Updated model

BTP_MobileNet_Model.ipynb

@@ -5,7 +5,7 @@
     "colab": {
       "name": "BTP_MobileNet_Model.ipynb",
       "provenance": [],
-      "authorship_tag": "ABX9TyOA6casfH36cpL6wCD15M4m"
+      "authorship_tag": "ABX9TyPn5kCKDiXOrbygHv1RJLp/"
     },
     "kernelspec": {
       "name": "python3",
@@ -142,6 +142,60 @@
       ],
       "execution_count": null,
       "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CtKQnHUT1O1B",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# add custom Layers\n",
+        "x = MobileNet_model.output\n",
+        "x = GlobalAveragePooling2D()(x)\n",
+        "x = Dense(512, activation=\"relu\")(x)\n",
+        "Custom_Output = Dense(num_classes, activation='softmax')(x)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ld4qzmpx1XCO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "model = Model(inputs = MobileNet_model.input, outputs = Custom_Output)\n",
+        "        \n",
+        "# compile the model\n",
+        "model.compile(loss='categorical_crossentropy',\n",
+        "                    optimizer='adam',\n",
+        "                    metrics=['accuracy'])\n",
+        "\n",
+        "model.summary()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "C6Z6WQzh1fDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "history = model.fit(x_train, y_train,\n",
+        "                    batch_size=batch_size,\n",
+        "                    epochs=num_epochs,\n",
+        "                    verbose=1,\n",
+        "                    validation_split=0.1)"
+      ],
+      "execution_count": null,
+      "outputs": []
     }
   ]
 }