add inputs the classifer

memimo · memimo · commit 373dfbfea523 · 2015-06-26T17:52:33.000-04:00
diff --git a/code/logistic_sgd.py b/code/logistic_sgd.py
@@ -44,7 +44,6 @@
 
 import theano
 import theano.tensor as T
-from theano.gof import graph
 
 
 class LogisticRegression(object):
@@ -110,6 +109,9 @@ def __init__(self, input, n_in, n_out):
         # parameters of the model
         self.params = [self.W, self.b]
 
+        # keep track of model input
+        self.input = input
+
     def negative_log_likelihood(self, y):
         """Return the mean of the negative log-likelihood of the prediction
         of this model under a given target distribution.
@@ -447,17 +449,11 @@ def predict():
 
     # load the saved model
     classifier = cPickle.load(open('best_model.pkl'))
-    y_pred = classifier.y_pred
-
-    # find the input to theano graph
-    inputs = graph.inputs([y_pred])
-    # select only x
-    inputs = [item for item in inputs if item.name == 'x']
 
     # compile a predictor function
     predict_model = theano.function(
-        inputs=inputs,
-        outputs=y_pred)
+        inputs=[classifier.input],
+        outputs=classifier.y_pred)
 
     # We can test it on some examples from test test
     dataset='mnist.pkl.gz'
diff --git a/doc/logreg.txt b/doc/logreg.txt
@@ -274,8 +274,7 @@ labels of new data. ``predict`` function shows an example of how
 this could be done.
 
 .. literalinclude:: ../code/logistic_sgd.py
-  :start-after:  ' ran for %.1fs' % ((end_time - start_time)))
-  :end-before: if __name__ == '__main__':
+    :pyobject: predict
 
 
 .. rubric:: Footnotes
