Merge branch 'develop' into v1_api

.copyright.hook

@@ -90,7 +90,7 @@ def main(argv=None):
     retv = 0
     for filename in args.filenames:
         first_line = io.open(filename).readline()
-        if "Copyright" in first_line: continue
+        if "COPYRIGHT" in first_line.upper() : continue
         original_contents = io.open(filename).read()
         new_contents = generate_copyright(
             COPYRIGHT, lang_type(filename)) + original_contents

adversarial/advbox/attacks/gradientsign.py

@@ -49,3 +49,39 @@ class GradientSignAttack(Attack):
 
 
 FGSM = GradientSignAttack
+
+
+class IteratorGradientSignAttack(Attack):
+    """
+    This attack was originally implemented by Alexey Kurakin(Google Brain).
+    Paper link: https://arxiv.org/pdf/1607.02533.pdf
+    """
+
+    def _apply(self, image_label, epsilons=100, steps=10):
+        """
+        Apply the iterative gradient sign attack.
+        Args:
+            image_label(list): The image and label tuple list of one element.
+            epsilons(list|tuple|int): The epsilon (input variation parameter).
+            steps(int): The number of iterator steps.
+        Return:
+            numpy.ndarray: The adversarail sample generated by the algorithm.
+        """
+        assert len(image_label) == 1
+        pre_label = np.argmax(self.model.predict(image_label))
+        gradient = self.model.gradient(image_label)
+        min_, max_ = self.model.bounds()
+
+        if not isinstance(epsilons, Iterable):
+            epsilons = np.linspace(0, 1, num=epsilons + 1)
+
+        for epsilon in epsilons:
+            adv_img = image_label[0][0].reshape(gradient.shape)
+            for _ in range(steps):
+                gradient = self.model.gradient([(adv_img, image_label[0][1])])
+                gradient_sign = np.sign(gradient) * (max_ - min_)
+                adv_img = adv_img + epsilon * gradient_sign
+                adv_img = np.clip(adv_img, min_, max_)
+                adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
+                if pre_label != adv_label:
+                    return adv_img

benchmark/tensorflow/image/googlenet_multi_gpu.py

+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 from six.moves import xrange  # pylint: disable=redefined-builtin
 from datetime import datetime
 import math

doc/getstarted/concepts/src/infer.py

+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 import paddle.v2 as paddle
 import numpy as np
 

paddle/operators/op_documentation/batch_norm_op.md

@@ -66,7 +66,7 @@ As most C++ operators do, `batch_norm_op` is defined by inputs, outputs, attribu
 
 The following graph showes the training computational process of `batch_norm_op`:
 
-<img src="./images/batch_norm_op_kernel.png" width="800"/>
+<img src="../images/batch_norm_op_kernel.png" width="800"/>
 
 cudnn provides APIs to finish the whole series of computation, we can use them in our GPU kernel.
 
@@ -124,7 +124,7 @@ for pass_id in range(PASS_NUM):
 `is_infer` is an attribute. Once an operator is created, its attributes can not be changed. It suggests us that we shall maintain two `batch_norm_op` in the model, one's `is_infer` is `True`(we call it `infer_batch_norm_op`) and the other one's is `False`(we call it `train_batch_norm_op`). They share all parameters and variables, but be placed in two different branches. That is to say, if a network contains a `batch_norm_op`, it will fork into two branches, one go through `train_batch_norm_op` and the other one go through `infer_batch_norm_op`:
 
 <div align=center>
-<img src="./images/batch_norm_fork.png" width="500"/>
+<img src="../images/batch_norm_fork.png" width="500"/>
 </div>
 
 Just like what is shown in the above graph, the net forks before `batch_norm_op` and will never merge again. All the operators after `batch_norm_op` will duplicate. 

python/paddle/v2/fluid/tests/test_dynrnn_static_input.py

+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 import unittest
 import paddle.v2 as paddle
 import paddle.v2.fluid.core as core