eeehhh

plato/tools/common/basic.py

@@ -81,7 +81,7 @@ def running_mean_and_variance(data, decay = None, shape = None, elementwise=True
         var_new = s_new
     add_update(mean_last, mean_new)
     add_update(s_last, s_new)
-    return var_new
+    return mean_new, var_new
 
 
 @symbolic
@@ -93,4 +93,4 @@ def running_variance(data, decay=None, shape = None, elementwise=True, initial_v
     :param shape:
     :return:
     """
-    return running_mean_and_variance(data=data, decay=decay, shape=shape, elementwise=elementwise, initial_var = initial_value)
+    return running_mean_and_variance(data=data, decay=decay, shape=shape, elementwise=elementwise, initial_var = initial_value)[1]

plato/tools/common/online_predictors.py

@@ -1,4 +1,6 @@
 from abc import ABCMeta, abstractmethod
+from contextlib import contextmanager
+
 from plato.interfaces.decorators import symbolic_simple, symbolic_updater
 from plato.interfaces.interfaces import IParameterized
 from plato.tools.optimization.cost import get_named_cost_function
@@ -87,6 +89,25 @@ def parameters(self):
         return self._function.parameters + opt_params
 
 
+_LOCAL_LOSSES = None
+
+
+def declare_local_loss(loss):
+    if _LOCAL_LOSSES is not None:
+        _LOCAL_LOSSES.append(loss)
+
+
+@contextmanager
+def capture_local_losses():
+    global  _LOCAL_LOSSES
+    assert _LOCAL_LOSSES is None, "Local loss book already open"
+    _LOCAL_LOSSES = []
+    try:
+        yield _LOCAL_LOSSES
+    finally:
+        _LOCAL_LOSSES = None
+
+
 class CompiledSymbolicPredictor(IPredictor, IParameterized):
     """
     A Predictor containing the compiled methods for a SymbolicPredictor.
@@ -125,7 +146,12 @@ def __call__(self, x):
         raise NotImplementedError()
 
     def train(self, x, y, cost_fcn, optimizer, assert_all_params_optimized=False, regularization_cost = None):
-        cost = cost_fcn(self.train_call(x), y)
+        with capture_local_losses() as local_losses:
+            cost = cost_fcn(self.train_call(x), y)
+
+        if len(local_losses)>0:
+            cost = cost + sum(local_losses)
+
         if regularization_cost is not None:
             cost = cost + regularization_cost(self.parameters)
         if isinstance(optimizer, dict):

plato/tools/mlp/mlp.py

@@ -28,8 +28,8 @@ def __call__(self, x):
         return x
 
     @symbolic
-    def get_layer_activations(self, x):
-        activations = []
+    def get_layer_activations(self, x, include_input = False):
+        activations = [x] if include_input else []
         for lay in self.layers:
             x = lay(x)
             activations.append(x)