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author | Alex Auvolat <alex.auvolat@ens.fr> | 2015-06-15 13:57:37 -0400 |
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committer | Alex Auvolat <alex.auvolat@ens.fr> | 2015-06-15 13:57:37 -0400 |
commit | 211c2272c544ab0bbf7b87b374736a71c790ac8e (patch) | |
tree | 802557e488eff0362c7c5928c1c3d4ec402fa54d /gentext.py | |
parent | bbbf8861503b6102bbd52af3dbd4041cd3560562 (diff) | |
download | text-rnn-211c2272c544ab0bbf7b87b374736a71c790ac8e.tar.gz text-rnn-211c2272c544ab0bbf7b87b374736a71c790ac8e.zip |
Put GenText in separate file
Diffstat (limited to 'gentext.py')
-rw-r--r-- | gentext.py | 61 |
1 files changed, 61 insertions, 0 deletions
diff --git a/gentext.py b/gentext.py new file mode 100644 index 0000000..b8a27bf --- /dev/null +++ b/gentext.py @@ -0,0 +1,61 @@ +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 + + + |