use tf.keras rather than keras

examples/mnist-keras-v2.py

@@ -6,9 +6,8 @@
 import numpy as np
 import tensorflow as tf
 
-from keras.models import Sequential
-import keras.layers as KL
-from keras import regularizers
+from tensorflow import keras
+KL = keras.layers
 
 
 from tensorpack.train import SimpleTrainer
@@ -35,7 +34,7 @@ def get_data():
 
 if __name__ == '__main__':
     logger.auto_set_dir()
-    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'))
@@ -43,9 +42,9 @@ if __name__ == '__main__':
     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)))
     M.add(KL.Activation('softmax'))
 
     dataset_train, dataset_test = get_data()

tensorpack/contrib/keras.py

@@ -4,7 +4,7 @@
 
 import tensorflow as tf
 from six.moves import zip
-import keras
+from tensorflow import keras
 
 from ..graph_builder import InputDesc
 from ..tfutils.tower import get_current_tower_context