Correct text typos and fix NTM's addressing bugs.

mt_with_external_memory/README.md

@@ -99,5 +99,5 @@ TBD
 ## References
 
 1. Alex Graves, Greg Wayne, Ivo Danihelka, [Neural Turing Machines](https://arxiv.org/abs/1410.5401). arXiv preprint arXiv:1410.5401, 2014.
-2. Mingxuan Wang, Zhengdong Lu, Hang Li, Qun Liu，[Memory-enhanced Decoder Neural Machine Translation](https://arxiv.org/abs/1606.02003). arXiv preprint arXiv:1606.02003, 2016.
-3. Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473). arXiv preprint arXiv:1409.0473, 2014.
+2. Mingxuan Wang, Zhengdong Lu, Hang Li, Qun Liu, [Memory-enhanced Decoder Neural Machine Translation](https://arxiv.org/abs/1606.02003). arXiv preprint arXiv:1606.02003, 2016.
+3. Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473). arXiv preprint arXiv:1409.0473, 2014.

mt_with_external_memory/mt_with_external_memory.py

@@ -1,18 +1,5 @@
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# 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.
 """
-    This python script is a example model configuration for neural machine
+    This python script is an example model configuration for neural machine
     translation with external memory, based on PaddlePaddle V2 APIs.
 
     The "external memory" refers to two types of memories.
@@ -21,7 +8,7 @@
     Both types of external memories are exploited to enhance the vanilla
     Seq2Seq neural machine translation.
 
-    The implementation largely followers the paper
+    The implementation primarily follows the paper
     `Memory-enhanced Decoder for Neural Machine Translation
     <https://arxiv.org/abs/1606.02003>`_,
     with some minor differences (will be listed in README.md).
@@ -39,7 +26,7 @@
 hidden_size = 1024
 batch_size = 5
 memory_slot_num = 8
-beam_size = 40
+beam_size = 3
 infer_data_num = 3
 
 
@@ -67,24 +54,40 @@ class ExternalMemory(object):
     :type name: basestring
     :param mem_slot_size: Size of memory slot/vector.
     :type mem_slot_size: int
-    :param boot_layer: Boot layer for initializing memory. Sequence layer
-                       with sequence length indicating the number of memory
-                       slots, and size as mem_slot_size.
+    :param boot_layer: Boot layer for initializing the external memory. The
+                       sequence layer has sequence length indicating the number
+                       of memory slots, and size as memory slot size.
     :type boot_layer: LayerOutput
     :param readonly: If true, the memory is read-only, and write function cannot
                      be called. Default is false.
     :type readonly: bool
+    :param enable_interpolation: If set true, the read/write addressing weights
+                                 will be interpolated with the weights in the
+                                 last step, with the affine coefficients being
+                                 a learnable gate function.
+    :type enable_interpolation: bool
     """
 
-    def __init__(self, name, mem_slot_size, boot_layer, readonly=False):
+    def __init__(self,
+                 name,
+                 mem_slot_size,
+                 boot_layer,
+                 readonly=False,
+                 enable_interpolation=True):
         self.name = name
         self.mem_slot_size = mem_slot_size
         self.readonly = readonly
+        self.enable_interpolation = enable_interpolation
         self.external_memory = paddle.layer.memory(
             name=self.name,
             size=self.mem_slot_size,
             is_seq=True,
             boot_layer=boot_layer)
+        # prepare a constant (zero) intializer for addressing weights 
+        self.zero_addressing_init = paddle.layer.slope_intercept(
+            input=paddle.layer.fc(input=boot_layer, size=1),
+            slope=0.0,
+            intercept=0.0)
         # set memory to constant when readonly=True
         if self.readonly:
             self.updated_external_memory = paddle.layer.mixed(
@@ -111,18 +114,18 @@ def __content_addressing__(self, key_vector):
             size=self.mem_slot_size,
             act=paddle.activation.Linear(),
             bias_attr=False)
-        merged = paddle.layer.addto(
+        merged_projection = paddle.layer.addto(
             input=[key_proj_expanded, memory_projection],
             act=paddle.activation.Tanh())
         # softmax addressing weight: w=softmax(v^T a)
         addressing_weight = paddle.layer.fc(
-            input=merged,
+            input=merged_projection,
             size=1,
             act=paddle.activation.SequenceSoftmax(),
             bias_attr=False)
         return addressing_weight
 
-    def __interpolation__(self, key_vector, addressing_weight):
+    def __interpolation__(self, head_name, key_vector, addressing_weight):
         """
         Interpolate between previous and current addressing weights.
         """
@@ -134,34 +137,33 @@ def __interpolation__(self, key_vector, addressing_weight):
             bias_attr=False)
         # interpolation: w_t = g*w_t+(1-g)*w_{t-1}
         last_addressing_weight = paddle.layer.memory(
-            name=self.name + "_addressing_weight", size=1, is_seq=True)
-        gated_addressing_weight = paddle.layer.addto(
-            name=self.name + "_addressing_weight",
-            input=[
-                last_addressing_weight,
-                paddle.layer.scaling(weight=gate, input=addressing_weight),
-                paddle.layer.mixed(
-                    input=paddle.layer.dotmul_operator(
-                        a=gate, b=last_addressing_weight, scale=-1.0),
-                    size=1)
-            ],
-            act=paddle.activation.Tanh())
-        return gated_addressing_weight
-
-    def __get_addressing_weight__(self, key_vector):
+            name=self.name + "_addressing_weight_" + head_name,
+            size=1,
+            is_seq=True,
+            boot_layer=self.zero_addressing_init)
+        interpolated_weight = paddle.layer.interpolation(
+            name=self.name + "_addressing_weight_" + head_name,
+            input=[addressing_weight, addressing_weight],
+            weight=paddle.layer.expand(input=gate, expand_as=addressing_weight))
+        return interpolated_weight
+
+    def __get_addressing_weight__(self, head_name, key_vector):
         """
         Get final addressing weights for read/write heads, including content
         addressing and interpolation.
         """
         # current content-based addressing
         addressing_weight = self.__content_addressing__(key_vector)
-        return addressing_weight
         # interpolation with previous addresing weight
-        return self.__interpolation__(key_vector, addressing_weight)
+        if self.enable_interpolation:
+            return self.__interpolation__(head_name, key_vector,
+                                          addressing_weight)
+        else:
+            return addressing_weight
 
     def write(self, write_key):
         """
-        Write head for external memory.
+        Write onto the external memory.
         It cannot be called if "readonly" set True.
 
         :param write_key: Key vector for write heads to generate writing
@@ -172,7 +174,7 @@ def write(self, write_key):
         if self.readonly:
             raise ValueError("ExternalMemory with readonly=True cannot write.")
         # get addressing weight for write head
-        write_weight = self.__get_addressing_weight__(write_key)
+        write_weight = self.__get_addressing_weight__("write_head", write_key)
         # prepare add_vector and erase_vector
         erase_vector = paddle.layer.fc(
             input=write_key,
@@ -205,7 +207,7 @@ def write(self, write_key):
 
     def read(self, read_key):
         """
-        Read head for external memory.
+        Read from the external memory.
 
         :param write_key: Key vector for read head to generate addressing
                           signals.
@@ -214,7 +216,7 @@ def read(self, read_key):
         :rtype: LayerOutput
         """
         # get addressing weight for write head
-        read_weight = self.__get_addressing_weight__(read_key)
+        read_weight = self.__get_addressing_weight__("read_head", read_key)
         # read content from external memory
         scaled = paddle.layer.scaling(
             weight=read_weight, input=self.updated_external_memory)
@@ -227,19 +229,16 @@ def bidirectional_gru_encoder(input, size, word_vec_dim):
     Bidirectional GRU encoder.
     """
     # token embedding
-    embeddings = paddle.layer.embedding(
-        input=input,
-        size=word_vec_dim,
-        param_attr=paddle.attr.ParamAttr(name='_encoder_word_embedding'))
+    embeddings = paddle.layer.embedding(input=input, size=word_vec_dim)
     # token-level forward and backard encoding for attentions
     forward = paddle.networks.simple_gru(
         input=embeddings, size=size, reverse=False)
     backward = paddle.networks.simple_gru(
         input=embeddings, size=size, reverse=True)
-    merged = paddle.layer.concat(input=[forward, backward])
+    forward_backward = paddle.layer.concat(input=[forward, backward])
     # sequence-level encoding
     backward_first = paddle.layer.first_seq(input=backward)
-    return merged, backward_first
+    return forward_backward, backward_first
 
 
 def memory_enhanced_decoder(input, target, initial_state, source_context, size,
@@ -256,9 +255,9 @@ def memory_enhanced_decoder(input, target, initial_state, source_context, size,
     The vanilla RNN/LSTM/GRU also has a narrow memory mechanism, namely the
     hidden state vector (or cell state in LSTM) carrying information through
     a span of sequence time, which is a successful design enriching the model
-    with capability to "remember" things in the long run. However, such a vector
-    state is somewhat limited to a very narrow memory bandwidth. External memory
-    introduced here could easily increase the memory capacity with linear
+    with the capability to "remember" things in the long run. However, such a
+    vector state is somewhat limited to a very narrow memory bandwidth. External
+    memory introduced here could easily increase the memory capacity with linear
     complexity cost (rather than quadratic for vector state).
 
     This enhanced decoder expands its "memory passage" through two
@@ -268,7 +267,7 @@ def memory_enhanced_decoder(input, target, initial_state, source_context, size,
     - Unbounded memory for handling source language's token-wise information.
       Exactly the attention mechanism over Seq2Seq.
     
-    Notice that we take the attention mechanism as a special form of external
+    Notice that we take the attention mechanism as a particular form of external
     memory, with read-only memory bank initialized with encoder states, and a
     read head with content-based addressing (attention). From this view point,
     we arrive at a better understanding of attention mechanism itself and other
@@ -306,12 +305,14 @@ def recurrent_decoder_step(cur_embedding):
             name="bounded_memory",
             mem_slot_size=size,
             boot_layer=bounded_memory_init,
-            readonly=False)
+            readonly=False,
+            enable_interpolation=True)
         unbounded_memory = ExternalMemory(
             name="unbounded_memory",
             mem_slot_size=size * 2,
             boot_layer=unbounded_memory_init,
-            readonly=True)
+            readonly=True,
+            enable_interpolation=False)
         # write bounded memory
         bounded_memory.write(state)
         # read bounded memory
@@ -566,7 +567,7 @@ def infer():
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=8)
+    paddle.init(use_gpu=False, trainer_count=1)
     train(num_passes=1)
     infer()