dense.py

# -*- coding: utf-8 -*-
import numpy

import theano
import theano.tensor as T


class DenseLayer(object):
    def __init__(self, rng, input, n_in, n_out, W=None, b=None,
                 activation=T.tanh):

        self.input = input

        if W is None:
            W_values = numpy.asarray(
                numpy.random.uniform(
                    low=-numpy.sqrt(6. / (n_in + n_out)),
                    high=numpy.sqrt(6. / (n_in + n_out)),
                    size=(n_in, n_out)
                ),
                dtype=theano.config.floatX  # @UndefinedVariable
            )

            W = theano.shared(value=W_values, name='W', borrow=True)

        if b is None:
            b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)  # @UndefinedVariable
            b = theano.shared(value=b_values, name='b', borrow=True)

        self.W = W
        self.b = b

        if activation is None:
            self.y_pred = T.dot(input, self.W) + self.b
        else:
            self.output = activation(T.dot(input, self.W) + self.b)

        self.params = [self.W, self.b]

    def mse(self, y):
        return T.mean((self.y_pred.T-y)**2)