modify ce file code style of 01.fit_a_line and 02.recognize_digits

01.fit_a_line/_ce.py

@@ -9,28 +9,31 @@ from kpi import CostKpi
 
 train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True, desc='train cost')
 test_cost_kpi = CostKpi('test_cost', 0.02, 0, actived=True, desc='test cost')
-tracking_kpis=[train_cost_kpi,test_cost_kpi]
+tracking_kpis = [train_cost_kpi, test_cost_kpi]
+
 
 def parse_log(log):
     for line in log.split('\n'):
         fs = line.strip().split('\t')
         print(fs)
-        if len(fs) == 3 and fs[0] =='kpis':
-           print("-----%s" % fs)
-           kpi_name = fs[1]
-           kpi_value = float(fs[2])
-           yield kpi_name,kpi_value
+        if len(fs) == 3 and fs[0] == 'kpis':
+            print("-----%s" % fs)
+            kpi_name = fs[1]
+            kpi_value = float(fs[2])
+            yield kpi_name, kpi_value
+
+
 def log_to_ce(log):
     kpi_tracker = {}
     for kpi in tracking_kpis:
-        kpi_tracker[kpi.name]=kpi
-    
+        kpi_tracker[kpi.name] = kpi
+
     for (kpi_name, kpi_value) in parse_log(log):
-         print(kpi_name,kpi_value)
-         kpi_tracker[kpi_name].add_record(kpi_value)
-         kpi_tracker[kpi_name].persist()
+        print(kpi_name, kpi_value)
+        kpi_tracker[kpi_name].add_record(kpi_value)
+        kpi_tracker[kpi_name].persist()
 
-if __name__ == '__main__':
-     log = sys.stdin.read()
-     log_to_ce(log)   
 
+if __name__ == '__main__':
+    log = sys.stdin.read()
+    log_to_ce(log)

01.fit_a_line/train.py

@@ -23,14 +23,24 @@ import numpy
 import paddle
 import paddle.fluid as fluid
 
+
 def parse_args():
     parser = argparse.ArgumentParser("fit_a_line")
-    parser.add_argument('--enable_ce', action='store_true', help="If set, run the task with continuous evaluation logs." )
-    parser.add_argument('--use_gpu', type=bool, default=False, help="Whether to use GPU or not." )
-    parser.add_argument('--num_epochs', type=int, default=100, help="number of epochs." )
+    parser.add_argument(
+        '--enable_ce',
+        action='store_true',
+        help="If set, run the task with continuous evaluation logs.")
+    parser.add_argument(
+        '--use_gpu',
+        type=bool,
+        default=False,
+        help="Whether to use GPU or not.")
+    parser.add_argument(
+        '--num_epochs', type=int, default=100, help="number of epochs.")
     args = parser.parse_args()
     return args
 
+
 # For training test cost
 def train_test(executor, program, reader, feeder, fetch_list):
     accumulated = 1 * [0]
@@ -62,14 +72,18 @@ def main():
     batch_size = 20
 
     if args.enable_ce:
-	train_reader = paddle.batch(paddle.dataset.uci_housing.train(), batch_size=batch_size)
-        test_reader = paddle.batch(paddle.dataset.uci_housing.test(), batch_size=batch_size)
-    else :
-	train_reader = paddle.batch(
-            paddle.reader.shuffle(paddle.dataset.uci_housing.train(), buf_size=500),
+        train_reader = paddle.batch(
+            paddle.dataset.uci_housing.train(), batch_size=batch_size)
+        test_reader = paddle.batch(
+            paddle.dataset.uci_housing.test(), batch_size=batch_size)
+    else:
+        train_reader = paddle.batch(
+            paddle.reader.shuffle(
+                paddle.dataset.uci_housing.train(), buf_size=500),
             batch_size=batch_size)
         test_reader = paddle.batch(
-            paddle.reader.shuffle(paddle.dataset.uci_housing.test(), buf_size=500),
+            paddle.reader.shuffle(
+                paddle.dataset.uci_housing.test(), buf_size=500),
             batch_size=batch_size)
 
     # feature vector of length 13
@@ -78,11 +92,11 @@ def main():
 
     main_program = fluid.default_main_program()
     startup_program = fluid.default_startup_program()
-    
+
     if args.enable_ce:
         main_program.random_seed = 90
-        startup_program.random_seed = 90 
-   
+        startup_program.random_seed = 90
+
     y_predict = fluid.layers.fc(input=x, size=1, act=None)
     cost = fluid.layers.square_error_cost(input=y_predict, label=y)
     avg_loss = fluid.layers.mean(cost)
@@ -140,12 +154,13 @@ def main():
                 sys.exit("got NaN loss, training failed.")
         if params_dirname is not None:
             # We can save the trained parameters for the inferences later
-            fluid.io.save_inference_model(params_dirname, ['x'], [y_predict], exe)
-  
+            fluid.io.save_inference_model(params_dirname, ['x'], [y_predict],
+                                          exe)
+
         if args.enable_ce and pass_id == args.num_epochs - 1:
-           print("kpis\ttrain_cost\t%f" % avg_loss_value[0])
-           print("kpis\ttest_cost\t%f" % test_metics[0])
-  
+            print("kpis\ttrain_cost\t%f" % avg_loss_value[0])
+            print("kpis\ttest_cost\t%f" % test_metics[0])
+
     infer_exe = fluid.Executor(place)
     inference_scope = fluid.core.Scope()
 
@@ -182,5 +197,5 @@ def main():
 
 
 if __name__ == '__main__':
-    args=parse_args()
+    args = parse_args()
     main()

02.recognize_digits/_ce.py

@@ -10,28 +10,30 @@ from kpi import AccKpi
 
 train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True, desc='train cost')
 test_cost_kpi = CostKpi('test_cost', 0.02, 0, actived=True, desc='test cost')
-test_acc_kpi= AccKpi('test_acc', 0.02, 0, actived=True, desc='test acc')
-tracking_kpis=[train_cost_kpi, test_cost_kpi, test_acc_kpi]
+test_acc_kpi = AccKpi('test_acc', 0.02, 0, actived=True, desc='test acc')
+tracking_kpis = [train_cost_kpi, test_cost_kpi, test_acc_kpi]
+
 
 def parse_log(log):
     for line in log.split('\n'):
         fs = line.strip().split('\t')
         print(fs)
-        if len(fs) == 3 and fs[0] =='kpis':
-           kpi_name = fs[1]
-           kpi_value = float(fs[2])
-           yield kpi_name,kpi_value
+        if len(fs) == 3 and fs[0] == 'kpis':
+            kpi_name = fs[1]
+            kpi_value = float(fs[2])
+            yield kpi_name, kpi_value
+
 
 def log_to_ce(log):
     kpi_tracker = {}
     for kpi in tracking_kpis:
-        kpi_tracker[kpi.name]=kpi
+        kpi_tracker[kpi.name] = kpi
     for (kpi_name, kpi_value) in parse_log(log):
-         print(kpi_name,kpi_value)
-         kpi_tracker[kpi_name].add_record(kpi_value)
-         kpi_tracker[kpi_name].persist()
+        print(kpi_name, kpi_value)
+        kpi_tracker[kpi_name].add_record(kpi_value)
+        kpi_tracker[kpi_name].persist()
 
-if __name__ == '__main__':
-     log = sys.stdin.read()
-     log_to_ce(log)   
 
+if __name__ == '__main__':
+    log = sys.stdin.read()
+    log_to_ce(log)

02.recognize_digits/train.py

@@ -21,14 +21,24 @@ import numpy
 import paddle
 import paddle.fluid as fluid
 
+
 def parse_args():
     parser = argparse.ArgumentParser("mnist")
-    parser.add_argument('--enable_ce', action='store_true', help="If set, run the task with continuous evaluation logs.")
-    parser.add_argument('--use_gpu', type=bool, default=False, help="Whether to use GPU or not.")
-    parser.add_argument('--num_epochs', type=int, default=5, help="number of epochs.")
-    args=parser.parse_args()
+    parser.add_argument(
+        '--enable_ce',
+        action='store_true',
+        help="If set, run the task with continuous evaluation logs.")
+    parser.add_argument(
+        '--use_gpu',
+        type=bool,
+        default=False,
+        help="Whether to use GPU or not.")
+    parser.add_argument(
+        '--num_epochs', type=int, default=5, help="number of epochs.")
+    args = parser.parse_args()
     return args
 
+
 def loss_net(hidden, label):
     prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
     loss = fluid.layers.cross_entropy(input=prediction, label=label)
@@ -73,18 +83,21 @@ def train(nn_type,
           params_filename=None):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
-   
+
     startup_program = fluid.default_startup_program()
     main_program = fluid.default_main_program()
-        
+
     if args.enable_ce:
-        train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=BATCH_SIZE)
-        test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
+        train_reader = paddle.batch(
+            paddle.dataset.mnist.train(), batch_size=BATCH_SIZE)
+        test_reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
         startup_program.random_seed = 90
         main_program.random_seed = 90
-    else :
+    else:
         train_reader = paddle.batch(
-            paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500), batch_size=BATCH_SIZE)
+            paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500),
+            batch_size=BATCH_SIZE)
         test_reader = paddle.batch(
             paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 
@@ -122,7 +135,7 @@ def train(nn_type,
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     exe = fluid.Executor(place)
-    
+
     feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
     exe.run(startup_program)
     epochs = [epoch_id for epoch_id in range(PASS_NUM)]
@@ -154,17 +167,17 @@ def train(nn_type,
                 exe,
                 model_filename=model_filename,
                 params_filename=params_filename)
-    
+
     if args.enable_ce:
-        print("kpis\ttrain_cost\t%f" % metrics[0] )
+        print("kpis\ttrain_cost\t%f" % metrics[0])
         print("kpis\ttest_cost\t%s" % avg_loss_val)
         print("kpis\ttest_acc\t%s" % acc_val)
-   
+
     # find the best pass
     best = sorted(lists, key=lambda list: float(list[1]))[0]
     print('Best pass is %s, testing Avgcost is %s' % (best[0], best[1]))
     print('The classification accuracy is %.2f%%' % (float(best[2]) * 100))
-   
+
 
 def infer(use_cuda,
           save_dirname=None,