Top view model part two modified

Backend/.ipynb_checkpoints/SF_Weed_identification_Finalized-checkpoint.ipynb

@@ -524,6 +524,60 @@
     "ensemble_model = Model(inputs = model_input , outputs = ensemble_output , name = 'ensemble')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#compile the ensemble model with adam optimizer + CategoricalCrossentropy as the loss function\n",
+    "ensemble_model.compile(optimizer='adam' , loss = tf.losses.CategoricalCrossentropy(from_logits=False), metrics =['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_loss_curve(history):\n",
+    "  '''\n",
+    "  Return separate loss curves for training and validation metrics\n",
+    "  '''\n",
+    "  loss = history.history[\"loss\"]\n",
+    "  val_loss = history.history[\"val_loss\"]\n",
+    "\n",
+    "  accuracy = history.history[\"accuracy\"]\n",
+    "  val_accuracy = history.history[\"val_accuracy\"]\n",
+    "\n",
+    "  #get the number of epochs that we run for\n",
+    "  epochs =  range(len(history.history[\"loss\"]))\n",
+    "\n",
+    "  #Plot the lost\n",
+    "  plt.plot(epochs , loss , label=\"Training Loss\")\n",
+    "  plt.plot(epochs , val_loss , label=\"Validation Loss\")\n",
+    "  plt.title(\"Loss\")\n",
+    "  plt.xlabel(\"Epochs\")\n",
+    "  plt.legend()\n",
+    "\n",
+    "  #Plot the accuracy\n",
+    "  plt.figure()\n",
+    "  plt.plot(epochs , accuracy , label=\"Training accuracy\")\n",
+    "  plt.plot(epochs , val_accuracy , label=\"Validation accuracy\")\n",
+    "  plt.title(\"accuracy\")\n",
+    "  plt.xlabel(\"Epochs\")\n",
+    "  plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_loss_curve(ensemble_hist)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

Backend/SF_Weed_identification_Finalized.ipynb

@@ -524,6 +524,60 @@
     "ensemble_model = Model(inputs = model_input , outputs = ensemble_output , name = 'ensemble')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#compile the ensemble model with adam optimizer + CategoricalCrossentropy as the loss function\n",
+    "ensemble_model.compile(optimizer='adam' , loss = tf.losses.CategoricalCrossentropy(from_logits=False), metrics =['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_loss_curve(history):\n",
+    "  '''\n",
+    "  Return separate loss curves for training and validation metrics\n",
+    "  '''\n",
+    "  loss = history.history[\"loss\"]\n",
+    "  val_loss = history.history[\"val_loss\"]\n",
+    "\n",
+    "  accuracy = history.history[\"accuracy\"]\n",
+    "  val_accuracy = history.history[\"val_accuracy\"]\n",
+    "\n",
+    "  #get the number of epochs that we run for\n",
+    "  epochs =  range(len(history.history[\"loss\"]))\n",
+    "\n",
+    "  #Plot the lost\n",
+    "  plt.plot(epochs , loss , label=\"Training Loss\")\n",
+    "  plt.plot(epochs , val_loss , label=\"Validation Loss\")\n",
+    "  plt.title(\"Loss\")\n",
+    "  plt.xlabel(\"Epochs\")\n",
+    "  plt.legend()\n",
+    "\n",
+    "  #Plot the accuracy\n",
+    "  plt.figure()\n",
+    "  plt.plot(epochs , accuracy , label=\"Training accuracy\")\n",
+    "  plt.plot(epochs , val_accuracy , label=\"Validation accuracy\")\n",
+    "  plt.title(\"accuracy\")\n",
+    "  plt.xlabel(\"Epochs\")\n",
+    "  plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_loss_curve(ensemble_hist)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,