Merge pull request #542 from jetfuel/image_classification_new_api_markdown

Image classification new api markdown

02.recognize_digits/README.md

@@ -421,7 +421,7 @@ def load_image(file):
     im = im / 255.0 * 2.0 - 1.0
     return im
 
-cur_dir = cur_dir = os.getcwd()
+cur_dir = os.getcwd()
 img = load_image(cur_dir + '/image/infer_3.png')
 ```
 

02.recognize_digits/index.html

@@ -463,7 +463,7 @@ def load_image(file):
     im = im / 255.0 * 2.0 - 1.0
     return im
 
-cur_dir = cur_dir = os.getcwd()
+cur_dir = os.getcwd()
 img = load_image(cur_dir + '/image/infer_3.png')
 ```
 

03.image_classification/train.py

@@ -42,6 +42,10 @@ def train_network():
     return [avg_cost, accuracy]
 
 
+def optimizer_program():
+    return fluid.optimizer.Adam(learning_rate=0.001)
+
+
 def train(use_cuda, train_program, params_dirname):
     BATCH_SIZE = 128
     EPOCH_NUM = 2
@@ -56,7 +60,7 @@ def train(use_cuda, train_program, params_dirname):
     def event_handler(event):
         if isinstance(event, fluid.EndStepEvent):
             if event.step % 100 == 0:
-                print("Pass %d, Batch %d, Cost %f, Acc %f" %
+                print("\nPass %d, Batch %d, Cost %f, Acc %f" %
                       (event.step, event.epoch, event.metrics[0],
                        event.metrics[1]))
             else:
@@ -67,15 +71,14 @@ def train(use_cuda, train_program, params_dirname):
             avg_cost, accuracy = trainer.test(
                 reader=test_reader, feed_order=['pixel', 'label'])
 
-            print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
+            print('\nTest with Pass {0}, Loss {1:2.2}, Acc {2:2.2}'.format(
+                event.epoch, avg_cost, accuracy))
             if params_dirname is not None:
                 trainer.save_params(params_dirname)
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     trainer = fluid.Trainer(
-        train_func=train_program,
-        optimizer=fluid.optimizer.Adam(learning_rate=0.001),
-        place=place)
+        train_func=train_program, optimizer_func=optimizer_program, place=place)
 
     trainer.train(
         reader=train_reader,
@@ -99,14 +102,10 @@ def infer(use_cuda, inference_program, params_dirname=None):
         im = im.resize((32, 32), Image.ANTIALIAS)
 
         im = np.array(im).astype(np.float32)
-        # The storage order of the loaded image is W(widht),
+        # The storage order of the loaded image is W(width),
         # H(height), C(channel). PaddlePaddle requires
         # the CHW order, so transpose them.
         im = im.transpose((2, 0, 1))  # CHW
-        # In the training phase, the channel order of CIFAR
-        # image is B(Blue), G(green), R(Red). But PIL open
-        # image in RGB mode. It must swap the channel order.
-        im = im[(2, 1, 0), :, :]  # BGR
         im = im / 255.0
 
         # Add one dimension to mimic the list format.