Method get_cost_updates

code/dA.py:233–260

This function computes the cost and the updates for one trainng step of the dA

(self, corruption_level, learning_rate)

        return T.nnet.sigmoid(T.dot(hidden, self.W_prime) + self.b_prime)

    def get_cost_updates(self, corruption_level, learning_rate):
        """ This function computes the cost and the updates for one trainng
        step of the dA """

        tilde_x = self.get_corrupted_input(self.x, corruption_level)
        y = self.get_hidden_values(tilde_x)
        z = self.get_reconstructed_input(y)
        # note : we sum over the size of a datapoint; if we are using
        #        minibatches, L will be a vector, with one entry per
        #        example in minibatch
        L = - T.sum(self.x * T.log(z) + (1 - self.x) * T.log(1 - z), axis=1)
        # note : L is now a vector, where each element is the
        #        cross-entropy cost of the reconstruction of the
        #        corresponding example of the minibatch. We need to
        #        compute the average of all these to get the cost of
        #        the minibatch
        cost = T.mean(L)

        # compute the gradients of the cost of the `dA` with respect
        # to its parameters
        gparams = T.grad(cost, self.params)
        # generate the list of updates
        updates = [
            (param, param - learning_rate * gparam)
            for param, gparam in zip(self.params, gparams)
        ]

        return (cost, updates)


def test_dA(learning_rate=0.1, training_epochs=15,