Fix up conv models to match current master.

theanets/convolution.py

@@ -6,47 +6,14 @@
 
 
 class Regressor(feedforward.Regressor):
-    '''A regressor attempts to produce a target output.
+    '''A regressor attempts to produce a target output.'''
 
-    A convolutional regression model takes the following inputs during training:
-
-    - ``x``: A three-dimensional array of input data. Each element of axis 0 of
-      ``x`` is expected to be one moment in time. Each element of axis 1 of
-      ``x`` holds a single sample from a batch of data. Each element of axis 2
-      of ``x`` represents the measurements of a particular input variable across
-      all times and all data items.
-
-    - ``targets``: A two-dimensional array of target output data. Each element
-      of axis 0 of ``targets`` is expected to be one moment in time. Each
-      element of axis 1 of ``targets`` holds a single sample from a batch of
-      data. Each element of axis 2 of ``targets`` represents the measurements of
-      a particular output variable across all times and all data items.
-    '''
-
-    def __init__(self, layers=(), loss='mse', weighted=False):
-        super(feedforward.Regressor, self).__init__(
-            layers=layers, loss=loss, in_dim=4, out_dim=2, weighted=weighted)
+    INPUT_NDIM = 4
+    '''Number of dimensions for holding input data arrays.'''
 
 
 class Classifier(feedforward.Classifier):
-    '''A classifier attempts to match a 1-hot target output.
-
-    Unlike a feedforward classifier, where the target labels are provided as a
-    single vector, a recurrent classifier requires a vector of target labels for
-    each time step in the input data. So a recurrent classifier model requires
-    the following inputs for training:
-
-    - ``x``: A three-dimensional array of input data. Each element of axis 0 of
-      ``x`` is expected to be one moment in time. Each element of axis 1 of
-      ``x`` holds a single sample in a batch of data. Each element of axis 2 of
-      ``x`` represents the measurements of a particular input variable across
-      all times and all data items in a batch.
-
-    - ``labels``: A one-dimensional vector of integer target labels. Each
-      element of ``labels`` is expected to be the class index for a single batch
-      item.
-    '''
-
-    def __init__(self, layers=(), loss='xe', weighted=False):
-        super(feedforward.Classifier, self).__init__(
-            layers=layers, loss=loss, in_dim=4, out_dim=1, weighted=weighted)
+    '''A classifier attempts to match a 1-hot target output.'''
+
+    INPUT_NDIM = 4
+    '''Number of dimensions for holding input data arrays.'''