Fix errors computation

1 files changed, 9 insertions, 6 deletions

diff --git a/simulation/vi_blocks.py b/simulation/vi_blocks.py
index 50c7fb1..e2e3bd9 100644
--- a/simulation/vi_blocks.py
+++ b/simulation/vi_blocks.py
@@ -26,7 +26,7 @@ def create_vi_model(n_nodes, n_samp=100):
     """return variational inference theano computation graph"""
     def aux(a, b):
         rand = a + b * np.random.normal(size=(n_nodes, n_nodes))
-        return np.clip(rand, 1e-3, 1 - 1e-3).astype(theano.config.floatX)
+        return np.clip(rand, 1e-10, 1000).astype(theano.config.floatX)
 
     x = tsr.matrix(name='x', dtype='int8')
     s = tsr.matrix(name='s', dtype='int8')
@@ -54,12 +54,14 @@ if __name__ == "__main__":
     batch_size = 100
     freq = 10
     graph = utils.create_wheel(1000)
+    g_shared = theano.shared(value=graph, name='graph')
     n_samples = 50
-    #graph = utils.create_random_graph(n_nodes=10)
+    # graph = utils.create_random_graph(n_nodes=10)
     print('GRAPH:\n', graph, '\n-------------\n')
 
     x, s, mu, sig, cost = create_vi_model(len(graph), n_samples)
-    rmse = ub.rmse_error(graph, mu)
+    rmse = ub.rmse_error(g_shared, mu)
+    error = ub.absolute_error(g_shared, mu)
 
     step_rules = algorithms.CompositeRule([algorithms.AdaDelta(),
                                            ClippedParams(1e-3, 1000)])
@@ -72,11 +74,12 @@ if __name__ == "__main__":
         log_backend="sqlite",
         extensions=[
             be.FinishAfter(after_n_batches=10**3),
-            bm.TrainingDataMonitoring([cost, rmse, mu], every_n_batches=freq),
+            bm.TrainingDataMonitoring([cost, rmse, mu, error],
+                                      every_n_batches=freq),
             be.Printing(every_n_batches=freq, after_epoch=False),
             ub.JSONDump("logs/nonactive_vi.json", every_n_batches=freq),
-            #ub.ActiveLearning(dataset=data_stream.dataset, params=graph,
-            #every_n_batches=freq)
+            # ub.ActiveLearning(dataset=data_stream.dataset, params=graph,
+            # every_n_batches=freq)
         ]
     )
     loop.run()