisy_sampling_W'):
            rval['noisy_sampling_W_min'] = self.noisy_sampling_W.min()
            rval['noisy_sampling_W_max'] = self.noisy_sampling_W.max()
            rval['noisy_sampling_b_min'] = self.noisy_sampling_b.min()
            rval['noisy_sampling_b_max'] = self.noisy_sampling_b.max()

        return rval
Returns list of log entries in tuple form.
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/dbm/ising.py/BoltzmannIsingHidden.get_monitoring_channels--crossover with--dataset/ETHPy150Open morepath/morepath/morepath/tests/capturelog.py/CaptureLog.record_tuples