add 02.recognize_digits ce files

02.recognize_digits/.run_ce.sh

+#!/bin/bash
+#This file is only used for continuous evaluation.
+python train.py --enable_ce | python _ce.py
+ 

02.recognize_digits/_ce.py

+### This file is only used for continuous evaluation test!
+from __future__ import print_function
+from __future__ import division
+from __future__ import absolute_import
+import os
+import sys
+sys.path.append(os.environ['ceroot'])
+from kpi import CostKpi
+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]
+
+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
+
+def log_to_ce(log):
+    kpi_tracker = {}
+    for kpi in tracking_kpis:
+        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()
+
+if __name__ == '__main__':
+     log = sys.stdin.read()
+     log_to_ce(log)   
+

02.recognize_digits/train.py

@@ -15,14 +15,19 @@
 from __future__ import print_function
 
 import os
+import argparse
 from PIL import Image
 import numpy
 import paddle
 import paddle.fluid as fluid
 
-BATCH_SIZE = 64
-PASS_NUM = 5
-
+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()
+    return args
 
 def loss_net(hidden, label):
     prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
@@ -68,6 +73,20 @@ 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)
+        startup_program.random_seed = 90
+        main_program.random_seed = 90
+    else :
+        train_reader = paddle.batch(
+            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)
 
     img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
@@ -81,8 +100,7 @@ def train(nn_type,
 
     prediction, avg_loss, acc = net_conf(img, label)
 
-    test_program = fluid.default_main_program().clone(for_test=True)
-
+    test_program = main_program.clone(for_test=True)
     optimizer = fluid.optimizer.Adam(learning_rate=0.001)
     optimizer.minimize(avg_loss)
 
@@ -104,16 +122,9 @@ def train(nn_type,
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     exe = fluid.Executor(place)
-
-    train_reader = paddle.batch(
-        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)
+    
     feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
-
-    exe.run(fluid.default_startup_program())
-    main_program = fluid.default_main_program()
+    exe.run(startup_program)
     epochs = [epoch_id for epoch_id in range(PASS_NUM)]
 
     lists = []
@@ -143,12 +154,17 @@ 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\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,
@@ -210,7 +226,10 @@ def main(use_cuda, nn_type):
 
 
 if __name__ == '__main__':
-    use_cuda = False
+    args = parse_args()
+    BATCH_SIZE = 64
+    PASS_NUM = args.num_epochs
+    use_cuda = args.use_gpu
     # predict = 'softmax_regression' # uncomment for Softmax
     # predict = 'multilayer_perceptron' # uncomment for MLP
     predict = 'convolutional_neural_network'  # uncomment for LeNet5