update a cifar config

example_cifar10.py

@@ -18,7 +18,7 @@ from tensorpack.dataflow import *
 from tensorpack.dataflow import imgaug
 
 """
-CIFAR10 89% test accuracy after 60k step (about 150 epochs)
+CIFAR10 90% validation accuracy after 100k step, 91% after 160k step.
 """
 
 BATCH_SIZE = 128
@@ -91,6 +91,7 @@ class Model(ModelDesc):
         return tf.add_n([cost, wd_cost], name='cost')
 
 def get_config():
+    #anchors = np.mgrid[0:4,0:4][:,1:,1:].transpose(1,2,0).reshape((-1,2)) / 4.0
     # prepare dataset
     dataset_train = dataset.Cifar10('train')
     augmentors = [
@@ -98,14 +99,15 @@ def get_config():
         imgaug.Flip(horiz=True),
         imgaug.BrightnessAdd(63),
         imgaug.Contrast((0.2,1.8)),
-        #imgaug.GaussianDeform([(0.2, 0.2), (0.2, 0.8), (0.8,0.8), (0.8,0.2)],
-                             #(30,30), 0.2, 3),
+        imgaug.GaussianDeform(
+            [(0.2, 0.2), (0.2, 0.8), (0.8,0.8), (0.8,0.2)],
+            (30,30), 0.2, 3),
         imgaug.MeanVarianceNormalize(all_channel=True)
     ]
     dataset_train = AugmentImageComponent(dataset_train, augmentors)
     dataset_train = BatchData(dataset_train, 128)
-    #dataset_train = PrefetchData(dataset_train, 3, 2)
-    step_per_epoch = dataset_train.size()
+    dataset_train = PrefetchData(dataset_train, 3, 2)
+    step_per_epoch = dataset_train.size() / 2
 
     augmentors = [
         imgaug.CenterCrop((30, 30)),
@@ -121,8 +123,8 @@ def get_config():
     lr = tf.train.exponential_decay(
         learning_rate=1e-2,
         global_step=get_global_step_var(),
-        decay_steps=dataset_train.size() * 40,
-        decay_rate=0.4, staircase=True, name='learning_rate')
+        decay_steps=dataset_train.size() * 30,
+        decay_rate=0.7, staircase=True, name='learning_rate')
     tf.scalar_summary('learning_rate', lr)
 
     return TrainConfig(
@@ -136,7 +138,7 @@ def get_config():
         session_config=sess_config,
         model=Model(),
         step_per_epoch=step_per_epoch,
-        max_epoch=300,
+        max_epoch=500,
     )
 
 if __name__ == '__main__':

tensorpack/utils/summary.py

@@ -45,7 +45,7 @@ def add_param_summary(regex):
             if p.get_shape().ndims == 0:
                 tf.scalar_summary(name, p)
             else:
-                tf.scalar_summary(name + '/sparsity', tf.nn.zero_fraction(p))
+                #tf.scalar_summary(name + '/sparsity', tf.nn.zero_fraction(p))
                 tf.histogram_summary(name, p)
 
 def summary_moving_average(cost_var):