mlp.py

from hidden_layer import HiddenLayer
from logistic_regression import LogisticRegression
import theano.tensor as T
import logging


class MLP(object):
    """Multi-Layer Perceptron Class

    A multilayer perceptron is a feedforward artificial neural network model
    that has one layer or more of hidden units and nonlinear activations.
    Intermediate layers usually have as activation function tanh or the
    sigmoid function (defined here by a ``HiddenLayer`` class)  while the
    top layer is a softamx layer (defined here by a ``LogisticRegression``
    class).
    """

    def __init__(self, rng, input, n_in, n_hidden, activation, n_out, l1_reg, l2_reg):
        """Initialize the parameters for the multilayer perceptron

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.TensorType
        :param input: symbolic variable that describes the input of the
        architecture (one minibatch)

        :type n_in: int
        :param n_in: number of input units, the dimension of the space in
        which the datapoints lie

        :type n_hidden: int
        :param n_hidden: number of hidden units

        :type n_out: int
        :param n_out: number of output units, the dimension of the space in
        which the labels lie

        """

        logging.debug(
            'Using MultiLayerPerceptron with %i inputs, %i %s hidden units, and %i outputs' %
            (n_in, n_hidden, activation, n_out)
        )
        # Since we are dealing with a one hidden layer MLP, this will translate
        # into a HiddenLayer with a tanh activation function connected to the
        # LogisticRegression layer; the activation function can be replaced by
        # sigmoid or any other nonlinear function
        self.hiddenLayer = HiddenLayer(
            rng=rng,
            input=input,
            n_in=n_in,
            n_out=n_hidden,
            activation=activation
        )

        # The logistic regression layer gets as input the hidden units
        # of the hidden layer
        self.logRegressionLayer = LogisticRegression(
            input=self.hiddenLayer.output,
            n_in=n_hidden,
            n_out=n_out
        )
        # end-snippet-2 start-snippet-3
        # L1 norm ; one regularization option is to enforce L1 norm to
        # be small
        self.L1 = (
            abs(self.hiddenLayer.W).sum()
            + abs(self.logRegressionLayer.W).sum()
        )

        # square of L2 norm ; one regularization option is to enforce
        # square of L2 norm to be small
        self.L2_sqr = (
            (self.hiddenLayer.W ** 2).sum()
            + (self.logRegressionLayer.W ** 2).sum()
        )

        # negative log likelihood of the MLP is given by the negative
        # log likelihood of the output of the model, computed in the
        # logistic regression layer
        self.negative_log_likelihood = (
            self.logRegressionLayer.negative_log_likelihood
        )
        # same holds for the function computing the number of errors
        self.errors = self.logRegressionLayer.errors

        # the parameters of the model are the parameters of the two layer it is
        # made out of
        self.params = self.hiddenLayer.params + self.logRegressionLayer.params

        self.l1_reg = l1_reg
        self.l2_reg = l2_reg

    def updates(self, cost, learning_rate):
        # compute the gradient of cost with respect to theta (sotred in params)
        # the resulting gradients will be stored in a list gparams
        gparams = [T.grad(cost, param) for param in self.params]

        # specify how to update the parameters of the model as a list of
        # (variable, update expression) pairs

        # given two list the zip A = [a1, a2, a3, a4] and B = [b1, b2, b3, b4]
        # of same length, zip generates a list C of same size, where each
        # element is a pair formed from the two lists :
        #    C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
        return [
            (param, param - learning_rate * gparam)
            for param, gparam in zip(self.params, gparams)
        ]

    def cost(self, y):
        return (
            self.negative_log_likelihood(y)
            + self.l1_reg * self.L1
            + self.l2_reg * self.L2_sqr
        )