use tf.keras for both keras examples

examples/mnist-keras.py

@@ -10,10 +10,8 @@ import os
 import sys
 import argparse
 
-import keras
-import keras.layers as KL
-from keras.models import Sequential
-from keras import regularizers
+from tensorflow import keras
+KL = keras.layers
 
 """
 This is an mnist example demonstrating how to use Keras symbolic function inside tensorpack.
@@ -31,7 +29,7 @@ IMAGE_SIZE = 28
 
 @memoized        # this is necessary for sonnet/Keras to work under tensorpack
 def get_keras_model():
-    M = Sequential()
+    M = keras.models.Sequential()
     M.add(KL.Conv2D(32, 3, activation='relu', input_shape=[IMAGE_SIZE, IMAGE_SIZE, 1], padding='same'))
     M.add(KL.MaxPooling2D())
     M.add(KL.Conv2D(32, 3, activation='relu', padding='same'))
@@ -39,9 +37,9 @@ def get_keras_model():
     M.add(KL.MaxPooling2D())
     M.add(KL.Conv2D(32, 3, padding='same', activation='relu'))
     M.add(KL.Flatten())
-    M.add(KL.Dense(512, activation='relu', kernel_regularizer=regularizers.l2(1e-5)))
+    M.add(KL.Dense(512, activation='relu', kernel_regularizer=keras.regularizers.l2(1e-5)))
     M.add(KL.Dropout(0.5))
-    M.add(KL.Dense(10, activation=None, kernel_regularizer=regularizers.l2(1e-5)))
+    M.add(KL.Dense(10, activation=None, kernel_regularizer=keras.regularizers.l2(1e-5)))
     return M
 
 
@@ -67,7 +65,7 @@ class Model(ModelDesc):
 
         wd_cost = tf.add_n(M.losses, name='regularize_loss')    # this is how Keras manage regularizers
         self.cost = tf.add_n([wd_cost, cost], name='total_cost')
-        summary.add_moving_summary(self.cost)
+        summary.add_moving_summary(self.cost, wd_cost)
 
     def _get_optimizer(self):
         lr = tf.train.exponential_decay(