Put GenText in separate file

1 files changed, 5 insertions, 55 deletions

diff --git a/train.py b/train.py
index a8c246a..ddd1d0c 100755
--- a/train.py
+++ b/train.py
@@ -22,7 +22,7 @@ from blocks.model import Model
 from blocks.algorithms import GradientDescent
 
 import datastream
-# from apply_model import Apply
+import gentext
 
 logging.basicConfig(level='INFO')
 logger = logging.getLogger(__name__)
@@ -36,57 +36,6 @@ if __name__ == "__main__":
     model_name = sys.argv[1]
     config = importlib.import_module('%s' % model_name)
 
-
-class GenText(SimpleExtension):
-    def __init__(self, model, init_text, max_bytes, **kwargs):
-        super(GenText, self).__init__(**kwargs)
-        
-        self.init_text = init_text
-        self.max_bytes = max_bytes
-
-        out = model.out[:, -1, :] / numpy.float32(config.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
-
-
 class ResetStates(SimpleExtension):
     def __init__(self, state_vars, **kwargs):
         super(ResetStates, self).__init__(**kwargs)
@@ -142,9 +91,10 @@ def train_model(m, train_stream, dump_path=None):
                  server_url='http://eos21:4201/',
                  every_n_epochs=1, after_epoch=False),
 
-            GenText(m, '\nalex\ttu crois ?\n', config.sample_len,
-                    every_n_epochs=config.sample_freq,
-                    after_epoch=False, before_training=True),
+            gentext.GenText(m, '\nalex\ttu crois ?\n',
+                            config.sample_len, config.sample_temperature,
+                            every_n_epochs=config.sample_freq,
+                            after_epoch=False, before_training=True),
             ResetStates([v for v, _ in m.states], after_epoch=True)
         ]
     )