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import sys
import numpy
import theano
from theano import tensor
from blocks.extensions import SimpleExtension
from blocks.graph import ComputationGraph
class GenText(SimpleExtension):
def __init__(self, model, init_text, max_bytes, sample_temperature, **kwargs):
super(GenText, self).__init__(**kwargs)
self.init_text = init_text
self.max_bytes = max_bytes
out = model.out[:, -1, :] / numpy.float32(sample_temperature)
prob = tensor.nnet.softmax(out)
cg = ComputationGraph([prob])
assert(len(cg.inputs) == 1)
assert(cg.inputs[0].name == 'bytes')
state_vars = [theano.shared(v[0:1, :].zeros_like().eval(), v.name+'-gen')
for v, _ in model.states]
givens = [(v, x) for (v, _), x in zip(model.states, state_vars)]
updates= [(x, upd) for x, (_, upd) in zip(state_vars, model.states)]
self.f = theano.function(inputs=cg.inputs, outputs=[prob],
givens=givens, updates=updates)
self.reset_states = theano.function(inputs=[], outputs=[],
updates=[(v, v.zeros_like()) for v in state_vars])
def do(self, which_callback, *args):
print "Sample:"
print "-------"
self.reset_states()
v = numpy.array([ord(i) for i in self.init_text],
dtype='int16')[None, :]
prob, = self.f(v)
sys.stdout.write(self.init_text)
while v.shape[1] < self.max_bytes:
prob = prob / 1.00001
pred = numpy.random.multinomial(1, prob[0, :]).nonzero()[0][0]
v = numpy.concatenate([v, pred[None, None]], axis=1)
sys.stdout.write(chr(int(pred)))
sys.stdout.flush()
prob, = self.f(pred[None, None])
print
print "-------"
print
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