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22_23-J 65
22_23-J 65
Commit c85ca861 authored by Manukalpani G.S. IT19111698's avatar Manukalpani G.S. IT19111698
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Top view model part two modified

parent 78a3dad9
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Backend/.ipynb_checkpoints/SF_Weed_identification_Finalized-checkpoint.ipynb
View file @ c85ca861
......@@ -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
View file @ c85ca861
......@@ -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,
......
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