v0.1.1

v0.1.1

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,26 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug(问题描述)**
+A clear and concise description of what the bug is.
+
+**To Reproduce(复现步骤)**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Operating environment(运行环境):**
+ - python version [e.g. 3.4, 3.6]
+ - tensorflow version [e.g. 1.4.0, 1.12.0]
+ - deepmatch version [e.g. 0.1.1,]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: enhancement&feature request
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/question.md

@@ -0,0 +1,20 @@
+---
+name: Question
+about: Ask any question ~
+title: ''
+labels: question
+assignees: ''
+
+---
+Please refer to the [FAQ](https://deepmatch.readthedocs.io/en/latest/FAQ.html) in doc and search for the [related issues](https://github.com/shenweichen/DeepCTR/issues) before you ask the question.
+
+**Describe the question(问题描述)**
+A clear and concise description of what the question is.
+
+**Additional context**
+Add any other context about the problem here.
+
+**Operating environment(运行环境):**
+ - python version [e.g. 3.6]
+ - tensorflow version [e.g. 1.4.0,]
+ - deepmatch version [e.g. 0.1.1,]

CONTRIBUTING.md

@@ -0,0 +1,10 @@
+This project is under development and we need developers to participate in.
+
+If you
+
+- familiar with and interested in matching algorithms
+- familiar with tensorflow 
+- have spare time to learn and develop
+- familiar with git
+
+please send a brief introduction of your background and experience to [email protected], welcome to join us!

README.md

@@ -12,7 +12,7 @@
 [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#disscussiongroup)
 [![License](https://img.shields.io/github/license/shenweichen/deepmatch.svg)](https://github.com/shenweichen/deepmatch/blob/master/LICENSE)
 
-DeepMatch is a deep matching model library for recommendations, advertising, and search. It's easy to **train models** and to **export representation vectors** for user and item which can be used for **ANN search**.You can use any complex model with `model.fit()`and `model.predict()` .
+DeepMatch is a deep matching model library for recommendations & advertising. It's easy to **train models** and to **export representation vectors** for user and item which can be used for **ANN search**.You can use any complex model with `model.fit()`and `model.predict()` .
 
 Let's [**Get Started!**](https://deepmatch.readthedocs.io/en/latest/Quick-Start.html)
 

deepmatch/__init__.py

@@ -1,4 +1,4 @@
 from .utils import check_version
 
-__version__ = '0.1.0'
+__version__ = '0.1.1'
 check_version(__version__)

deepmatch/layers/core.py

@@ -153,53 +153,51 @@ def compute_output_shape(self, input_shape):
 
 
 class CapsuleLayer(Layer):
-    def __init__(self, input_units, out_units, max_len, k_max, iteration=3,
-                 weight_initializer=RandomNormal(stddev=1.0), **kwargs):
-        self.input_units = input_units  # E1
-        self.out_units = out_units  # E2
+    def __init__(self, input_units, out_units, max_len, k_max, iteration_times=3,
+                 initializer=RandomNormal(stddev=1.0), **kwargs):
+        self.input_units = input_units
+        self.out_units = out_units
         self.max_len = max_len
         self.k_max = k_max
-        self.iteration = iteration
-        self.weight_initializer = weight_initializer
+        self.iteration_times = iteration_times
+        self.initializer = initializer
         super(CapsuleLayer, self).__init__(**kwargs)
 
     def build(self, input_shape):
-        self.B_matrix = self.add_weight(shape=[1, self.k_max, self.max_len], initializer=self.weight_initializer,
-                                        trainable=False, name="B", dtype=tf.float32)  # [1,K,H]
-        self.S_matrix = self.add_weight(shape=[self.input_units, self.out_units], initializer=self.weight_initializer,
-                                        name="S", dtype=tf.float32)
+        self.routing_logits = self.add_weight(shape=[1, self.k_max, self.max_len], initializer=self.initializer,
+                                              trainable=False, name="B", dtype=tf.float32)
+        self.bilinear_mapping_matrix = self.add_weight(shape=[self.input_units, self.out_units],
+                                                       initializer=self.initializer,
+                                                       name="S", dtype=tf.float32)
         super(CapsuleLayer, self).build(input_shape)
 
-    def call(self, inputs, **kwargs):  # seq_len:[B,1]
-        low_capsule, seq_len = inputs
-        B = tf.shape(low_capsule)[0]
-        seq_len_tile = tf.tile(seq_len, [1, self.k_max])  # [B,K]
-
-        for i in range(self.iteration):
-            mask = tf.sequence_mask(seq_len_tile, self.max_len)  # [B,K,H]
-            pad = tf.ones_like(mask, dtype=tf.float32) * (-2 ** 16 + 1)  # [B,K,H]
-            B_tile = tf.tile(self.B_matrix, [B, 1, 1])  # [B,K,H]
-            B_mask = tf.where(mask, B_tile, pad)
-            W = tf.nn.softmax(B_mask)  # [B,K,H]
-            low_capsule_new = tf.tensordot(low_capsule, self.S_matrix, axes=1)  # [B,H,E2]
-            high_capsule_tmp = tf.matmul(W, low_capsule_new)  # [B,K,E2]
-            high_capsule = squash(high_capsule_tmp)  # [B,K,E2]
-
-            # ([B,K,E2], [B,H,E2]->[B,E2,H])->[B,K,H]->[1,K,H]
-            B_delta = tf.reduce_sum(
-                tf.matmul(high_capsule, tf.transpose(low_capsule_new, perm=[0, 2, 1])),
+    def call(self, inputs, **kwargs):
+        behavior_embddings, seq_len = inputs
+        batch_size = tf.shape(behavior_embddings)[0]
+        seq_len_tile = tf.tile(seq_len, [1, self.k_max])
+
+        for i in range(self.iteration_times):
+            mask = tf.sequence_mask(seq_len_tile, self.max_len)
+            pad = tf.ones_like(mask, dtype=tf.float32) * (-2 ** 32 + 1)
+            routing_logits_with_padding = tf.where(mask, tf.tile(self.routing_logits, [batch_size, 1, 1]), pad)
+            weight = tf.nn.softmax(routing_logits_with_padding)
+            behavior_embdding_mapping = tf.tensordot(behavior_embddings, self.bilinear_mapping_matrix, axes=1)
+            Z = tf.matmul(weight, behavior_embdding_mapping)
+            interet_capsules = squash(Z)
+            delta_routing_logits = tf.reduce_sum(
+                tf.matmul(interet_capsules, tf.transpose(behavior_embdding_mapping, perm=[0, 2, 1])),
                 axis=0, keep_dims=True
-            )  # [1,K,H]
-            self.B_matrix.assign_add(B_delta)
-        high_capsule = tf.reshape(high_capsule, [-1, self.k_max, self.out_units])
-        return high_capsule
+            )
+            self.routing_logits.assign_add(delta_routing_logits)
+        interet_capsules = tf.reshape(interet_capsules, [-1, self.k_max, self.out_units])
+        return interet_capsules
 
     def compute_output_shape(self, input_shape):
         return (None, self.k_max, self.out_units)
 
 
 def squash(inputs):
     vec_squared_norm = tf.reduce_sum(tf.square(inputs), axis=-1, keep_dims=True)
-    scalar_factor = vec_squared_norm / (1 + vec_squared_norm) / tf.sqrt(vec_squared_norm + 1e-9)
-    vec_squashed = scalar_factor * inputs  # element-wise
+    scalar_factor = vec_squared_norm / (1 + vec_squared_norm) / tf.sqrt(vec_squared_norm + 1e-8)
+    vec_squashed = scalar_factor * inputs
     return vec_squashed

deepmatch/utils.py

@@ -20,7 +20,7 @@
 
 import tensorflow as tf
 from tensorflow.python.keras import backend as K
-from tensorflow.python.keras._impl.keras.layers import Lambda
+from tensorflow.python.keras.layers import Lambda
 
 def recall_N(y_true, y_pred, N=50):
     return len(set(y_pred[:N]) & set(y_true)) * 1.0 / len(y_true)

docs/source/conf.py

@@ -26,7 +26,7 @@
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = '0.1.0'
+release = '0.1.1'
 
 
 # -- General configuration ---------------------------------------------------

setup.py

@@ -9,7 +9,7 @@
 
 setuptools.setup(
     name="deepmatch",
-    version="0.1.0",
+    version="0.1.1",
     author="Weichen Shen",
     author_email="[email protected]",
     description="Deep matching model library for recommendations, advertising, and search. It's easy to train models and to **export representation vectors** for user and item which can be used for **ANN search**.",