From 211c2272c544ab0bbf7b87b374736a71c790ac8e Mon Sep 17 00:00:00 2001
From: Alex Auvolat <alex.auvolat@ens.fr>
Date: Mon, 15 Jun 2015 13:57:37 -0400
Subject: Put GenText in separate file

---
 gentext.py | 61 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 gentext.py

(limited to 'gentext.py')

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
+
+
+
-- 
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