def make_V_O_M(self):
from numpy import zeros, ones
V = zeros((self.T, self.batch_size, self.V))
O = zeros((self.T, self.batch_size, self.O))
M = zeros((self.T, self.batch_size, 1))
from pylab import rand, randn
for b in range(self.batch_size):
from pylab import randint
if self.batch_size < self.char_size**self.num_chars:
# our minibatch is too small and requires random
# sampling
data = randint(self.char_size, size=self.num_chars)
elif self.batch_size == self.char_size**self.num_chars:
# our minibatch is large enoguh to contain all possible
# sequences, so each possible sequence will appear precisely
# once in the batch.
from pylab import base_repr, amap
data = amap(int,
base_repr(
b,
base=self.char_size,
padding=self.num_chars))[-self.num_chars:]
else:
raise TypeError("self.batch_size (%s) is bigger than %s" %
(self.batch_size, self.char_size**self.num_chars))
# input
V[:, b, -1] = 1
V[:self.num_chars, b, :] = 0
V[:self.num_chars, b, :self.char_size] = \
expand(data,
self.char_size)
if self.teacher_force:
# I'll admit I haven't tested teacher_force.
V[-self.num_chars:, b, :] = 0
V[-self.num_chars:, b, :self.char_size] = \
expand(data,
self.char_size)
# a pre signal. Cool.
V[-self.num_chars-1, b, :] = 0
V[-self.num_chars-1, b, -2] = 1
# output:
O[:, b, -1] = 1
O[-self.num_chars:, b, :] = 0
O[-self.num_chars:, b, :self.char_size] = \
expand(data,
self.char_size)
if self.only_predict_the_memory_bits:
M[:, b, :] = 0
M[-self.num_chars:, b, :] = 1
else:
M[:, b, :] = 1
return V, O, M
