Specify output dimension more explicitly.

lmjohns3 · Jan 18, 2016 · 7a8f25e · 7a8f25e
1 parent b17228f
commit 7a8f25e
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Showing 3 changed files with 16 additions and 6 deletions.
diff --git a/theanets/feedforward.py b/theanets/feedforward.py
@@ -353,9 +353,11 @@ class Classifier(graph.Network):
     DEFAULT_OUTPUT_ACTIVATION = 'softmax'
     '''Default activation for the output layer.'''
 
+    OUTPUT_NDIM = 1
+    '''Number of dimensions for holding output data arrays.'''
+
     def __init__(self, layers, loss='xe', weighted=False, rng=13):
-        super(Classifier, self).__init__(layers, rng=rng)
-        self.set_loss(form=loss, target=1, weighted=weighted)
+        super(Classifier, self).__init__(layers, loss=loss, weighted=weighted, rng=rng)
 
     def monitors(self, **kwargs):
         '''Return expressions that should be computed to monitor training.

diff --git a/theanets/graph.py b/theanets/graph.py
@@ -80,6 +80,9 @@ class Network(object):
     INPUT_NDIM = 2
     '''Number of dimensions for holding input data arrays.'''
 
+    OUTPUT_NDIM = 2
+    '''Number of dimensions for holding output data arrays.'''
+
     def __init__(self, layers=(), loss='mse', weighted=False, rng=13):
         self._graphs = {}     # cache of symbolic computation graphs
         self._functions = {}  # cache of callable feedforward functions
@@ -94,7 +97,7 @@ def __init__(self, layers=(), loss='mse', weighted=False, rng=13):
         if loss and self.layers:
             self.set_loss(loss,
                           weighted=weighted,
-                          target=self.INPUT_NDIM,
+                          target=self.OUTPUT_NDIM,
                           output_name=self.layers[-1].output_name())
 
     def add_layer(self, layer=None, is_output=False, **kwargs):

diff --git a/theanets/recurrent.py b/theanets/recurrent.py
@@ -228,6 +228,9 @@ class Autoencoder(feedforward.Autoencoder):
     INPUT_NDIM = 3
     '''Number of dimensions for holding input data arrays.'''
 
+    OUTPUT_NDIM = 3
+    '''Number of dimensions for holding output data arrays.'''
+
 
 class Regressor(feedforward.Regressor):
     '''A regressor attempts to produce a target output given some inputs.
@@ -291,6 +294,9 @@ class Regressor(feedforward.Regressor):
     INPUT_NDIM = 3
     '''Number of dimensions for holding input data arrays.'''
 
+    OUTPUT_NDIM = 3
+    '''Number of dimensions for holding output data arrays.'''
+
 
 class Classifier(feedforward.Classifier):
     '''A classifier computes a distribution over labels, given an input.
@@ -379,9 +385,8 @@ class Classifier(feedforward.Classifier):
     INPUT_NDIM = 3
     '''Number of dimensions for holding input data arrays.'''
 
-    def __init__(self, layers, loss='xe', weighted=False, rng=13):
-        super(Classifier, self).__init__(layers, rng=rng)
-        self.set_loss(loss, target=2, weighted=weighted)
+    OUTPUT_NDIM = 2
+    '''Number of dimensions for holding output data arrays.'''
 
     def predict_sequence(self, labels, steps, streams=1, rng=None):
         '''Draw a sequential sample of class labels from this network.