"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y_val_pred = model.predict(x_val)\n",
"\n",
"plt.figure(figsize=(12,5))\n",
"for i in range(5):\n",
" plt.subplot(1,5,i+1)\n",
" plot_image_with_keypoints(x_val[i], y_val_pred[i])\n",
"plt.suptitle('Predicted facial keypoints (validation)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "979f32ae-2606-48e1-b991-9bd2d9ac1aa0",
"metadata": {},
"outputs": [],
"source": [
"final_model=tf.keras.models.clone_model(model)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "fbc249cf-67ba-4f10-82a9-f2e1d360e2e2",
"metadata": {},
"outputs": [],
"source": [
"final_model.compile(optimizer=tf.keras.optimizers.Adam(1e-4),loss=tf.keras.losses.MSE,metrics=[rmse, tf.keras.metrics.MAE])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "71b352e7-1e36-44e3-9ed5-78daef22dc59",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/94\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 7/67 [==>...........................] - ETA: 1s - loss: 0.2541 - rmse: 0.4625 - mean_absolute_error: 0.3746 "
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
"'+ptx85' is not a recognized feature for this target (ignoring feature)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"66/67 [============================>.] - ETA: 0s - loss: 0.1258 - rmse: 0.2941 - mean_absolute_error: 0.2355"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1766943644.713228 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"67/67 [==============================] - 3s 18ms/step - loss: 0.1253 - rmse: 0.2931 - mean_absolute_error: 0.2347\n",
"Epoch 2/94\n",
"11/67 [===>..........................] - ETA: 0s - loss: 0.0821 - rmse: 0.2197 - mean_absolute_error: 0.1740"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1766943644.914361 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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"W0000 00:00:1766943644.922761 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.931246 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.931650 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.931990 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.932408 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.933132 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.933628 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.934197 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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"W0000 00:00:1766943644.937979 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.939343 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.948742 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943644.967430 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"63/67 [===========================>..] - ETA: 0s - loss: 0.0755 - rmse: 0.2041 - mean_absolute_error: 0.1620"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1766943645.841340 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943645.841770 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943645.842114 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943645.842592 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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"W0000 00:00:1766943645.844677 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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"W0000 00:00:1766943646.035622 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"67/67 [==============================] - 1s 16ms/step - loss: 0.0753 - rmse: 0.2035 - mean_absolute_error: 0.1615\n",
"Epoch 3/94\n",
"10/67 [===>..........................] - ETA: 0s - loss: 0.0667 - rmse: 0.1821 - mean_absolute_error: 0.1447"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1766943646.044606 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.065244 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"67/67 [==============================] - 1s 14ms/step - loss: 0.0644 - rmse: 0.1758 - mean_absolute_error: 0.1392\n",
"Epoch 4/94\n",
" 9/67 [===>..........................] - ETA: 0s - loss: 0.0603 - rmse: 0.1644 - mean_absolute_error: 0.1296"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1766943646.947074 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.947579 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.948269 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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"W0000 00:00:1766943646.957660 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.958843 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.960136 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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"W0000 00:00:1766943646.962765 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.965808 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.966619 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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"W0000 00:00:1766943646.968909 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.969340 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.970239 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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"W0000 00:00:1766943646.973241 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.974428 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.975788 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.977069 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.978667 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.980201 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.981695 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.984686 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.985323 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.985950 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.987071 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.988173 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.988872 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.989528 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.990261 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.991097 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.992286 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.993940 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.994755 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943646.996018 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"67/67 [==============================] - 1s 13ms/step - loss: 0.0585 - rmse: 0.1594 - mean_absolute_error: 0.1260\n",
"Epoch 5/94\n",
" 9/67 [===>..........................] - ETA: 0s - loss: 0.0551 - rmse: 0.1487 - mean_absolute_error: 0.1167"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1766943647.856273 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766943647.856740 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"67/67 [==============================] - 1s 13ms/step - loss: 0.0541 - rmse: 0.1459 - mean_absolute_error: 0.1151\n",
"Epoch 6/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0513 - rmse: 0.1374 - mean_absolute_error: 0.1082\n",
"Epoch 7/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0486 - rmse: 0.1285 - mean_absolute_error: 0.1013\n",
"Epoch 8/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0465 - rmse: 0.1216 - mean_absolute_error: 0.0957\n",
"Epoch 9/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0450 - rmse: 0.1167 - mean_absolute_error: 0.0918\n",
"Epoch 10/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0432 - rmse: 0.1105 - mean_absolute_error: 0.0872\n",
"Epoch 11/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0420 - rmse: 0.1072 - mean_absolute_error: 0.0844\n",
"Epoch 12/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0407 - rmse: 0.1029 - mean_absolute_error: 0.0810\n",
"Epoch 13/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0396 - rmse: 0.0996 - mean_absolute_error: 0.0779\n",
"Epoch 14/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0386 - rmse: 0.0962 - mean_absolute_error: 0.0755\n",
"Epoch 15/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0375 - rmse: 0.0927 - mean_absolute_error: 0.0729\n",
"Epoch 16/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0366 - rmse: 0.0904 - mean_absolute_error: 0.0709\n",
"Epoch 17/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0357 - rmse: 0.0880 - mean_absolute_error: 0.0688\n",
"Epoch 18/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0349 - rmse: 0.0859 - mean_absolute_error: 0.0673\n",
"Epoch 19/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0340 - rmse: 0.0830 - mean_absolute_error: 0.0651\n",
"Epoch 20/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0330 - rmse: 0.0800 - mean_absolute_error: 0.0627\n",
"Epoch 21/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0324 - rmse: 0.0789 - mean_absolute_error: 0.0616\n",
"Epoch 22/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0315 - rmse: 0.0762 - mean_absolute_error: 0.0596\n",
"Epoch 23/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0307 - rmse: 0.0741 - mean_absolute_error: 0.0579\n",
"Epoch 24/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0300 - rmse: 0.0727 - mean_absolute_error: 0.0568\n",
"Epoch 25/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0294 - rmse: 0.0715 - mean_absolute_error: 0.0557\n",
"Epoch 26/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0287 - rmse: 0.0704 - mean_absolute_error: 0.0549\n",
"Epoch 27/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0280 - rmse: 0.0684 - mean_absolute_error: 0.0533\n",
"Epoch 28/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0276 - rmse: 0.0690 - mean_absolute_error: 0.0536\n",
"Epoch 29/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0268 - rmse: 0.0670 - mean_absolute_error: 0.0521\n",
"Epoch 30/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0262 - rmse: 0.0663 - mean_absolute_error: 0.0516\n",
"Epoch 31/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0254 - rmse: 0.0640 - mean_absolute_error: 0.0498\n",
"Epoch 32/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0250 - rmse: 0.0644 - mean_absolute_error: 0.0501\n",
"Epoch 33/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0244 - rmse: 0.0636 - mean_absolute_error: 0.0493\n",
"Epoch 34/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0237 - rmse: 0.0618 - mean_absolute_error: 0.0481\n",
"Epoch 35/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0232 - rmse: 0.0617 - mean_absolute_error: 0.0478\n",
"Epoch 36/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0227 - rmse: 0.0612 - mean_absolute_error: 0.0475\n",
"Epoch 37/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0220 - rmse: 0.0597 - mean_absolute_error: 0.0463\n",
"Epoch 38/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0216 - rmse: 0.0597 - mean_absolute_error: 0.0462\n",
"Epoch 39/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0210 - rmse: 0.0588 - mean_absolute_error: 0.0456\n",
"Epoch 40/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0204 - rmse: 0.0581 - mean_absolute_error: 0.0450\n",
"Epoch 41/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0199 - rmse: 0.0573 - mean_absolute_error: 0.0444\n",
"Epoch 42/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0193 - rmse: 0.0567 - mean_absolute_error: 0.0438\n",
"Epoch 43/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0189 - rmse: 0.0565 - mean_absolute_error: 0.0437\n",
"Epoch 44/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0183 - rmse: 0.0558 - mean_absolute_error: 0.0431\n",
"Epoch 45/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0179 - rmse: 0.0555 - mean_absolute_error: 0.0428\n",
"Epoch 46/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0174 - rmse: 0.0551 - mean_absolute_error: 0.0425\n",
"Epoch 47/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0169 - rmse: 0.0543 - mean_absolute_error: 0.0419\n",
"Epoch 48/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0164 - rmse: 0.0537 - mean_absolute_error: 0.0414\n",
"Epoch 49/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0160 - rmse: 0.0541 - mean_absolute_error: 0.0418\n",
"Epoch 50/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0155 - rmse: 0.0529 - mean_absolute_error: 0.0408\n",
"Epoch 51/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0151 - rmse: 0.0537 - mean_absolute_error: 0.0412\n",
"Epoch 52/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0146 - rmse: 0.0527 - mean_absolute_error: 0.0407\n",
"Epoch 53/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0142 - rmse: 0.0521 - mean_absolute_error: 0.0402\n",
"Epoch 54/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0137 - rmse: 0.0509 - mean_absolute_error: 0.0392\n",
"Epoch 55/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0133 - rmse: 0.0512 - mean_absolute_error: 0.0394\n",
"Epoch 56/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0130 - rmse: 0.0511 - mean_absolute_error: 0.0393\n",
"Epoch 57/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0125 - rmse: 0.0504 - mean_absolute_error: 0.0386\n",
"Epoch 58/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0122 - rmse: 0.0502 - mean_absolute_error: 0.0386\n",
"Epoch 59/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0117 - rmse: 0.0488 - mean_absolute_error: 0.0375\n",
"Epoch 60/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0115 - rmse: 0.0496 - mean_absolute_error: 0.0380\n",
"Epoch 61/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0110 - rmse: 0.0485 - mean_absolute_error: 0.0373\n",
"Epoch 62/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0107 - rmse: 0.0483 - mean_absolute_error: 0.0371\n",
"Epoch 63/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0103 - rmse: 0.0469 - mean_absolute_error: 0.0360\n",
"Epoch 64/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0100 - rmse: 0.0472 - mean_absolute_error: 0.0362\n",
"Epoch 65/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0097 - rmse: 0.0465 - mean_absolute_error: 0.0357\n",
"Epoch 66/94\n",
"67/67 [==============================] - 1s 12ms/step - loss: 0.0094 - rmse: 0.0471 - mean_absolute_error: 0.0360\n",
"Epoch 67/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0091 - rmse: 0.0468 - mean_absolute_error: 0.0358\n",
"Epoch 68/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0089 - rmse: 0.0469 - mean_absolute_error: 0.0360\n",
"Epoch 69/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0085 - rmse: 0.0455 - mean_absolute_error: 0.0349\n",
"Epoch 70/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0083 - rmse: 0.0460 - mean_absolute_error: 0.0352\n",
"Epoch 71/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0080 - rmse: 0.0455 - mean_absolute_error: 0.0348\n",
"Epoch 72/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0077 - rmse: 0.0444 - mean_absolute_error: 0.0341\n",
"Epoch 73/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0076 - rmse: 0.0455 - mean_absolute_error: 0.0348\n",
"Epoch 74/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0073 - rmse: 0.0445 - mean_absolute_error: 0.0342\n",
"Epoch 75/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0071 - rmse: 0.0440 - mean_absolute_error: 0.0336\n",
"Epoch 76/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0069 - rmse: 0.0442 - mean_absolute_error: 0.0337\n",
"Epoch 77/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0067 - rmse: 0.0439 - mean_absolute_error: 0.0335\n",
"Epoch 78/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0065 - rmse: 0.0437 - mean_absolute_error: 0.0334\n",
"Epoch 79/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0063 - rmse: 0.0434 - mean_absolute_error: 0.0330\n",
"Epoch 80/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0060 - rmse: 0.0424 - mean_absolute_error: 0.0324\n",
"Epoch 81/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0059 - rmse: 0.0429 - mean_absolute_error: 0.0328\n",
"Epoch 82/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0057 - rmse: 0.0421 - mean_absolute_error: 0.0322\n",
"Epoch 83/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0055 - rmse: 0.0416 - mean_absolute_error: 0.0317\n",
"Epoch 84/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0054 - rmse: 0.0419 - mean_absolute_error: 0.0319\n",
"Epoch 85/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0052 - rmse: 0.0418 - mean_absolute_error: 0.0320\n",
"Epoch 86/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0051 - rmse: 0.0411 - mean_absolute_error: 0.0314\n",
"Epoch 87/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0049 - rmse: 0.0412 - mean_absolute_error: 0.0314\n",
"Epoch 88/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0048 - rmse: 0.0407 - mean_absolute_error: 0.0311\n",
"Epoch 89/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0046 - rmse: 0.0401 - mean_absolute_error: 0.0306\n",
"Epoch 90/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0045 - rmse: 0.0401 - mean_absolute_error: 0.0305\n",
"Epoch 91/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0044 - rmse: 0.0399 - mean_absolute_error: 0.0305\n",
"Epoch 92/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0042 - rmse: 0.0392 - mean_absolute_error: 0.0300\n",
"Epoch 93/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0041 - rmse: 0.0395 - mean_absolute_error: 0.0302\n",
"Epoch 94/94\n",
"67/67 [==============================] - 1s 13ms/step - loss: 0.0041 - rmse: 0.0401 - mean_absolute_error: 0.0305\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_model.fit(x_c, y_c,epochs=94,batch_size=32,verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "054555d4-cd8d-4bbc-98be-a8b8ed253cb8",
"metadata": {},
"outputs": [],
"source": [
"final_model.save('final_keypoints_cnn.keras')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "c39a48b2-4b1e-455a-852d-a6f3e8574434",
"metadata": {},
"outputs": [],
"source": [
"with open('target_cols.json', 'w') as f:\n",
" json.dump(target_cols, f)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "e975b72c-4bcb-482f-9305-dc5a5a1fe85d",
"metadata": {},
"outputs": [],
"source": [
"preprocess_config={\n",
" 'img_size': [96, 96],\n",
" 'normalize': 'x / 255.0',\n",
" 'target_normalization': '(y - 48) / 48'\n",
"}\n",
"\n",
"with open('preprocess_config.json', 'w') as f:\n",
" json.dump(preprocess_config, f)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "fabf876b-66cd-444a-ba5e-78b3157054d0",
"metadata": {},
"outputs": [],
"source": [
"with open('history.pkl', 'wb') as f:\n",
" pickle.dump(history.history, f)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "ef48b0a3-29be-4c93-8172-f22e2504de66",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,4))\n",
"\n",
"# Loss\n",
"plt.subplot(1,2,1)\n",
"plt.plot(history.history['loss'], label='Train Loss')\n",
"plt.title('Training Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('MSE')\n",
"plt.legend()\n",
"\n",
"# RMSE\n",
"plt.subplot(1,2,2)\n",
"plt.plot(history.history['rmse'], label='Train RMSE')\n",
"plt.title('Training RMSE')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('RMSE')\n",
"plt.legend()\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3615f966-1bd7-4d9b-b4cb-bf84eabeaa13",
"metadata": {},
"source": [
"The training loss decreases steadily over epochs, showing stable and effective learning without signs of divergence.\n",
"\n",
"The training RMSE drops consistently and converges, indicating improving keypoint prediction accuracy over time."
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "0e2e1231-475c-4edd-b130-9a5f160808c8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 0s 245ms/step\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1766945038.163182 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.163783 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.163974 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.164265 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.164470 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.164999 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.165230 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.165453 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.165758 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.165970 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.166258 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.176744 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.182822 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.184053 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.184541 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.185031 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.185277 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.185688 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.185901 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.190644 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.191572 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.191869 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.192168 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.192512 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.192780 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.193315 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.197378 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.202119 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.237475 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.238002 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.238483 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.239125 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.239406 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.239857 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.240183 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.242075 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.242467 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.242721 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.242960 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.243520 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.243800 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.244187 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.260370 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.270381 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.287906 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.288666 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.290295 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.290745 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.290990 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.291560 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.291834 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.292182 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.292463 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.293058 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.293624 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.294079 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.294561 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.311989 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.315575 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945038.326483 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"idx = np.random.choice(len(x_c), 5, replace=False)\n",
"\n",
"pred = final_model.predict(x_c[idx])\n",
"pred = pred * 48 + 48\n",
"gt = y_c[idx] * 48 + 48\n",
"\n",
"plt.figure(figsize=(15,4))\n",
"for i in range(5):\n",
" plt.subplot(1,5,i+1)\n",
" plt.imshow(x_c[idx[i]].squeeze(), cmap='gray')\n",
"\n",
" plt.scatter(gt[i][0::2], gt[i][1::2], c='green', s=20, label='GT')\n",
" plt.scatter(pred[i][0::2], pred[i][1::2], c='red', s=20, label='Pred')\n",
" plt.axis('off')\n",
"\n",
"plt.suptitle('Ground Truth (green) vs Prediction (red)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "35ff3c92-d45d-4355-ae4e-7a095c5f25b2",
"metadata": {},
"source": [
"Predicted keypoints (red) closely align with the ground truth (green), demonstrating strong spatial accuracy across facial features.\n",
"\n",
"The model generalizes well to different faces and expressions, with most keypoints correctly localize"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "dfffdd4d-792f-44a2-9fff-d5160394664e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"errors = np.sqrt(np.mean((pred - gt)**2, axis=1))\n",
"\n",
"plt.figure(figsize=(6,4))\n",
"plt.hist(errors, bins=30)\n",
"plt.title('RMSE Distribution per Image')\n",
"plt.xlabel('RMSE')\n",
"plt.ylabel('Count')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "b25987f7-9bf7-4c37-a57d-0b49f3b189aa",
"metadata": {},
"source": [
"Most images have low RMSE values, while a small number of harder samples show higher errors."
]
},
{
"cell_type": "markdown",
"id": "824ac6a9-1e11-4536-9574-755fd0ea35f7",
"metadata": {},
"source": [
"### Final Model Inference on Test Data"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "bb672d29-eb78-4b42-b701-1b277f083cee",
"metadata": {},
"outputs": [],
"source": [
"submission = lookup_df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "0bf1cf78-9b6f-4aa5-8fef-ddcf790aef15",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 1783\n"
]
}
],
"source": [
"print(submission['ImageId'].min(), submission['ImageId'].max())"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "af3d3c32-512c-4713-ac02-7ebde524735f",
"metadata": {},
"outputs": [],
"source": [
"def parse_image(img_str):\n",
" pixels = np.fromstring(img_str, sep=' ',dtype=np.float32)\n",
" return pixels.reshape(96, 96, 1)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "04285546-0a2d-4ff2-97a5-5c27de20f155",
"metadata": {},
"outputs": [],
"source": [
"x_test=np.stack(test_df['Image'].apply(parse_image).values)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "4fadb546-9712-4a03-92ab-063d96d1b3da",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1783, 96, 96, 1)\n"
]
}
],
"source": [
"x_test=x_test / 255.0\n",
"print(x_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "e3684176-2d29-4c4f-a5ba-8c0d38b1a45f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"56/56 [==============================] - 1s 9ms/step\n",
"(1783, 30)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1766945733.552945 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.555958 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.556202 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.556589 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.556942 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.557548 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.558136 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.558772 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.559172 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.559521 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.560246 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.561045 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.565361 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.566378 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.566964 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.567699 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.568152 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.568993 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.569886 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.570815 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.571421 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.572325 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.573212 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.574368 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.575100 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.575910 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.576901 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.580505 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.581185 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.581642 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.582198 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.582835 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.583283 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.584131 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.584862 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.585725 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.586473 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.587308 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.588238 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.589296 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.590098 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.591094 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.593318 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.593930 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.594387 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.594813 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.595342 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.595754 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.596424 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.597273 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.598171 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.598761 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.599734 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.600729 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.601714 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.602685 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
"W0000 00:00:1766945733.603686 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n"
]
}
],
"source": [
"y_test_pred=model.predict(x_test, batch_size=32)\n",
"print(y_test_pred.shape)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "9ec44599-03a1-49ec-9ccc-ff921f095a94",
"metadata": {},
"outputs": [],
"source": [
"y_test_pred=y_test_pred * 48 + 48"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "2957cc0b-5f8f-4986-975b-1550d1a1cf5e",
"metadata": {},
"outputs": [],
"source": [
"feature_to_idx={f: i for i, f in enumerate(target_cols)}\n",
"\n",
"submission['Location']=[y_test_pred[row.ImageId - 1, feature_to_idx[row.FeatureName]]for row in submission.itertuples()]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "77192af0-257f-4144-a618-006e830395b7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" RowId ImageId FeatureName Location\n",
"0 1 1 left_eye_center_x 67.159271\n",
"1 2 1 left_eye_center_y 38.925602\n",
"2 3 1 right_eye_center_x 28.235607\n",
"3 4 1 right_eye_center_y 34.146210\n",
"4 5 1 left_eye_inner_corner_x 61.096382\n",
"(27124, 4)\n",
"0\n",
"count 27124.000000\n",
"mean 48.627544\n",
"std 17.872271\n",
"min 5.656898\n",
"25% 35.362759\n",
"50% 45.751131\n",
"75% 62.926924\n",
"max 102.183228\n",
"Name: Location, dtype: float64\n"
]
}
],
"source": [
"print(submission.head())\n",
"print(submission.shape)\n",
"print(submission['Location'].isna().sum())\n",
"print(submission['Location'].describe())"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "61b48caf-0c6b-41c0-a5e4-f509dd16891e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
},
{
"data": {
"text/html": [
"
![](\"https://www.ecovis.com/heidelberg-de/wp-content/uploads/2025/09/AdobeStock_425918469_djvstock-scaled.jpeg\")
"
]
},
{
"cell_type": "markdown",
"id": "d21a8e71-ab6d-452e-b7cf-8de90a4f3382",
"metadata": {},
"source": [
"### Introduction\n",
"\n",
"Facial keypoint detection is an important computer vision task used in areas such as face recognition, emotion analysis, and human–computer interaction.\n",
"The goal of this project is to predict 30 facial keypoint coordinates from 96×96 grayscale face images.\n",
"\n",
"To solve this task, a custom Convolutional Neural Network (CNN) was designed and trained from scratch.\n",
"The project covers the full deep learning workflow, including data preprocessing, model design, training, evaluation, visualization, and deployment."
]
},
{
"cell_type": "markdown",
"id": "e99ebf18-8f60-4a88-a415-ee99ab676a96",
"metadata": {},
"source": [
"### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "25596821-25b8-453b-901a-d08aecd2f257",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow import keras\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Rescaling, RandomFlip, RandomRotation, RandomZoom, Flatten, Dense, Dropout, BatchNormalization\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras import regularizers\n",
"from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
"from tensorflow.keras.layers import GlobalAveragePooling2D\n",
"from sklearn.model_selection import train_test_split\n",
"from PIL import Image\n",
"from pathlib import Path\n",
"import zipfile\n",
"import os\n",
"from collections import Counter\n",
"from tensorflow.keras.models import load_model\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import confusion_matrix, classification_report\n",
"from sklearn.metrics import ConfusionMatrixDisplay\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import pickle\n",
"import json\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\") "
]
},
{
"cell_type": "markdown",
"id": "c7c1a342-23e2-451d-933e-b75ae51d7b4f",
"metadata": {},
"source": [
"### Load Data"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "bf8ffe86-db68-4e5c-8c55-14fc06911cab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading from https://www.kaggle.com/api/v1/competitions/data/download-all/facial-keypoints-detection...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████████████████████| 76.3M/76.3M [00:12<00:00, 6.32MB/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting files...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DATA PATH: /root/.cache/kagglehub/competitions/facial-keypoints-detection\n",
"FILES: ['training.zip', 'IdLookupTable.csv', 'test.zip', 'SampleSubmission.csv']\n"
]
}
],
"source": [
"import kagglehub, os\n",
"\n",
"data_path = kagglehub.competition_download('facial-keypoints-detection')\n",
"print('DATA PATH:', data_path)\n",
"print('FILES:', os.listdir(data_path))\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "2a8fd3ee-df2d-4866-a374-5a7331c88421",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['training.zip', 'IdLookupTable.csv', 'test.zip', 'training.csv', 'test.csv', 'SampleSubmission.csv']\n"
]
}
],
"source": [
"for z in ['training.zip', 'test.zip']:\n",
" with zipfile.ZipFile(os.path.join(data_path, z), 'r') as zip_ref:\n",
" zip_ref.extractall(data_path)\n",
"\n",
"print(os.listdir(data_path))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "6f940a28-1897-4052-8096-83802fde2f56",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: (7049, 31)\n",
"test : (1783, 2)\n",
"lookup: (27124, 4)\n"
]
}
],
"source": [
"train_df=pd.read_csv(f'{data_path}/training.csv')\n",
"test_df=pd.read_csv(f'{data_path}/test.csv')\n",
"lookup_df=pd.read_csv(f'{data_path}/IdLookupTable.csv')\n",
"\n",
"print('train:', train_df.shape)\n",
"print('test :', test_df.shape)\n",
"print('lookup:', lookup_df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "81954a23-fad4-4b82-b97b-1f16b5429f15",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"n_targets: 30\n"
]
}
],
"source": [
"target_cols=train_df.columns.drop('Image').tolist()\n",
"print('n_targets:', len(target_cols))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "68e3ae90-726e-4366-b89f-ec1f0bed16a5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set()\n"
]
}
],
"source": [
"print(set(lookup_df['FeatureName']) - set(target_cols))"
]
},
{
"cell_type": "markdown",
"id": "e6df0edf-456e-48ed-bfea-5f672853cde9",
"metadata": {},
"source": [
"### EDA"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "4f8aefdd-7b6c-4e71-b786-919b3ff109d0",
"metadata": {},
"outputs": [
{
"data": {
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",
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