Formatting code

fluid/adversarial/fluid_mnist.py

@@ -69,17 +69,18 @@ def main():
     for pass_id in range(PASS_NUM):
         pass_acc.reset()
         for data in train_reader():
-            loss, acc, b_size = exe.run(fluid.default_main_program(),
-                                        feed=feeder.feed(data),
-                                        fetch_list=[avg_cost, batch_acc, batch_size])
+            loss, acc, b_size = exe.run(
+                fluid.default_main_program(),
+                feed=feeder.feed(data),
+                fetch_list=[avg_cost, batch_acc, batch_size])
             pass_acc.add(value=acc, weight=b_size)
-            print("pass_id=" + str(pass_id) + " acc=" + str(acc[0]) + " pass_acc="
-                  + str(pass_acc.eval()[0]))
+            print("pass_id=" + str(pass_id) + " acc=" + str(acc[0]) +
+                  " pass_acc=" + str(pass_acc.eval()[0]))
             if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD:
                 break
 
-        print("pass_id=" + str(pass_id) +
-              " pass_acc=" + str(pass_acc.eval()[0]))
+        print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc.eval()[
+            0]))
     fluid.io.save_params(
         exe, dirname='./mnist', main_program=fluid.default_main_program())
     print('train mnist done')

fluid/image_classification/mobilenet.py

@@ -196,21 +196,24 @@ def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
     for pass_id in range(num_passes):
         train_pass_acc_evaluator.reset()
         for batch_id, data in enumerate(train_reader()):
-            loss, acc, size = exe.run(fluid.default_main_program(),
-                                      feed=feeder.feed(data),
-                                      fetch_list=[avg_cost, b_acc_var, b_size_var])
+            loss, acc, size = exe.run(
+                fluid.default_main_program(),
+                feed=feeder.feed(data),
+                fetch_list=[avg_cost, b_acc_var, b_size_var])
             train_pass_acc_evaluator.add(value=acc, weight=size)
             print("Pass {0}, batch {1}, loss {2}, acc {3}".format(
                 pass_id, batch_id, loss[0], acc[0]))
 
         test_pass_acc_evaluator.reset()
         for data in test_reader():
-            loss, acc, size = exe.run(inference_program,
-                                      feed=feeder.feed(data),
-                                      fetch_list=[avg_cost, b_acc_var, b_size_var])
+            loss, acc, size = exe.run(
+                inference_program,
+                feed=feeder.feed(data),
+                fetch_list=[avg_cost, b_acc_var, b_size_var])
             test_pass_acc_evaluator.add(value=acc, weight=size)
         print("End pass {0}, train_acc {1}, test_acc {2}".format(
-            pass_id, train_pass_acc_evaluator.eval(), test_pass_acc_evaluator.eval()))
+            pass_id,
+            train_pass_acc_evaluator.eval(), test_pass_acc_evaluator.eval()))
         if pass_id % 10 == 0:
             model_path = os.path.join(model_save_dir, str(pass_id))
             print 'save models to %s' % (model_path)

fluid/text_classification/train.py

@@ -150,7 +150,8 @@ def main(dict_path):
             train_pass_acc_evaluator.add(value=acc_val, weight=size_val)
             if batch_id and batch_id % conf.log_period == 0:
                 print("Pass id: %d, batch id: %d, cost: %f, pass_acc: %f" %
-                      (pass_id, batch_id, cost_val, train_pass_acc_evaluator.eval()))
+                      (pass_id, batch_id, cost_val,
+                       train_pass_acc_evaluator.eval()))
         end_time = time.time()
         total_time += (end_time - start_time)
         pass_test_acc = test(exe)