Modification of weights

CNN model to Recommend Comperhancive DDIs/.ipynb_checkpoints/P04_select and evaluate CNN on triple DDI(-1,+1)-checkpoint.ipynb

@@ -214,7 +214,7 @@
     "model.add(Dropout(0.4))\n",
     "model.add(Dense( 16, activation='relu'))\n",
     "model.add(Dense(2, activation='sigmoid'))\n",
-    "# model.add(Softmax(128))\n",
+    "# model.add(Softmax())\n",
     "model.summary()\n",
     "\n",
     "#compile model using accuracy to measure model performance\n",
@@ -301,7 +301,7 @@
    "outputs": [],
    "source": [
     "### Saveing the Model\n",
-    "model.save_weights('cnnSelection42702(1and-1)_15_epoch.h5')"
+    "model.save_weights('Weight/cnnSelection42702(1and-1)_15_epoch.h5')"
    ]
   },
   {
@@ -352,81 +352,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'loss': [0.3907131850719452,\n",
-       "  0.2690473198890686,\n",
-       "  0.21946105360984802,\n",
-       "  0.18230456113815308,\n",
-       "  0.15816053748130798,\n",
-       "  0.13941122591495514,\n",
-       "  0.12324169278144836,\n",
-       "  0.10643041133880615,\n",
-       "  0.09168621152639389,\n",
-       "  0.08443722128868103,\n",
-       "  0.07511654496192932,\n",
-       "  0.06909380108118057,\n",
-       "  0.06320834904909134,\n",
-       "  0.05833422392606735,\n",
-       "  0.05364372208714485],\n",
-       " 'accuracy': [0.826576828956604,\n",
-       "  0.8799437880516052,\n",
-       "  0.9090341329574585,\n",
-       "  0.9259210824966431,\n",
-       "  0.9377862215042114,\n",
-       "  0.9454881548881531,\n",
-       "  0.9512645602226257,\n",
-       "  0.958628237247467,\n",
-       "  0.9645087718963623,\n",
-       "  0.9678653478622437,\n",
-       "  0.971221923828125,\n",
-       "  0.973849892616272,\n",
-       "  0.9760095477104187,\n",
-       "  0.978351354598999,\n",
-       "  0.9791319966316223],\n",
-       " 'val_loss': [0.30254605412483215,\n",
-       "  0.2570132911205292,\n",
-       "  0.20715923607349396,\n",
-       "  0.19037070870399475,\n",
-       "  0.17407278716564178,\n",
-       "  0.164819598197937,\n",
-       "  0.15946325659751892,\n",
-       "  0.13270674645900726,\n",
-       "  0.12475927919149399,\n",
-       "  0.13908205926418304,\n",
-       "  0.10399913042783737,\n",
-       "  0.12310915440320969,\n",
-       "  0.11184985935688019,\n",
-       "  0.1205950602889061,\n",
-       "  0.10268103331327438],\n",
-       " 'val_accuracy': [0.864168643951416,\n",
-       "  0.8927400708198547,\n",
-       "  0.9177985787391663,\n",
-       "  0.9241217970848083,\n",
-       "  0.933021068572998,\n",
-       "  0.9402810335159302,\n",
-       "  0.9454332590103149,\n",
-       "  0.9501171112060547,\n",
-       "  0.9573770761489868,\n",
-       "  0.9548009634017944,\n",
-       "  0.9613583087921143,\n",
-       "  0.9599531888961792,\n",
-       "  0.9641686081886292,\n",
-       "  0.9646369814872742,\n",
-       "  0.965573787689209]}"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "history.history"
+    "# history.history"
    ]
   },
   {
@@ -957,13 +887,6 @@
     "plt.ylabel('frequency distribution')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,