softmax.py

import numpy as np
import theano
import theano.tensor as T


class softmax(object):
    def __init__(self, x, n_input, n_output):
        self.n_input = n_input
        self.n_output = n_output

        # self.x = x.reshape([-1, x.shape[-1]])

        init_W = np.asarray(np.random.uniform(low=-np.sqrt(1. / n_input),
                                              high=np.sqrt(1. / n_input),
                                              size=(n_input, n_output)), dtype=theano.config.floatX)
        init_b = np.zeros((n_output), dtype=theano.config.floatX)

        self.W = theano.shared(value=init_W, name='output_W', borrow=True)
        self.b = theano.shared(value=init_b, name='output_b', borrow=True)

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

        self.activation = T.nnet.softmax(T.dot(x, self.W) + self.b)
        self.predict = T.argmax(self.activation, axis=-1)