Fixed a error in mnist unitest.

python/paddle/v2/framework/tests/test_mnist.py

@@ -128,7 +128,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
 def cross_entropy_layer(net, input, label):
     cost_name = "cross_entropy_%d" % uniq_id()
     cross_entropy_op = Operator(
-        "cross_entropy", X=input, label=label, Y=cost_name)
+        "cross_entropy", X=input, Label=label, Y=cost_name)
     net.append_op(cross_entropy_op)
     scope.new_var(cost_name)
     net.infer_shape(scope)
@@ -181,7 +181,7 @@ def error_rate(predict, label):
 
 
 images = data_layer(name="pixel", dims=[BATCH_SIZE, 784])
-labels = data_layer(name="label", dims=[BATCH_SIZE])
+labels = data_layer(name="label", dims=[BATCH_SIZE, 1])
 fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid")
 fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid")
 predict = fc_layer(net=forward_net, input=fc2, size=10, act="softmax")
@@ -215,6 +215,7 @@ def test(cost_name):
     for data in test_reader():
         image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
         label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
+        label_data = numpy.expand_dims(label_data, axis=1)
         feed_data(images, image_data)
         feed_data(labels, label_data)
 
@@ -235,6 +236,7 @@ for pass_id in range(PASS_NUM):
     for data in train_reader():
         image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
         label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
+        label_data = numpy.expand_dims(label_data, axis=1)
         feed_data(images, image_data)
         feed_data(labels, label_data)