controller.py

import theano
import theano.tensor as T
import numpy         as np
from theano_toolkit import utils as U

import controller
import model
import head
from collections import namedtuple
#from theano_toolkit.parameters import Parameters

def build(P,input_size,output_size,mem_size,mem_width,layer_sizes):
	"""
	Create controller function for use during scan op
	"""

	P.W_input_hidden = U.initial_weights(input_size,layer_sizes[0])
	P.W_read_hidden  = U.initial_weights(mem_width, layer_sizes[0])
	P.b_hidden_0 = 0. * U.initial_weights(layer_sizes[0])
	
	hidden_weights = []
	for i in xrange(len(layer_sizes)-1):
		P["W_hidden_%d"%(i+1)] = U.initial_weights(layer_sizes[i],layer_sizes[i+1])
		P["b_hidden_%d"%(i+1)] = 0. * U.initial_weights(layer_sizes[i+1])
		hidden_weights.append((P["W_hidden_%d"%(i+1)],P["b_hidden_%d"%(i+1)]))

	P.W_hidden_output = 0. * U.initial_weights(layer_sizes[-1],output_size)
	P.b_output = 0. * U.initial_weights(output_size)

	def controller(input_t,read_t):
#		print "input_t",input_t.type
		prev_layer = hidden_0 = T.tanh(
				T.dot(input_t,P.W_input_hidden) +\
				T.dot(read_t,P.W_read_hidden) +\
				P.b_hidden_0
			)

#		print "input",read_t.type,input_t.type
#		print "weights",P.W_input_hidden.type,P.W_read_hidden.type,P.b_hidden_0.type
#		print "layer", hidden_0.type
		for W,b in hidden_weights:
			prev_layer = T.tanh(T.dot(prev_layer,W) + b)

		fin_hidden = prev_layer
		output_t = T.nnet.sigmoid(T.dot(fin_hidden,P.W_hidden_output) + P.b_output)
		
		return output_t,fin_hidden
	return controller