remove cached files

split_mnist.py

@@ -2,13 +2,19 @@ from keras.models import Model
 from keras.layers import Dense, Input, Conv2D, Flatten, MaxPooling2D
 from configuration import conf
 from utils.dataloader import Sequential_loader
-import numpy as np
 from utils.model_utils import mask_layer_by_task
 from utils.layers import Probability_CLF_Mul_by_task
 from utils.train_utils import train_with_task
 from utils.predict_utils import get_task_likelihood, get_test_acc
+from utils.utils import paa, mkdir
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+sns.set()
 
 PATH = './results/%s/' % conf.dataset_name
+mkdir(PATH)
 
 epochs = 10
 output_dim = 10
@@ -52,24 +58,9 @@ for task_idx in range(conf.num_tasks):
         test_acc.append(get_test_acc(model, learned_task=task_idx, data_loader=data_loader, test_on_whole_set=False))
     # Print the average test accuracy across all the tasks
     print('Learned %dth Task, Average test accuracy on all the task : %.3f' % (
-    task_idx, get_test_acc(model, learned_task=task_idx, data_loader=data_loader, test_on_whole_set=True)))
-
-
-def paa(sample, w=None):
-    w = sample.shape[0] // 20 if w is None else w
-    l = len(sample)
-    stepfloat = l / w
-    step = int(np.ceil(stepfloat))
-    start = 0
-    j = 1
-    paa = []
-    while start <= (l - step):
-        section = sample[start:start + step]
-        paa.append(np.mean(section))
-        start = int(j * stepfloat)
-        j += 1
-    return paa
+        task_idx, get_test_acc(model, learned_task=task_idx, data_loader=data_loader, test_on_whole_set=True)))
 
+# Plot the result
 
 tlh_s = []
 for i in tlh:
@@ -80,14 +71,8 @@ tlh_std_s = []
 for i in tlh_std:
     tlh_std_s += i.tolist()
 tlh_std_s = np.array(tlh_std_s)
-
 test_acc_s = np.array(test_acc).reshape(-1)
 
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-sns.set()
-
 tlh = np.array(paa(tlh_s))
 tlh_std = np.array(paa(tlh_std_s))
 test_acc = np.array(paa(test_acc_s, tlh.shape[0]))
@@ -100,7 +85,6 @@ for i in a:
     fig.fill_between(np.arange(i, i + 10 + 1), 0, 0.1, alpha=0.1, color='red')
     fig.fill_between(np.arange(i - 10, i + 1), 0, 0.1, alpha=0.1, color='green')
 fig.fill_between(np.arange(90 - 10, 90), 0, 0.1, alpha=0.1, color='green')
-# a = fig.get_xticklabels()
 fig.set_xticklabels(['', 'Task 1', 'Task 2', 'Task 3', 'Task 4', 'Task 5'])
 plt.legend(loc='center right')
 plt.savefig(PATH + 'result')

utils/utils.py

 import os
+import numpy as np
 
 def mkdir(path):
     """ Create a directory if there isn't one already. """
     try:
         os.mkdir(path)
     except OSError:
-        pass
+        pass
\ No newline at end of file
+
+def paa(sample, w=None):
+    w = sample.shape[0] // 20 if w is None else w
+    l = len(sample)
+    stepfloat = l / w
+    step = int(np.ceil(stepfloat))
+    start = 0
+    j = 1
+    paa = []
+    while start <= (l - step):
+        section = sample[start:start + step]
+        paa.append(np.mean(section))
+        start = int(j * stepfloat)
+        j += 1
+    return paa
\ No newline at end of file