confusion_matrix.py

from itertools import product

import theano.tensor as T
from theano import config

from pylearn2.models.mlp import Softmax

class ConfusionSoftmax(Softmax):
    def get_layer_monitoring_channels(self, state_below=None,
                                      state=None, targets=None):
        rval = super(ConfusionSoftmax, self).get_layer_monitoring_channels(state_below, state, targets)

        if (state_below is not None) or (state is not None):
            if state is None:
                state = self.fprop(state_below)

            if (targets is not None):
                if ((not self._has_binary_target) or
                        self.binary_target_dim == 1):
                    y = T.argmax(targets, axis=1)
                    
                    for j in range(10):
                        rval["y[%i]" % (j)] = T.cast(y[j], config.floatX)
        
        return rval