add sub projects

dssm/dssm_tractate_model.py

@@ -3,7 +3,7 @@ import os
 import numpy as np
 import pandas as pd
 import tensorflow as tf
-from tensorflow.keras import activations, layers, losses, metrics, optimizers
+from tensorflow.keras import (activations, callbacks, layers, losses, metrics, optimizers)
 
 base_dir = os.getcwd()
 # base_dir = "/Users/offic/work/GM/strategy_embedding/"  # TODO remove
@@ -524,8 +524,6 @@ def tractate_dssm_model():
     device_tractate_dot = tf.reduce_sum(device_vector * tractate_vector, axis=1)
     device_tractate_dot = tf.expand_dims(device_tractate_dot, 1)
 
-    output = layers.Dense(1, activation=activations.sigmoid)(device_tractate_dot)
-
     inputs = [
         device_id, device_fd, device_sd, device_fs, device_ss, device_fp, device_sp, device_p, device_fd2, device_sd2, device_fs2,
         device_ss2, device_fp2, device_sp2, device_p2, card_id, is_pure_author, is_have_pure_reply, is_have_reply, content_level,
@@ -546,6 +544,7 @@ def tractate_dssm_model():
         thirty_reply_pure_rate, sixty_reply_pure_rate, ninety_reply_pure_rate, history_reply_pure_rate, card_fd, card_sd, card_fs,
         card_ss, card_fp, card_sp, card_p, card_fd2, card_sd2, card_fs2, card_ss2, card_fp2, card_sp2, card_p2
     ]
+    output = layers.Dense(1, activation=activations.sigmoid)(device_tractate_dot)
 
     model = tf.keras.Model(inputs=inputs, outputs=[output])
     print(model.summary())
@@ -576,12 +575,10 @@ if __name__ == "__main__":
 
     history = model.fit(x=x_train,
                         y=y,
-                        batch_size=32,
+                        batch_size=320,
                         epochs=5,
                         verbose=1,
-                        callbacks=[
-                            tf.keras.callbacks.EarlyStopping(monitor="loss", patience=3),
-                        ])
+                        callbacks=[callbacks.EarlyStopping(monitor="loss", patience=10)])
 
     history_dict = history.history
     print(history_dict)

personas_vector/admin.py

+from django.contrib import admin
+
+# Register your models here.

personas_vector/apps.py

+from django.apps import AppConfig
+
+
+class PersonasVectorConfig(AppConfig):
+    name = 'personas_vector'

personas_vector/models.py

+from django.db import models
+
+# Create your models here.

personas_vector/tests.py

+from django.test import TestCase
+
+# Create your tests here.

personas_vector/views.py

+from django.shortcuts import render
+
+# Create your views here.

requirements.txt

@@ -30,5 +30,6 @@ pytispark==2.0
 
 tensorflow==2.3.1
 keras==2.4.3
+protobuf==3.13.0
 
 ipython

strategy_embedding/settings.py

@@ -38,7 +38,8 @@ INSTALLED_APPS = [
     "django_extensions",
     "face_similarity",
     "word_vector",
-    "dssm"
+    "dssm",
+    "personas_vector",
     "utils",
 ]