fixed the name issue for conv_operator and added a test case (#131)

PaddlePaddle · Sep 28, 2016 · 1c56e0d · 1c56e0d
1 parent 942d2b6
commit 1c56e0d
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Showing 2 changed files with 13 additions and 4 deletions.
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
@@ -2704,8 +2704,8 @@ def conv_operator(img, filter, filter_size, num_filters,
                         PaddlePaddle now supports rectangular filters,
                         the filter's shape can be (filter_size, filter_size_y).
     :type filter_size_y: int
-    :param num_filter: channel of output data.
-    :type num_filter: int
+    :param num_filters: channel of output data.
+    :type num_filters: int
     :param num_channel: channel of input data.
     :type num_channel: int
     :param stride: The x dimension of the stride.
@@ -2726,7 +2726,7 @@ def conv_operator(img, filter, filter_size, num_filters,
     if padding_y is None:
         padding_y = padding
     op = ConvOperator(input_layer_names=[img.name, filter.name],
-                      num_filters = num_filter,
+                      num_filters = num_filters,
                       conv_conf=Conv(filter_size=filter_size,
                                      padding=padding,
                                      stride=stride,

diff --git a/python/paddle/trainer_config_helpers/tests/layers_test_config.py b/python/paddle/trainer_config_helpers/tests/layers_test_config.py
@@ -23,14 +23,23 @@
 
 x1 = fc_layer(input=x, size=5)
 y1 = fc_layer(input=y, size=5)
+
+z1 = mixed_layer(act=LinearActivation(),
+                 input=[conv_operator(img=x1,
+                                      filter=y1,
+                                      filter_size=1,
+                                      num_filters=5,
+                                      num_channel=5,
+                                      stride=1)])
+
 y2 = fc_layer(input=y, size=15)
 
 cos1 = cos_sim(a=x1, b=y1)
 cos3 = cos_sim(a=x1, b=y2, size=3)
 
 linear_comb = linear_comb_layer(weights=x1, vectors=y2, size=3)
 
-out = fc_layer(input=[cos1, cos3, linear_comb, z],
+out = fc_layer(input=[cos1, cos3, linear_comb, z, z1],
                size=num_classes,
                act=SoftmaxActivation())