Merge pull request #3729 from f0k/fix-conv2d-interface

theano/tensor/nnet/__init__.py

@@ -35,9 +35,9 @@ from .bn import batch_normalization
 import warnings
 from .abstract_conv import conv2d as abstract_conv2d
 
-def conv2d(input, filters, input_shape=None, image_shape=None, filter_shape=None,
+def conv2d(input, filters, input_shape=None, filter_shape=None,
            border_mode='valid', subsample=(1, 1), filter_flip=True,
-           **kargs):
+           image_shape=None, **kwargs):
     """
     This function will build the symbolic graph for convolving a mini-batch of a
     stack of 2D inputs with a set of 2D filters. The implementation is modelled
@@ -53,9 +53,6 @@ def conv2d(input, filters, input_shape=None, image_shape=None, filter_shape=None
         (output channels, input channels, filter rows, filter columns).
         See the optional parameter ``filter_shape``.
 
-    :type image_shape: None, tuple/list of len 4 of int or Constant variable
-    :param image_shape Deprecated, use input_shape instead
-
     :type input_shape: None, tuple/list of len 4 of int or Constant variable
     :param input_shape: The shape of the input parameter.
         Optional, possibly used to choose an optimal implementation.
@@ -93,21 +90,27 @@ def conv2d(input, filters, input_shape=None, image_shape=None, filter_shape=None
         to as a convolution, and this is the default. If ``False``, the filters
         are not flipped and the operation is referred to as a cross-correlation.
 
+    :type image_shape: None, tuple/list of len 4 of int or Constant variable
+    :param image_shape Deprecated alias for `input_shape`
+
+    :param **kwargs Any other keyword arguments are accepted for backwards
+        compatibility, but will be ignored.
+
     :rtype: symbolic 4D tensor
     :return: set of feature maps generated by convolutional layer. Tensor is
         of shape (batch size, output channels, output rows, output columns)
     """
 
-    if len(kargs.keys()) > 0:
-        warnings.warn(str(kargs.keys()) +
+    if len(kwargs.keys()) > 0:
+        warnings.warn(str(kwargs.keys()) +
                       " are now deprecated in "
                       "`tensor.nnet.abstract_conv.conv2d` interface"
                       " and will be ignored.")
 
     if image_shape is not None:
-        warnings.warn("image_shape is no longer supported in "
-                      "`tensor.nnet.abstract_conv.conv2d` interface"
-                      " use input_shape instead.")
+        warnings.warn("The `image_shape` keyword argument to "
+                      "`tensor.nnet.conv2d` is deprecated, it has been "
+                      "renamed to `input_shape`.")
         if input_shape is None:
             input_shape = image_shape
         else:
@@ -115,6 +118,6 @@ def conv2d(input, filters, input_shape=None, image_shape=None, filter_shape=None
                              " be provided at the same time.")
 
     return abstract_conv2d(input, filters, input_shape, filter_shape,
-                           border_mode, subsample=(1, 1))
+                           border_mode, subsample, filter_flip)