ensemble model part one added

Backend/.ipynb_checkpoints/SF_Weed_identification_Finalized-checkpoint.ipynb

@@ -460,6 +460,69 @@
    "source": [
     "efficientNet_model_top.save('/content/drive/MyDrive/RP_SmartFarmer/Sandhini Gamage - Weed identification /private/saved_models/Weed_top_view2.h5')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "🟩 03 -Combining Two Models - Ensemble Approach"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Getting a copy of the  saved model to colab\n",
+    "import shutil \n",
+    "\n",
+    "shutil.copy('/content/drive/MyDrive/RP_SmartFarmer/Sandhini Gamage - Weed identification /private/saved_models/Weed_top_view2.h5' , '/content')\n",
+    "shutil.copy('/content/drive/MyDrive/RP_SmartFarmer/Sandhini Gamage - Weed identification /private/saved_models/Weed_side_view2.h5', '/content')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_all_models():\n",
+    "    all_models = []\n",
+    "    model_names = ['Weed_top_view2.h5', 'Weed_side_view2.h5']\n",
+    "    for model_name in model_names:\n",
+    "        filename = os.path.join('/content', model_name)\n",
+    "        model = tf.keras.models.load_model(filename ,  custom_objects={'KerasLayer':hub.KerasLayer})\n",
+    "        all_models.append(model)\n",
+    "        print('loaded:', filename)\n",
+    "    return all_models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = load_all_models()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Model , load_model\n",
+    "from tensorflow.keras.layers import Input, Average\n",
+    "\n",
+    "model_input = Input(shape = (224, 224, 3))\n",
+    "model_outputs = [model(model_input) for model in models]\n",
+    "ensemble_output = Average()(model_outputs)\n",
+    "ensemble_model = Model(inputs = model_input , outputs = ensemble_output , name = 'ensemble')"
+   ]
   }
  ],
  "metadata": {

Backend/SF_Weed_identification_Finalized.ipynb

@@ -460,6 +460,69 @@
    "source": [
     "efficientNet_model_top.save('/content/drive/MyDrive/RP_SmartFarmer/Sandhini Gamage - Weed identification /private/saved_models/Weed_top_view2.h5')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "🟩 03 -Combining Two Models - Ensemble Approach"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Getting a copy of the  saved model to colab\n",
+    "import shutil \n",
+    "\n",
+    "shutil.copy('/content/drive/MyDrive/RP_SmartFarmer/Sandhini Gamage - Weed identification /private/saved_models/Weed_top_view2.h5' , '/content')\n",
+    "shutil.copy('/content/drive/MyDrive/RP_SmartFarmer/Sandhini Gamage - Weed identification /private/saved_models/Weed_side_view2.h5', '/content')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_all_models():\n",
+    "    all_models = []\n",
+    "    model_names = ['Weed_top_view2.h5', 'Weed_side_view2.h5']\n",
+    "    for model_name in model_names:\n",
+    "        filename = os.path.join('/content', model_name)\n",
+    "        model = tf.keras.models.load_model(filename ,  custom_objects={'KerasLayer':hub.KerasLayer})\n",
+    "        all_models.append(model)\n",
+    "        print('loaded:', filename)\n",
+    "    return all_models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = load_all_models()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Model , load_model\n",
+    "from tensorflow.keras.layers import Input, Average\n",
+    "\n",
+    "model_input = Input(shape = (224, 224, 3))\n",
+    "model_outputs = [model(model_input) for model in models]\n",
+    "ensemble_output = Average()(model_outputs)\n",
+    "ensemble_model = Model(inputs = model_input , outputs = ensemble_output , name = 'ensemble')"
+   ]
   }
  ],
  "metadata": {