cont ...

notebooks/Example 14 - Deep Learning Model Prediction - Tensorflow Application.ipynb

@@ -49,8 +49,8 @@
    "source": [
     "class LinearRegressionImpl(object):\n",
     "    def __init__(self, **kwargs):\n",
-    "        self.learning_rate = kwargs.get('learning_rate', 0.01)\n",
-    "        self.training_epochs = kwargs.get('training_epochs', 10)\n",
+    "        self.learning_rate = kwargs.get('learning_rate', 0.1)\n",
+    "        self.training_epochs = kwargs.get('training_epochs', 100)\n",
     "        self.display_steps = kwargs.get('display_steps', None)\n",
     "        self.W = None\n",
     "        self.b = None\n",
@@ -175,7 +175,7 @@
    "outputs": [],
    "source": [
     "\n",
-    "kernal_feature = 'roe_q'\n",
+    "kernal_feature = 'ROE'\n",
     "regress_features = {kernal_feature: LAST(kernal_feature),\n",
     "                    kernal_feature + '_l1': SHIFT(kernal_feature, 1),\n",
     "                    kernal_feature + '_l2': SHIFT(kernal_feature, 2),\n",
@@ -317,8 +317,6 @@
     "rets1 = []\n",
     "rets2 = []\n",
     "\n",
-    "\n",
-    "\n",
     "for i, ref_date in enumerate(model_dates):\n",
     "    py_ref_date = dt.datetime.strptime(ref_date, '%Y-%m-%d')\n",
     "    industry_matrix = industry_total[industry_total.trade_date == ref_date]\n",
@@ -416,7 +414,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {