update variational_api_quickstart.ipynb

Author: ferrine

docs/source/notebooks/variational_api_quickstart.ipynb

@@ -131,7 +131,7 @@ def _iterate_without_loss(self, s, _, step_func, progress, callbacks):
                 if np.isnan(self.approx.params[0].get_value()).any():
                     raise FloatingPointError('NaN occurred in optimization.')
                 for callback in callbacks:
-                    callback(self.approx, None, i)
+                    callback(self.approx, None, i+s+1)
         except (KeyboardInterrupt, StopIteration) as e:
             progress.close()
             if isinstance(e, StopIteration):
@@ -168,7 +168,7 @@ def _infmean(input_array):
                     progress.set_description(
                         'Average Loss = {:,.5g}'.format(avg_loss))
                 for callback in callbacks:
-                    callback(self.approx, scores[:i + 1], i)
+                    callback(self.approx, scores[:i + 1], i+s+1)
         except (KeyboardInterrupt, StopIteration) as e:  # pragma: no cover
             # do not print log on the same line
             progress.close()

pymc3/variational/inference.py

@@ -239,7 +239,6 @@ def add_obj_updates(self, updates, obj_n_mc=None, obj_optimizer=adagrad_window,
         if self.op.returns_loss:
             updates.loss = obj_target
 
-    @memoize
     @change_flags(compute_test_value='off')
     def step_function(self, obj_n_mc=None, tf_n_mc=None,
                       obj_optimizer=adagrad_window, test_optimizer=adagrad_window,
@@ -303,7 +302,6 @@ def step_function(self, obj_n_mc=None, tf_n_mc=None,
             step_fn = theano.function([], None, updates=updates, **fn_kwargs)
         return step_fn
 
-    @memoize
     @change_flags(compute_test_value='off')
     def score_function(self, sc_n_mc=None, more_replacements=None, fn_kwargs=None):   # pragma: no cover
         R"""Compile scoring function that operates which takes no inputs and returns Loss