Prediction should be a part of inference_network in new API (#10356)

python/paddle/fluid/tests/book/image_classification/notest_image_classification_resnet.py

@@ -64,15 +64,14 @@ def resnet_cifar10(input, depth=32):
     res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
     pool = fluid.layers.pool2d(
         input=res3, pool_size=8, pool_type='avg', pool_stride=1)
-    return pool
+    predict = fluid.layers.fc(input=pool, size=10, act='softmax')
+    return predict
 
 
 def inference_network():
-    classdim = 10
     data_shape = [3, 32, 32]
     images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
-    net = resnet_cifar10(images, 32)
-    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+    predict = resnet_cifar10(images, 32)
     return predict
 
 

python/paddle/fluid/tests/book/image_classification/notest_image_classification_vgg.py

@@ -43,15 +43,14 @@ def vgg16_bn_drop(input):
     bn = fluid.layers.batch_norm(input=fc1, act='relu')
     drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
     fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
-    return fc2
+    predict = fluid.layers.fc(input=fc2, size=10, act='softmax')
+    return predict
 
 
 def inference_network():
-    classdim = 10
     data_shape = [3, 32, 32]
     images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
-    net = vgg16_bn_drop(images)
-    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+    predict = vgg16_bn_drop(images)
     return predict