Start testing image classification with high level api

Author: jetfuel

…on/notest_image_classification_resnet.py …tion/test_image_classification_resnet.pypython/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_resnet.py renamed to python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py python/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_resnet.py renamed to python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py

@@ -80,8 +80,9 @@ def train_network():
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(cost)
-    accuracy = fluid.layers.accuracy(input=predict, label=label)
-    return avg_cost, accuracy
+    # accuracy = fluid.layers.accuracy(input=predict, label=label)
+    # return avg_cost, accuracy
+    return avg_cost
 
 
 def train(use_cuda, save_path):
@@ -97,12 +98,12 @@ def train(use_cuda, save_path):
         paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
 
     def event_handler(event):
-        if isinstance(event, fluid.EndIteration):
-            if (event.batch_id % 10) == 0:
+        if isinstance(event, fluid.EndEpochEvent):
+            if (event.epoch % 10) == 0:
                 avg_cost, accuracy = trainer.test(reader=test_reader)
 
                 print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
-                    event.batch_id + 1, avg_cost, accuracy))
+                    event.epoch + 1, avg_cost, accuracy))
 
                 if accuracy > 0.01:  # Low threshold for speeding up CI
                     trainer.params.save(save_path)
@@ -114,13 +115,18 @@ def event_handler(event):
         optimizer=fluid.optimizer.Adam(learning_rate=0.001),
         place=place,
         event_handler=event_handler)
-    trainer.train(train_reader, EPOCH_NUM, event_handler=event_handler)
+
+    trainer.train(
+        num_epochs=EPOCH_NUM,
+        event_handler=event_handler,
+        reader=train_reader,
+        feed_order=['pixel', 'label'])
 
 
 def infer(use_cuda, save_path):
-    params = fluid.Params(save_path)
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.Inferencer(inference_network, params, place=place)
+    inferencer = fluid.Inferencer(
+        inference_network, save_path=save_path, place=place)
 
     # The input's dimension of conv should be 4-D or 5-D.
     # Use normilized image pixels as input data, which should be in the range

…ation/notest_image_classification_vgg.py …ication/test_image_classification_vgg.pypython/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_vgg.py renamed to python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py python/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_vgg.py renamed to python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py

@@ -59,8 +59,9 @@ def train_network():
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(cost)
-    accuracy = fluid.layers.accuracy(input=predict, label=label)
-    return avg_cost, accuracy
+    # accuracy = fluid.layers.accuracy(input=predict, label=label)
+    # return avg_cost, accuracy
+    return avg_cost
 
 
 def train(use_cuda, save_path):
@@ -76,12 +77,12 @@ def train(use_cuda, save_path):
         paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
 
     def event_handler(event):
-        if isinstance(event, fluid.EndIteration):
-            if (event.batch_id % 10) == 0:
+        if isinstance(event, fluid.EndEpochEvent):
+            if (event.epoch % 10) == 0:
                 avg_cost, accuracy = trainer.test(reader=test_reader)
 
                 print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
-                    event.batch_id + 1, avg_cost, accuracy))
+                    event.epoch + 1, avg_cost, accuracy))
 
                 if accuracy > 0.01:  # Low threshold for speeding up CI
                     trainer.params.save(save_path)