update mobilenet.py

fluid/image_classification/mobilenet.py

@@ -172,13 +172,13 @@ def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
         momentum=0.9,
         regularization=fluid.regularizer.L2Decay(5 * 1e-5))
     opts = optimizer.minimize(avg_cost)
-    accuracy = fluid.evaluator.Accuracy(input=out, label=label)
+
+    b_size = fluid.layers.create_tensor(dtype='int64')
+    b_acc = fluid.layers.accuracy(input=out, label=label, total=b_size)
 
     inference_program = fluid.default_main_program().clone()
     with fluid.program_guard(inference_program):
-        test_accuracy = fluid.evaluator.Accuracy(input=out, label=label)
-        test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
-        inference_program = fluid.io.get_inference_program(test_target)
+        inference_program = fluid.io.get_inference_program(b_acc)
 
     place = fluid.CUDAPlace(0)
     exe = fluid.Executor(place)
@@ -190,24 +190,26 @@ def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
         paddle.dataset.flowers.test(), batch_size=batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
 
+    train_pass_acc = fluid.average.WeightedAverage()
+    test_pass_acc = fluid.average.WeightedAverage()
     for pass_id in range(num_passes):
-        accuracy.reset(exe)
+        train_pass_acc.reset()
         for batch_id, data in enumerate(train_reader()):
-            loss, acc = exe.run(fluid.default_main_program(),
-                                feed=feeder.feed(data),
-                                fetch_list=[avg_cost] + accuracy.metrics)
+            loss, acc, size = exe.run(fluid.default_main_program(),
+                                      feed=feeder.feed(data),
+                                      fetch_list=[avg_cost, b_acc, b_size])
+            train_pass_acc.add(value=acc, weight=size)
             print("Pass {0}, batch {1}, loss {2}, acc {3}".format(
                 pass_id, batch_id, loss[0], acc[0]))
-        pass_acc = accuracy.eval(exe)
 
-        test_accuracy.reset(exe)
+        test_pass_acc.reset()
         for data in test_reader():
-            loss, acc = exe.run(inference_program,
-                                feed=feeder.feed(data),
-                                fetch_list=[avg_cost] + test_accuracy.metrics)
-        test_pass_acc = test_accuracy.eval(exe)
+            loss, acc, size = exe.run(inference_program,
+                                      feed=feeder.feed(data),
+                                      fetch_list=[avg_cost, b_acc, b_size])
+            test_pass_acc.add(value=acc, weight=size)
         print("End pass {0}, train_acc {1}, test_acc {2}".format(
-            pass_id, pass_acc, test_pass_acc))
+            pass_id, train_pass_acc.eval(), test_pass_acc.eval()))
         if pass_id % 10 == 0:
             model_path = os.path.join(model_save_dir, str(pass_id))
             print 'save models to %s' % (model_path)