examples/time_series/cases/5.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "467482e4",
   "metadata": {},
   "source": [
    "### Example 5\n",
    "\n",
    "Refer to [/examples/time_series/generate_data.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/generate_data.ipynb) for the artificial time-series dataset (used in this example) generation  \n",
    "\n",
    "|Case|Small Batch|Medium Batch|Large Batch|\n",
    "|-|-|-|-|\n",
    "|Example Number|[4](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/4.ipynb)|[2](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/2.ipynb)|5|\n",
    "|ID|0_1_0_3_64_1_0_1_0_0|0_1_0_3_128_1_0_1_0_0|0_1_0_3_256_1_0_1_0_0|\n",
    "|Total params|17,185|17,185|17,185|\n",
    "|Total time (min)|2.8|1.5|1.0|\n",
    "|Num of epochs|47|49|63|\n",
    "|Time per epoch (s)|3.61|1.79|0.95|\n",
    "|Train RMSE|0.52|0.53|0.55|\n",
    "|Test RMSE|0.56|0.58|0.58|\n",
    "\n",
    "- Increasing the batch size does not deteriorate the performance much\n",
    "- Time per epoch decreases as the batch size increases\n",
    "- Number of epochs increases as the batch size increases\n",
    "- Overall training time decreases as batch size increases\n",
    "\n",
    "Previous example: [/examples/time_series/cases/4.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/4.ipynb)  \n",
    "Next example: [/examples/time_series/cases/6.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/6.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a748aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '../') # To be able to reach 'functions.py'\n",
    "sys.path.insert(0, '../../../') # To be able to reach 'datasets' folder\n",
    "import functions as fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f978cd32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do the settings in this cell and run all\n",
    "class Train_Sets:\n",
    "    vers = 0 # 0 or 1\n",
    "    within = 1 # 0 or 1\n",
    "    inter = 0 # 0 or 1\n",
    "    numOfSens = 3 # 1 or 2 or 3\n",
    "    batchSize = 256 # (2 ** 0) or ... or (2 ** 8)\n",
    "    useHalf = True # False or True\n",
    "    dense = False # False or True\n",
    "    shuffle = True # False or True\n",
    "    stateful = False # False or True\n",
    "    resetStates = False # False or True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2fb5b2fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: 0_1_0_3_256_1_0_1_0_0\n"
     ]
    }
   ],
   "source": [
    "Train_Sets, Paths = fn.init(Train_Sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3350324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:      (9216, 32, 3)\n",
      "y_train shape:      (9216,)\n",
      "X_val shape:        (3072, 32, 3)\n",
      "y_val shape:        (3072,)\n",
      "X_test shape:       (3072, 32, 3)\n",
      "y_test shape:       (3072,)\n",
      "Common data type:   <class 'numpy.float32'>\n"
     ]
    }
   ],
   "source": [
    "X_train, X_val, X_test, y_train, y_val, y_test = fn.split_data(Train_Sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ad3409e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2\n",
      "\n",
      "systemMemory: 8.00 GB\n",
      "maxCacheSize: 2.67 GB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 22:44:05.612193: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-12-11 22:44:05.612442: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 22:44:06.005897: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-12-11 22:44:06.935876: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 22:44:07.107013: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 22:44:07.505768: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 22:44:07.795170: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 3/36 [=>............................] - ETA: 0s - loss: 49.1688 - root_mean_squared_error: 7.0120 - mean_absolute_error: 6.2680  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 22:44:08.059524: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36/36 [==============================] - ETA: 0s - loss: 26.8566 - root_mean_squared_error: 5.1823 - mean_absolute_error: 4.2330"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 22:44:09.239575: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 22:44:09.304621: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 22:44:09.365179: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36/36 [==============================] - 4s 40ms/step - loss: 26.8566 - root_mean_squared_error: 5.1823 - mean_absolute_error: 4.2330 - val_loss: 6.1730 - val_root_mean_squared_error: 2.4846 - val_mean_absolute_error: 1.9662\n",
      "Epoch 2/128\n",
      "36/36 [==============================] - 1s 24ms/step - loss: 5.0293 - root_mean_squared_error: 2.2426 - mean_absolute_error: 1.7842 - val_loss: 3.9857 - val_root_mean_squared_error: 1.9964 - val_mean_absolute_error: 1.5887\n",
      "Epoch 3/128\n",
      "36/36 [==============================] - 1s 26ms/step - loss: 3.5592 - root_mean_squared_error: 1.8866 - mean_absolute_error: 1.5037 - val_loss: 2.7909 - val_root_mean_squared_error: 1.6706 - val_mean_absolute_error: 1.3174\n",
      "Epoch 4/128\n",
      "36/36 [==============================] - 1s 26ms/step - loss: 2.5862 - root_mean_squared_error: 1.6082 - mean_absolute_error: 1.2814 - val_loss: 2.1194 - val_root_mean_squared_error: 1.4558 - val_mean_absolute_error: 1.1459\n",
      "Epoch 5/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 2.0672 - root_mean_squared_error: 1.4378 - mean_absolute_error: 1.1396 - val_loss: 1.7279 - val_root_mean_squared_error: 1.3145 - val_mean_absolute_error: 1.0393\n",
      "Epoch 6/128\n",
      "36/36 [==============================] - 1s 24ms/step - loss: 1.6892 - root_mean_squared_error: 1.2997 - mean_absolute_error: 1.0357 - val_loss: 1.3824 - val_root_mean_squared_error: 1.1758 - val_mean_absolute_error: 0.9341\n",
      "Epoch 7/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 1.4286 - root_mean_squared_error: 1.1952 - mean_absolute_error: 0.9482 - val_loss: 1.1408 - val_root_mean_squared_error: 1.0681 - val_mean_absolute_error: 0.8470\n",
      "Epoch 8/128\n",
      "36/36 [==============================] - 1s 27ms/step - loss: 1.2172 - root_mean_squared_error: 1.1033 - mean_absolute_error: 0.8728 - val_loss: 0.9882 - val_root_mean_squared_error: 0.9941 - val_mean_absolute_error: 0.7833\n",
      "Epoch 9/128\n",
      "36/36 [==============================] - 1s 24ms/step - loss: 1.0975 - root_mean_squared_error: 1.0476 - mean_absolute_error: 0.8270 - val_loss: 0.8956 - val_root_mean_squared_error: 0.9463 - val_mean_absolute_error: 0.7407\n",
      "Epoch 10/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.9807 - root_mean_squared_error: 0.9903 - mean_absolute_error: 0.7816 - val_loss: 0.8759 - val_root_mean_squared_error: 0.9359 - val_mean_absolute_error: 0.7296\n",
      "Epoch 11/128\n",
      "36/36 [==============================] - 1s 24ms/step - loss: 0.9311 - root_mean_squared_error: 0.9649 - mean_absolute_error: 0.7605 - val_loss: 0.7420 - val_root_mean_squared_error: 0.8614 - val_mean_absolute_error: 0.6717\n",
      "Epoch 12/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.8500 - root_mean_squared_error: 0.9220 - mean_absolute_error: 0.7285 - val_loss: 0.7212 - val_root_mean_squared_error: 0.8493 - val_mean_absolute_error: 0.6639\n",
      "Epoch 13/128\n",
      "36/36 [==============================] - 1s 31ms/step - loss: 0.8189 - root_mean_squared_error: 0.9049 - mean_absolute_error: 0.7125 - val_loss: 0.6541 - val_root_mean_squared_error: 0.8087 - val_mean_absolute_error: 0.6332\n",
      "Epoch 14/128\n",
      "36/36 [==============================] - 1s 27ms/step - loss: 0.7671 - root_mean_squared_error: 0.8758 - mean_absolute_error: 0.6894 - val_loss: 0.6278 - val_root_mean_squared_error: 0.7923 - val_mean_absolute_error: 0.6185\n",
      "Epoch 15/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.7341 - root_mean_squared_error: 0.8568 - mean_absolute_error: 0.6755 - val_loss: 0.6447 - val_root_mean_squared_error: 0.8029 - val_mean_absolute_error: 0.6315\n",
      "Epoch 16/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.7308 - root_mean_squared_error: 0.8548 - mean_absolute_error: 0.6754 - val_loss: 0.6039 - val_root_mean_squared_error: 0.7771 - val_mean_absolute_error: 0.6067\n",
      "Epoch 17/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.6773 - root_mean_squared_error: 0.8230 - mean_absolute_error: 0.6490 - val_loss: 0.6214 - val_root_mean_squared_error: 0.7883 - val_mean_absolute_error: 0.6165\n",
      "Epoch 18/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.6766 - root_mean_squared_error: 0.8226 - mean_absolute_error: 0.6493 - val_loss: 0.5436 - val_root_mean_squared_error: 0.7373 - val_mean_absolute_error: 0.5762\n",
      "Epoch 19/128\n",
      "36/36 [==============================] - 1s 26ms/step - loss: 0.6215 - root_mean_squared_error: 0.7883 - mean_absolute_error: 0.6211 - val_loss: 0.5538 - val_root_mean_squared_error: 0.7442 - val_mean_absolute_error: 0.5807\n",
      "Epoch 20/128\n",
      "36/36 [==============================] - 1s 26ms/step - loss: 0.6298 - root_mean_squared_error: 0.7936 - mean_absolute_error: 0.6261 - val_loss: 0.5052 - val_root_mean_squared_error: 0.7108 - val_mean_absolute_error: 0.5554\n",
      "Epoch 21/128\n",
      "36/36 [==============================] - 1s 26ms/step - loss: 0.6078 - root_mean_squared_error: 0.7796 - mean_absolute_error: 0.6187 - val_loss: 0.4961 - val_root_mean_squared_error: 0.7044 - val_mean_absolute_error: 0.5509\n",
      "Epoch 22/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.6002 - root_mean_squared_error: 0.7747 - mean_absolute_error: 0.6128 - val_loss: 0.5589 - val_root_mean_squared_error: 0.7476 - val_mean_absolute_error: 0.5829\n",
      "Epoch 23/128\n",
      "36/36 [==============================] - 1s 26ms/step - loss: 0.5978 - root_mean_squared_error: 0.7732 - mean_absolute_error: 0.6108 - val_loss: 0.4815 - val_root_mean_squared_error: 0.6939 - val_mean_absolute_error: 0.5432\n",
      "Epoch 24/128\n",
      "36/36 [==============================] - 1s 31ms/step - loss: 0.5757 - root_mean_squared_error: 0.7588 - mean_absolute_error: 0.5965 - val_loss: 0.4808 - val_root_mean_squared_error: 0.6934 - val_mean_absolute_error: 0.5424\n",
      "Epoch 25/128\n",
      "36/36 [==============================] - 1s 26ms/step - loss: 0.5692 - root_mean_squared_error: 0.7544 - mean_absolute_error: 0.5928 - val_loss: 0.4589 - val_root_mean_squared_error: 0.6774 - val_mean_absolute_error: 0.5307\n",
      "Epoch 26/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.5514 - root_mean_squared_error: 0.7426 - mean_absolute_error: 0.5868 - val_loss: 0.4632 - val_root_mean_squared_error: 0.6806 - val_mean_absolute_error: 0.5329\n",
      "Epoch 27/128\n",
      "36/36 [==============================] - 1s 27ms/step - loss: 0.5547 - root_mean_squared_error: 0.7448 - mean_absolute_error: 0.5884 - val_loss: 0.4574 - val_root_mean_squared_error: 0.6763 - val_mean_absolute_error: 0.5315\n",
      "Epoch 28/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.5609 - root_mean_squared_error: 0.7490 - mean_absolute_error: 0.5888 - val_loss: 0.4841 - val_root_mean_squared_error: 0.6957 - val_mean_absolute_error: 0.5463\n",
      "Epoch 29/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.5387 - root_mean_squared_error: 0.7340 - mean_absolute_error: 0.5803 - val_loss: 0.4302 - val_root_mean_squared_error: 0.6559 - val_mean_absolute_error: 0.5139\n",
      "Epoch 30/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.5421 - root_mean_squared_error: 0.7363 - mean_absolute_error: 0.5824 - val_loss: 0.4601 - val_root_mean_squared_error: 0.6783 - val_mean_absolute_error: 0.5325\n",
      "Epoch 31/128\n",
      "36/36 [==============================] - 1s 26ms/step - loss: 0.5032 - root_mean_squared_error: 0.7093 - mean_absolute_error: 0.5600 - val_loss: 0.4178 - val_root_mean_squared_error: 0.6464 - val_mean_absolute_error: 0.5054\n",
      "Epoch 32/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.5036 - root_mean_squared_error: 0.7096 - mean_absolute_error: 0.5587 - val_loss: 0.4199 - val_root_mean_squared_error: 0.6480 - val_mean_absolute_error: 0.5109\n",
      "Epoch 33/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.5110 - root_mean_squared_error: 0.7148 - mean_absolute_error: 0.5628 - val_loss: 0.4395 - val_root_mean_squared_error: 0.6629 - val_mean_absolute_error: 0.5164\n",
      "Epoch 34/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.5175 - root_mean_squared_error: 0.7194 - mean_absolute_error: 0.5658 - val_loss: 0.4126 - val_root_mean_squared_error: 0.6423 - val_mean_absolute_error: 0.5053\n",
      "Epoch 35/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.5283 - root_mean_squared_error: 0.7268 - mean_absolute_error: 0.5705 - val_loss: 0.4167 - val_root_mean_squared_error: 0.6455 - val_mean_absolute_error: 0.5082\n",
      "Epoch 36/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4922 - root_mean_squared_error: 0.7016 - mean_absolute_error: 0.5519 - val_loss: 0.3908 - val_root_mean_squared_error: 0.6251 - val_mean_absolute_error: 0.4883\n",
      "Epoch 37/128\n",
      "36/36 [==============================] - 1s 24ms/step - loss: 0.4837 - root_mean_squared_error: 0.6955 - mean_absolute_error: 0.5484 - val_loss: 0.4337 - val_root_mean_squared_error: 0.6586 - val_mean_absolute_error: 0.5181\n",
      "Epoch 38/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4882 - root_mean_squared_error: 0.6987 - mean_absolute_error: 0.5516 - val_loss: 0.3929 - val_root_mean_squared_error: 0.6268 - val_mean_absolute_error: 0.4924\n",
      "Epoch 39/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4670 - root_mean_squared_error: 0.6834 - mean_absolute_error: 0.5396 - val_loss: 0.3931 - val_root_mean_squared_error: 0.6270 - val_mean_absolute_error: 0.4945\n",
      "Epoch 40/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4821 - root_mean_squared_error: 0.6943 - mean_absolute_error: 0.5460 - val_loss: 0.3934 - val_root_mean_squared_error: 0.6272 - val_mean_absolute_error: 0.4921\n",
      "Epoch 41/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4704 - root_mean_squared_error: 0.6859 - mean_absolute_error: 0.5382 - val_loss: 0.3852 - val_root_mean_squared_error: 0.6207 - val_mean_absolute_error: 0.4877\n",
      "Epoch 42/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4797 - root_mean_squared_error: 0.6926 - mean_absolute_error: 0.5459 - val_loss: 0.4142 - val_root_mean_squared_error: 0.6436 - val_mean_absolute_error: 0.5120\n",
      "Epoch 43/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4591 - root_mean_squared_error: 0.6776 - mean_absolute_error: 0.5316 - val_loss: 0.3804 - val_root_mean_squared_error: 0.6167 - val_mean_absolute_error: 0.4834\n",
      "Epoch 44/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4586 - root_mean_squared_error: 0.6772 - mean_absolute_error: 0.5343 - val_loss: 0.3793 - val_root_mean_squared_error: 0.6158 - val_mean_absolute_error: 0.4827\n",
      "Epoch 45/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4428 - root_mean_squared_error: 0.6654 - mean_absolute_error: 0.5249 - val_loss: 0.3718 - val_root_mean_squared_error: 0.6097 - val_mean_absolute_error: 0.4789\n",
      "Epoch 46/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4560 - root_mean_squared_error: 0.6753 - mean_absolute_error: 0.5324 - val_loss: 0.3851 - val_root_mean_squared_error: 0.6205 - val_mean_absolute_error: 0.4877\n",
      "Epoch 47/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4453 - root_mean_squared_error: 0.6673 - mean_absolute_error: 0.5224 - val_loss: 0.3996 - val_root_mean_squared_error: 0.6321 - val_mean_absolute_error: 0.5010\n",
      "Epoch 48/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4564 - root_mean_squared_error: 0.6755 - mean_absolute_error: 0.5304 - val_loss: 0.3858 - val_root_mean_squared_error: 0.6212 - val_mean_absolute_error: 0.4904\n",
      "Epoch 49/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4370 - root_mean_squared_error: 0.6611 - mean_absolute_error: 0.5203 - val_loss: 0.3540 - val_root_mean_squared_error: 0.5949 - val_mean_absolute_error: 0.4706\n",
      "Epoch 50/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4487 - root_mean_squared_error: 0.6698 - mean_absolute_error: 0.5254 - val_loss: 0.3753 - val_root_mean_squared_error: 0.6126 - val_mean_absolute_error: 0.4828\n",
      "Epoch 51/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4383 - root_mean_squared_error: 0.6620 - mean_absolute_error: 0.5234 - val_loss: 0.4260 - val_root_mean_squared_error: 0.6527 - val_mean_absolute_error: 0.5185\n",
      "Epoch 52/128\n",
      "36/36 [==============================] - 1s 24ms/step - loss: 0.4425 - root_mean_squared_error: 0.6652 - mean_absolute_error: 0.5237 - val_loss: 0.3906 - val_root_mean_squared_error: 0.6250 - val_mean_absolute_error: 0.4909\n",
      "Epoch 53/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4427 - root_mean_squared_error: 0.6653 - mean_absolute_error: 0.5221 - val_loss: 0.3646 - val_root_mean_squared_error: 0.6038 - val_mean_absolute_error: 0.4744\n",
      "Epoch 54/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4344 - root_mean_squared_error: 0.6591 - mean_absolute_error: 0.5194 - val_loss: 0.4150 - val_root_mean_squared_error: 0.6442 - val_mean_absolute_error: 0.5174\n",
      "Epoch 55/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4331 - root_mean_squared_error: 0.6581 - mean_absolute_error: 0.5182 - val_loss: 0.3518 - val_root_mean_squared_error: 0.5932 - val_mean_absolute_error: 0.4680\n",
      "Epoch 56/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4215 - root_mean_squared_error: 0.6492 - mean_absolute_error: 0.5104 - val_loss: 0.3541 - val_root_mean_squared_error: 0.5950 - val_mean_absolute_error: 0.4696\n",
      "Epoch 57/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4271 - root_mean_squared_error: 0.6535 - mean_absolute_error: 0.5142 - val_loss: 0.3588 - val_root_mean_squared_error: 0.5990 - val_mean_absolute_error: 0.4733\n",
      "Epoch 58/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4247 - root_mean_squared_error: 0.6517 - mean_absolute_error: 0.5122 - val_loss: 0.3545 - val_root_mean_squared_error: 0.5954 - val_mean_absolute_error: 0.4718\n",
      "Epoch 59/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4344 - root_mean_squared_error: 0.6591 - mean_absolute_error: 0.5186 - val_loss: 0.3904 - val_root_mean_squared_error: 0.6248 - val_mean_absolute_error: 0.4991\n",
      "Epoch 60/128\n",
      "36/36 [==============================] - 1s 24ms/step - loss: 0.4315 - root_mean_squared_error: 0.6569 - mean_absolute_error: 0.5157 - val_loss: 0.3539 - val_root_mean_squared_error: 0.5949 - val_mean_absolute_error: 0.4699\n",
      "Epoch 61/128\n",
      "36/36 [==============================] - 1s 24ms/step - loss: 0.4088 - root_mean_squared_error: 0.6393 - mean_absolute_error: 0.5008 - val_loss: 0.3519 - val_root_mean_squared_error: 0.5932 - val_mean_absolute_error: 0.4702\n",
      "Epoch 62/128\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4292 - root_mean_squared_error: 0.6551 - mean_absolute_error: 0.5142 - val_loss: 0.3718 - val_root_mean_squared_error: 0.6098 - val_mean_absolute_error: 0.4833\n",
      "Epoch 63/128\n",
      "34/36 [===========================>..] - ETA: 0s - loss: 0.4340 - root_mean_squared_error: 0.6588 - mean_absolute_error: 0.5182Restoring model weights from the end of the best epoch: 55.\n",
      "36/36 [==============================] - 1s 25ms/step - loss: 0.4344 - root_mean_squared_error: 0.6591 - mean_absolute_error: 0.5180 - val_loss: 0.4213 - val_root_mean_squared_error: 0.6491 - val_mean_absolute_error: 0.5160\n",
      "Epoch 63: early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /Users/serhatsoyer/Repos/py4ML/datasets/time_series/cases/0_1_0_3_256_1_0_1_0_0/model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /Users/serhatsoyer/Repos/py4ML/datasets/time_series/cases/0_1_0_3_256_1_0_1_0_0/model/assets\n"
     ]
    }
   ],
   "source": [
    "model, df, train_time = fn.get_model(Train_Sets, X_train, X_val, y_train, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c649667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"0_1_0_3_256_1_0_1_0_0\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm1 (LSTM)                (256, 32, 32)             4608      \n",
      "                                                                 \n",
      " lstm2 (LSTM)                (256, 32)                 8320      \n",
      "                                                                 \n",
      " dense3 (Dense)              (256, 32)                 1056      \n",
      "                                                                 \n",
      " dense4 (Dense)              (256, 32)                 1056      \n",
      "                                                                 \n",
      " dense5 (Dense)              (256, 32)                 1056      \n",
      "                                                                 \n",
      " drop0.0625 (Dropout)        (256, 32)                 0         \n",
      "                                                                 \n",
      " dense6 (Dense)              (256, 32)                 1056      \n",
      "                                                                 \n",
      " out (Dense)                 (256, 1)                  33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 17,185\n",
      "Trainable params: 17,185\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a3689ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training time:          1.0 min\n",
      "Num of epochs:                63\n",
      "Best val RMSE epoch:          55\n",
      "Training time per epoch:      0.95 sec\n"
     ]
    }
   ],
   "source": [
    "fn.train_info(df, train_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9939f067",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn.plot_learn_curve(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc576e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-11 22:45:12.901764: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 22:45:12.960609: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-11 22:45:13.027490: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train_pred shape: (9216,)\n",
      "y_val_pred shape:   (3072,)\n",
      "y_test_pred shape:  (3072,)\n",
      "Common data type:   <class 'numpy.float32'>\n"
     ]
    }
   ],
   "source": [
    "y_train_pred, y_val_pred, y_test_pred = fn.get_predictions(Train_Sets, model, X_train, X_val, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c28b462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse_train:     0.30\n",
      "mse_val:       0.35\n",
      "mse_test:      0.34\n",
      "rmse_train:    0.55\n",
      "rmse_val:      0.59\n",
      "rmse_test:     0.58\n",
      "mae_train:     0.43\n",
      "mae_val:       0.47\n",
      "mae_test:      0.45\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn.evaluate_model(y_train, y_val, y_test, y_train_pred, y_val_pred, y_test_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1a17012b",
   "metadata": {},
   "source": [
    "Previous example: [/examples/time_series/cases/4.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/4.ipynb)  \n",
    "Next example: [/examples/time_series/cases/6.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/time_series/cases/6.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('study')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8 (main, Nov 24 2022, 08:08:27) [Clang 14.0.6 ]"
  },
  "vscode": {
   "interpreter": {
    "hash": "29d06b973d1ddb34db3279b24f9b5152402e688db937648b736e855ec4de60c0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}