ANN_ABO3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "naked-record",
   "metadata": {
    "id": "iraqi-thriller"
   },
   "source": [
    "## Introduction\n",
    "This notebook aims to replicate the studies that were successful in predicting the lattice constants of the Perovskite crystal ($ABO_{3}$). The papers preoduced the results with the model's error around 2%. It's important to identify the features neccessary to perform predictions. The lattice constants of the $ABO_{3}$ -type may be correlated compounds as a general function of nine variables as below\n",
    "\n",
    "$Lattice constant = f(z_A + z_B + z_O + r_A + r_B + r_O + x_A + x_B + x_O)$\n",
    "\n",
    "where z, r and x denote the valence, the radius and the electronegativity of the ions (A, B, O), respectively. It is noted that the three variables associated with anion $O^{2-}$ , namely $z_{O}, r_{O},$ and $x_{O}$ can be ignored as they remain unchanged for all of the samples.\n",
    "\n",
    "The lattice constant can be reduced as a ﬁve-parameter function shown below.\n",
    "\n",
    "$Lattice constant = f(z_A + r_A + r_B + x_A + x_B)$\n",
    "\n",
    "The Goldschmidt’s tolerance factor (t) can condense the coation atomic radii into one expression. It has been widely accepted as a criterion for the formation of the perovskite structure, up to now. I will use $\\textbf{four features}$ including the tolerance factor and others are denoted by, $r_{A}, r_{B}$ and $\\frac{r_{A}}{t}$. The list of feature does not include electronegativity and valence number. Basically, other sets of features that includes these two atomic configurations can be used to predict the lattice constant with great accuracy. However, I will only take the atomic radii of the two cations in to my account. The tolerance factor (t) is expressed as below.\n",
    "\n",
    "$t = \\frac{r_A + r_O}{\\sqrt{2}(r_B + r_O)}$\n",
    "\n",
    "## Procedure\n",
    "First I will perform feature engineering. Next I will build the ANN (Hereafter called as ANN) model and also perform a GridSearch (GS). GS fine tunes the hyperparameters of the ANN. Finally I will perform the predictions from the model. The model will be prepared with the fine tuned hyperparameters. The GridSearch in the sklearn package convineintly provides the best model which can be used to fit and predict.\n",
    "\n",
    "## Feature engineering\n",
    "In the following section, the \".csv\" file is loaded into the program. The data is inspected for assurity. I observed the '.csv' provides the atomic radii for two atoms A and B in the crystal ($r_{A}$ and $r_{b}$), and the tolerance factor is absent. So the tolerance factor and $\\frac{r_{A}}{t}$ needs to be calculated. I have concanated them as columns in the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bigger-plastic",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "actual-cemetery",
    "outputId": "6dbe1700-fcb6-4d60-b8f7-e8a6b217be5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first five smaples from the DataFrame\n",
      "\n",
      "            ra    rb    xa    xb  za       a       b       c\n",
      "compound                                                    \n",
      "BaCeO3    1.35  0.87  0.89  1.12   2  6.2350  8.7810  6.2120\n",
      "BaPrO3    1.35  0.85  0.89  1.13   2  6.2140  8.7220  6.1810\n",
      "BaPuO3    1.35  0.86  0.89  1.30   2  6.1930  8.7440  6.2190\n",
      "CaCrO3    1.00  0.55  1.00  1.66   2  5.3160  7.4860  5.2870\n",
      "CaGeO3    1.00  0.53  1.00  2.01   2  5.2688  7.4452  5.2607\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "lat_df = pd.read_csv('lat_const.csv', header=0, index_col = 0, usecols= ['compound','ra','rb','xa','xb','za','a','b','c'])\n",
    "print (\"The first five smaples from the DataFrame\\n\")\n",
    "print (lat_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "broken-walnut",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "editorial-provider",
    "outputId": "759b4126-9bf4-4762-c1a1-dc53a770cdd0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first five rows of imputed DataFrame\n",
      "\n",
      "            ra    rb    xa    xb  za       a       b       c  tolerance  \\\n",
      "compound                                                                  \n",
      "BaCeO3    1.35  0.87  0.89  1.12   2  6.2350  8.7810  6.2120   0.856627   \n",
      "BaPrO3    1.35  0.85  0.89  1.13   2  6.2140  8.7220  6.1810   0.864242   \n",
      "BaPuO3    1.35  0.86  0.89  1.30   2  6.1930  8.7440  6.2190   0.860418   \n",
      "CaCrO3    1.00  0.55  1.00  1.66   2  5.3160  7.4860  5.2870   0.870285   \n",
      "CaGeO3    1.00  0.53  1.00  2.01   2  5.2688  7.4452  5.2607   0.879304   \n",
      "\n",
      "               rat  \n",
      "compound            \n",
      "BaCeO3    1.575948  \n",
      "BaPrO3    1.562063  \n",
      "BaPuO3    1.569006  \n",
      "CaCrO3    1.149049  \n",
      "CaGeO3    1.137263  \n"
     ]
    }
   ],
   "source": [
    "lat_df = lat_df.assign(tolerance = lambda x:((x['ra'] + 1.4)/(np.sqrt(2)*(x['rb'] + 1.4))))\n",
    "lat_df = lat_df.assign(rat = lambda x:(x['ra']/x['tolerance']))\n",
    "\n",
    "print (\"The first five rows of imputed DataFrame\\n\")\n",
    "print (lat_df.head())\n",
    "\n",
    "features = np.array(lat_df[['ra', 'rb', 'tolerance', 'rat']], np.float32)\n",
    "targets = np.array(lat_df[['a', 'b', 'c']], np.float32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f0c9d2c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The first five observations of the features\n",
      "[[1.35       0.87       0.85662717 1.5759481 ]\n",
      " [1.35       0.85       0.8642416  1.5620632 ]\n",
      " [1.35       0.86       0.86041754 1.5690056 ]\n",
      " [1.         0.55       0.8702853  1.1490486 ]\n",
      " [1.         0.53       0.87930375 1.1372634 ]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print (\"\\nThe first five observations of the features\")\n",
    "print (features[:5,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "64216d3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The first five observations of the targets\n",
      "[[6.235  8.781  6.212 ]\n",
      " [6.214  8.722  6.181 ]\n",
      " [6.193  8.744  6.219 ]\n",
      " [5.316  7.486  5.287 ]\n",
      " [5.2688 7.4452 5.2607]]\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nThe first five observations of the targets\")\n",
    "print (targets[:5,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01f4dcae",
   "metadata": {},
   "source": [
    "### Normalization\n",
    "Normalization (or min-max normalization) scale all values in a fixed range between 0 and 1. This transformation does not change the distribution of the feature and due to the decreased standard deviations, the effects of the outliers increases. Therefore, before normalization, it is recommended to handle the outliers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c4579310",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.5607106  0.36134684 0.35579255 0.65455616]\n",
      " [0.56387436 0.35503203 0.3609805  0.65244997]\n",
      " [0.56229514 0.35820284 0.35837674 0.65351427]\n",
      " [0.54391146 0.29915133 0.47335815 0.6249807 ]\n",
      " [0.54656696 0.28968048 0.4805984  0.6215906 ]\n",
      " [0.54656696 0.28968048 0.4805984  0.6215906 ]\n",
      " [0.53398734 0.33107215 0.44861713 0.63560325]\n",
      " [0.5232715  0.3610573  0.42489052 0.644432  ]\n",
      " [0.5354591  0.32663006 0.45209166 0.63419986]\n",
      " [0.5397707  0.31306702 0.46263704 0.6297647 ]\n",
      " [0.51849306 0.373315   0.4150528  0.6477129 ]\n",
      " [0.524666   0.33689082 0.45657924 0.63463813]\n",
      " [0.53310525 0.3378093  0.44092375 0.638177  ]\n",
      " [0.38529196 0.5008795  0.50501543 0.58790237]\n",
      " [0.5230177  0.35047576 0.4407823  0.63978916]\n",
      " [0.5095158  0.36393985 0.44612792 0.63946176]\n",
      " [0.5095158  0.36393985 0.44612792 0.63946176]\n",
      " [0.5229058  0.3217882  0.47887594 0.627455  ]\n",
      " [0.5110687  0.3594329  0.44968116 0.63828176]\n",
      " [0.5048296  0.36869577 0.44804478 0.6391138 ]\n",
      " [0.50638986 0.36414552 0.45163262 0.6379618 ]\n",
      " [0.51860607 0.35483572 0.4424997  0.63979167]\n",
      " [0.51860607 0.35483572 0.4424997  0.63979167]\n",
      " [0.53185767 0.3135161  0.47464466 0.6273337 ]\n",
      " [0.51636755 0.35706267 0.44338205 0.6397524 ]\n",
      " [0.52965415 0.31553864 0.47567385 0.6274044 ]\n",
      " [0.51324344 0.36582246 0.43644154 0.64208555]\n",
      " [0.51790905 0.3526189  0.4468856  0.63853216]\n",
      " [0.5048296  0.36869577 0.44804478 0.6391138 ]\n",
      " [0.5048296  0.36869577 0.44804478 0.6391138 ]\n",
      " [0.54023236 0.32518843 0.44615245 0.635098  ]\n",
      " [0.4966893  0.42917815 0.361829   0.66195583]\n",
      " [0.5357593  0.33810055 0.43598342 0.6391935 ]\n",
      " [0.5357593  0.33810055 0.43598342 0.6391935 ]\n",
      " [0.5311589  0.35066804 0.4260055  0.6429786 ]\n",
      " [0.5327055  0.34651718 0.42930987 0.6417506 ]\n",
      " [0.49838263 0.42580262 0.36465493 0.6613121 ]\n",
      " [0.53726536 0.3338348  0.43935215 0.6378635 ]\n",
      " [0.37345642 0.53777725 0.47333944 0.58930105]\n",
      " [0.35438412 0.58827764 0.42700994 0.58822095]\n",
      " [0.49367896 0.4224263  0.3824735  0.65692556]\n",
      " [0.48432726 0.4510891  0.34575722 0.6651298 ]\n",
      " [0.52519166 0.3483414  0.43994647 0.63974917]\n",
      " [0.52519166 0.3483414  0.43994647 0.63974917]\n",
      " [0.5220983  0.35694477 0.43312955 0.64218575]\n",
      " [0.52671766 0.34397888 0.44338766 0.6384782 ]\n",
      " [0.378579   0.522439   0.4867196  0.5889307 ]\n",
      " [0.5167647  0.3675955  0.4271773  0.6444745 ]\n",
      " [0.527345   0.34623662 0.4391255  0.63968444]\n",
      " [0.527345   0.34623662 0.4391255  0.63968444]\n",
      " [0.5404545  0.30571163 0.4707071  0.62680477]\n",
      " [0.5367693  0.30193272 0.48096305 0.62401116]\n",
      " [0.52082264 0.3526403  0.44163325 0.63980365]\n",
      " [0.534039   0.31152275 0.4736338  0.62723756]\n",
      " [0.5177138  0.3613211  0.43475556 0.6421893 ]\n",
      " [0.53031814 0.3909973  0.36118802 0.6598689 ]\n",
      " [0.55584633 0.33444992 0.4072818  0.64288384]\n",
      " [0.5676636  0.30307466 0.43233234 0.6316586 ]\n",
      " [0.5449109  0.36019534 0.38644856 0.6511444 ]\n",
      " [0.53359854 0.38437185 0.36665264 0.65810084]\n",
      " [0.5690764  0.29900625 0.4355539  0.6301113 ]\n",
      " [0.5588784  0.3268018  0.41342217 0.64026374]\n",
      " [0.5543133  0.3382251  0.4042427  0.64414924]\n",
      " [0.5279396  0.3098776  0.48525238 0.6243282 ]\n",
      " [0.51182294 0.36161402 0.44519576 0.6395884 ]\n",
      " [0.51182294 0.36161402 0.44519576 0.6395884 ]\n",
      " [0.513372   0.35712835 0.44873214 0.63839483]\n",
      " [0.5024494  0.3711274  0.4490303  0.6388904 ]\n",
      " [0.5040133  0.36655512 0.45263594 0.6377528 ]\n",
      " [0.513974   0.3227279  0.4923052  0.6239495 ]\n",
      " [0.49761203 0.3761021  0.45105758 0.63833886]\n",
      " [0.49918324 0.37148517 0.45469984 0.6372307 ]\n",
      " [0.51181036 0.35258046 0.45785508 0.63569325]\n",
      " [0.5071849  0.3663002  0.44707748 0.63930386]\n",
      " [0.50403094 0.37522304 0.44000456 0.6415266 ]\n",
      " [0.53398734 0.33107215 0.44861713 0.63560325]\n",
      " [0.50228155 0.36210996 0.46205196 0.6348997 ]\n",
      " [0.50228155 0.36210996 0.46205196 0.6348997 ]\n",
      " [0.50709707 0.35727295 0.4599143  0.635364  ]\n",
      " [0.50946623 0.35490906 0.45887512 0.63554496]\n",
      " [0.5164258  0.3480261  0.45587075 0.6358963 ]\n",
      " [0.5231744  0.34144014 0.45302728 0.6359823 ]\n",
      " [0.5253793  0.3393075  0.4521134  0.63595635]\n",
      " [0.5275626  0.337205   0.45121577 0.6359047 ]\n",
      " [0.53608847 0.329084   0.44778112 0.6354565 ]\n",
      " [0.53186625 0.33308795 0.449468   0.63572735]\n",
      " [0.52972484 0.33513203 0.4503341  0.6358281 ]\n",
      " [0.50946623 0.35490906 0.45887512 0.63554496]\n",
      " [0.51413006 0.3502864  0.45685378 0.63581   ]\n",
      " [0.5209475  0.34360367 0.45395792 0.6359815 ]\n",
      " [0.53833705 0.30762115 0.47166544 0.62697226]\n",
      " [0.5226972  0.35902435 0.42897755 0.64332527]\n",
      " [0.53159124 0.34211317 0.43752673 0.63948387]\n",
      " [0.5210705  0.31615514 0.48868075 0.6242776 ]\n",
      " [0.51873106 0.31831226 0.48986644 0.6242005 ]\n",
      " [0.49761203 0.3761021  0.45105758 0.63833886]\n",
      " [0.5327055  0.34651718 0.42930987 0.6417506 ]\n",
      " [0.5285214  0.35060334 0.43079728 0.6419939 ]\n",
      " [0.52886707 0.34189382 0.4425517  0.63840127]\n",
      " [0.52425945 0.35480186 0.4323382  0.6421459 ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "scaler = Normalizer()\n",
    "\n",
    "scaler.fit(features)\n",
    "features = scaler.transform(features)\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "established-maintenance",
   "metadata": {},
   "source": [
    "## Machine Learning Model\n",
    "\n",
    "The prediction of lattice constants for $ABO_{3}$ from the atomic configurations like radii and tolerance has enigmatic mechanism. A linear regression model can not be conceived to solve multiple target variables. It can be deployed only in the case of single target variable. Here, the artificial neural network comes to the rescue. ANNs performance is remarkable for regression model. ANN works for any kind of numerical system.\n",
    "\n",
    "For the ANN, I will choose the loss function as the mean absolute error. \"MAE\" does not penalise the outliers in predictions compared to mean square error and huber loss function. I will choose Adam optimizer over Stochastic gradient descent (SGD). Besides Adam optimizer the other hyperparameters are number of neural nodes, number of hidden layers, learning rate (Adam optimizer), activation functions and so on. The ANN has lots of hyperparameters to be tuned before I set out to predict the target variables. So a GS will be neccessary to narrow down the optimal set of hyperparameters.\n",
    "\n",
    "A quick run down to my model's parameter is below.\n",
    "1. Number of epochs = 350\n",
    "2. Train and test data split ratio = 80:20\n",
    "3. Number of hidden layers = 2\n",
    "4. Number of neurons in each hidden layer = 400\n",
    "\n",
    "Once the model finishes its job, the loss (mean absolute error) and the graph for train and test loss against number of epochs is shown. Next, I will perform the GridSearch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "hispanic-location",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "weird-offense",
    "outputId": "21326357-b249-4129-816b-cfd4692d28d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/650\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 6.1585 - val_loss: 6.0434\n",
      "Epoch 2/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 5.9095 - val_loss: 5.7940\n",
      "Epoch 3/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 5.6508 - val_loss: 5.5002\n",
      "Epoch 4/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 5.3407 - val_loss: 5.1396\n",
      "Epoch 5/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 4.9571 - val_loss: 4.6898\n",
      "Epoch 6/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 4.4797 - val_loss: 4.1320\n",
      "Epoch 7/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 3.8878 - val_loss: 3.4426\n",
      "Epoch 8/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 3.1572 - val_loss: 2.5959\n",
      "Epoch 9/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 2.2653 - val_loss: 1.7291\n",
      "Epoch 10/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 1.5865 - val_loss: 1.3107\n",
      "Epoch 11/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 1.2039 - val_loss: 1.1563\n",
      "Epoch 12/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 1.1350 - val_loss: 0.9876\n",
      "Epoch 13/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.9778 - val_loss: 0.7614\n",
      "Epoch 14/650\n",
      "3/3 [==============================] - 0s 18ms/step - loss: 0.6842 - val_loss: 0.3109\n",
      "Epoch 15/650\n",
      "3/3 [==============================] - 0s 19ms/step - loss: 0.2705 - val_loss: 0.4581\n",
      "Epoch 16/650\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.4201 - val_loss: 0.4740\n",
      "Epoch 17/650\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 0.3769 - val_loss: 0.3378\n",
      "Epoch 18/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.3044 - val_loss: 0.3189\n",
      "Epoch 19/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.3201 - val_loss: 0.2662\n",
      "Epoch 20/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.2297 - val_loss: 0.2600\n",
      "Epoch 21/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.2209 - val_loss: 0.3095\n",
      "Epoch 22/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.2223 - val_loss: 0.2365\n",
      "Epoch 23/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.2108 - val_loss: 0.2287\n",
      "Epoch 24/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.1995 - val_loss: 0.2368\n",
      "Epoch 25/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.1953 - val_loss: 0.2777\n",
      "Epoch 26/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.1960 - val_loss: 0.2195\n",
      "Epoch 27/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.1933 - val_loss: 0.2175\n",
      "Epoch 28/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.1849 - val_loss: 0.2411\n",
      "Epoch 29/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.1815 - val_loss: 0.2382\n",
      "Epoch 30/650\n",
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      "3/3 [==============================] - 0s 11ms/step - loss: 0.0699 - val_loss: 0.0571\n",
      "Epoch 369/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0674 - val_loss: 0.0617\n",
      "Epoch 370/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0751 - val_loss: 0.0596\n",
      "Epoch 371/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0713 - val_loss: 0.0697\n",
      "Epoch 372/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0741 - val_loss: 0.0578\n",
      "Epoch 373/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0777 - val_loss: 0.0566\n",
      "Epoch 374/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0753 - val_loss: 0.0728\n",
      "Epoch 375/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.0721 - val_loss: 0.0711\n",
      "Epoch 376/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0786 - val_loss: 0.0745\n",
      "Epoch 377/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.0759 - val_loss: 0.0631\n",
      "Epoch 378/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0705 - val_loss: 0.0567\n",
      "Epoch 379/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0673 - val_loss: 0.0683\n",
      "Epoch 380/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0701 - val_loss: 0.0633\n",
      "Epoch 381/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.0691 - val_loss: 0.0627\n",
      "Epoch 382/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.0700 - val_loss: 0.0563\n",
      "Epoch 383/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.0680 - val_loss: 0.0599\n",
      "Epoch 384/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0671 - val_loss: 0.0814\n",
      "Epoch 385/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0768 - val_loss: 0.0636\n",
      "Epoch 386/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0765 - val_loss: 0.0826\n",
      "Epoch 387/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0788 - val_loss: 0.0589\n",
      "Epoch 388/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.0776 - val_loss: 0.0598\n",
      "Epoch 389/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0718 - val_loss: 0.0910\n",
      "Epoch 390/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0825 - val_loss: 0.0882\n",
      "Epoch 391/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0844 - val_loss: 0.0745\n",
      "Epoch 392/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0735 - val_loss: 0.0746\n",
      "Epoch 393/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0755 - val_loss: 0.0653\n",
      "Epoch 394/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0812 - val_loss: 0.0571\n",
      "Epoch 395/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0782 - val_loss: 0.0796\n",
      "Epoch 396/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0950 - val_loss: 0.0796\n",
      "Epoch 397/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0851 - val_loss: 0.0734\n",
      "Epoch 398/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0795 - val_loss: 0.0831\n",
      "Epoch 399/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0751 - val_loss: 0.0696\n",
      "Epoch 400/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0725 - val_loss: 0.0756\n",
      "Epoch 401/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0755 - val_loss: 0.0740\n",
      "Epoch 402/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0738 - val_loss: 0.0765\n",
      "Epoch 403/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0807 - val_loss: 0.0596\n",
      "Epoch 404/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0719 - val_loss: 0.0674\n",
      "Epoch 405/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0798 - val_loss: 0.0928\n",
      "Epoch 406/650\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 0.0811 - val_loss: 0.0957\n",
      "Epoch 407/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.0908 - val_loss: 0.0917\n",
      "Epoch 408/650\n",
      "3/3 [==============================] - 0s 12ms/step - loss: 0.0787 - val_loss: 0.0670\n",
      "Epoch 409/650\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.0716 - val_loss: 0.0637\n",
      "Epoch 410/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0725 - val_loss: 0.0688\n",
      "Epoch 411/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0717 - val_loss: 0.0717\n",
      "Epoch 412/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0735 - val_loss: 0.0991\n",
      "Epoch 413/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0835 - val_loss: 0.1052\n",
      "Epoch 414/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0993 - val_loss: 0.0958\n",
      "Epoch 415/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0829 - val_loss: 0.0795\n",
      "Epoch 416/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0773 - val_loss: 0.0806\n",
      "Epoch 417/650\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0793 - val_loss: 0.0714\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0714\n",
      "1/1 [==============================] - 0s 37ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " The predicted values (Lattice constants)\n",
      "[[6.207704  8.309064  5.780457 ]\n",
      " [5.72568   7.8735228 5.539663 ]\n",
      " [5.5966444 7.666996  5.3705606]\n",
      " [5.7735844 8.057046  5.724116 ]\n",
      " [5.6514635 7.7628736 5.4516163]\n",
      " [5.6368704 7.618869  5.2953415]\n",
      " [5.6018505 7.716814  5.4245634]\n",
      " [5.6453485 7.700329  5.383646 ]\n",
      " [6.4260883 7.9754753 5.2657013]\n",
      " [5.674617  7.773094  5.4514494]\n",
      " [5.6789827 7.830855  5.5149107]\n",
      " [6.170784  8.446334  5.95971  ]\n",
      " [5.371375  7.416997  5.2018557]\n",
      " [5.360969  7.357772  5.1398015]\n",
      " [5.815357  8.096269  5.7475486]\n",
      " [5.586218  7.5844216 5.2820663]\n",
      " [5.72568   7.8735228 5.539663 ]\n",
      " [6.1088285 8.505891  6.0622005]\n",
      " [5.638216  7.5960126 5.2683225]\n",
      " [5.5980673 7.679818  5.3844137]]\n",
      "\n",
      " The mean absolute error (MAE)\n",
      "-0.07142883539199829\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow import keras\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# The seed is required for replication of results. Parameters defined.\n",
    "seed = 1234\n",
    "tolerance_value = 35\n",
    "n_epochs = 650\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, targets,\n",
    "                                                    random_state=seed,\n",
    "                                                    test_size=0.2)\n",
    "\n",
    "def build_model(n_hidden=2, n_neurons=375, learning_rate=1e-3, input_shape=[features.shape[1],]):\n",
    "    model = keras.Sequential()\n",
    "\n",
    "    model.add(keras.layers.InputLayer(input_shape=input_shape))\n",
    "    for layer in range(n_hidden):\n",
    "        model.add(keras.layers.Dense(n_neurons, activation=\"relu\", use_bias= False))\n",
    "        # model.add(keras.layers.LeakyReLU(alpha=0.2))\n",
    "    model.add(keras.layers.Dense(targets.shape[1]))\n",
    "\n",
    "    model.compile(loss=\"mae\", optimizer=keras.optimizers.Adam(lr=learning_rate))\n",
    "\n",
    "    return model\n",
    "\n",
    "# The wrapper is neccessary for GridSearch later in this notebook\n",
    "keras_reg = keras.wrappers.scikit_learn.KerasRegressor(build_model)\n",
    "\n",
    "# The history dictionary is useful for 'loss' plot of the model. In this case, the 'loss' is mean absolute error \n",
    "history = keras_reg.fit(X_train, y_train, epochs=n_epochs,\n",
    "              validation_data=(X_test, y_test),\n",
    "              callbacks=[keras.callbacks.EarlyStopping(patience=tolerance_value)])\n",
    "\n",
    "# Test on the holdout data\n",
    "mae_test = keras_reg.score(X_test, y_test)\n",
    "y_pred = keras_reg.predict(X_test)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title(\"Model loss\")\n",
    "plt.ylabel(\"Loss (MAE)\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylim(top=0.6, bottom = 0)\n",
    "plt.legend([\"Train\", \"Test\"])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print (\"\\n The predicted values (Lattice constants)\")\n",
    "print (y_pred)\n",
    "print (\"\\n The mean absolute error (MAE)\")\n",
    "print (mae_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hybrid-bunch",
   "metadata": {
    "id": "atlantic-arrow"
   },
   "source": [
    "## GridSearch\n",
    "Grid search (GS) as the name suggests requires a grid made up by the hyperparamters. The alogorithm then runs the model over the grid and finds the best set of parameters for which the model perform best, i.e. low error (MSE, MAE, huber loss, etc in case of refression). The algorithm is heavy because it is performing a brute force approach. There are some techniques like Bayesian inference methods which are very quick in findig the hyperparameters. \n",
    "\n",
    "In the GS, I will set cross-validation option which results in more accurate loss metrics. Cross-validation is a statistical method of evaluating generalization performance that is more stable and thorough than using a split into a training and a test set.\n",
    "\n",
    "The parameter grid for my models are bulleted below.\n",
    "\n",
    "1.   number of hidden layers (n_hidden)\n",
    "2.   number of neural nodes in each hidden layers (n_neurons)\n",
    "3.   learning rate for Adam optimizer\n",
    "\n",
    "Since GS is a type of brute force approach I have employed a callback function which will terminate the GS once the patience level is crossed. This way I do not have to lose the computational power once the model loss starts saturating. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "metric-realtor",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Y2FNniocgLzQ",
    "outputId": "46dbc949-688d-4577-c759-1fedbe6a267b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-26 12:09:32.811790: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-26 12:09:32.811881: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-26 12:09:32.811878: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-26 12:09:32.812560: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-26 12:09:32.812964: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-26 12:09:32.813246: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-26 12:09:32.814793: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-26 12:09:32.814808: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 4ms/step - loss: 6.1916\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2291\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2657\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2395\n",
      "2/2 [==============================] - 1s 6ms/step - loss: 6.2400\n",
      "2/2 [==============================] - 1s 6ms/step - loss: 6.2587\n",
      "2/2 [==============================] - 1s 3ms/step - loss: 6.2869\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.3273\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "1/1 [==============================] - 0s 99ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 5ms/step - loss: 6.2227\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 6.1995\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 6.2811\n",
      "2/2 [==============================] - 1s 3ms/step - loss: 6.1989\n",
      "2/2 [==============================] - 1s 3ms/step - loss: 6.2435\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2615\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2140\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.1698\n",
      "1/1 [==============================] - 0s 74ms/step\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "1/1 [==============================] - 0s 74ms/step\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "1/1 [==============================] - 0s 81ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2568\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2053\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2189\n",
      "2/2 [==============================] - 1s 3ms/step - loss: 6.2286\n",
      "2/2 [==============================] - 1s 10ms/step - loss: 6.2197\n",
      "2/2 [==============================] - 1s 9ms/step - loss: 6.2061\n",
      "2/2 [==============================] - 1s 6ms/step - loss: 6.2541\n",
      "2/2 [==============================] - 1s 3ms/step - loss: 6.2338\n",
      "1/1 [==============================] - 0s 87ms/step\n",
      "1/1 [==============================] - 0s 72ms/step\n",
      "1/1 [==============================] - 0s 73ms/step\n",
      "1/1 [==============================] - 0s 73ms/step\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "1/1 [==============================] - 0s 85ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 21ms/step - loss: 6.2166\n",
      "2/2 [==============================] - 1s 15ms/step - loss: 6.2456\n",
      "2/2 [==============================] - 1s 13ms/step - loss: 6.2032\n",
      "2/2 [==============================] - 1s 10ms/step - loss: 6.2331\n",
      "2/2 [==============================] - 1s 7ms/step - loss: 6.2046\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2220\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2366\n",
      "2/2 [==============================] - 1s 7ms/step - loss: 6.2219\n",
      "1/1 [==============================] - 0s 82ms/step\n",
      "1/1 [==============================] - 0s 89ms/step\n",
      "1/1 [==============================] - 0s 88ms/step\n",
      "1/1 [==============================] - 0s 84ms/step\n",
      "1/1 [==============================] - 0s 88ms/step\n",
      "1/1 [==============================] - 0s 86ms/step\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "1/1 [==============================] - 0s 94ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_train_function.<locals>.train_function at 0x7fc9bd6ed3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_train_function.<locals>.train_function at 0x7f8abea8e3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_train_function.<locals>.train_function at 0x7ff6171543a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_train_function.<locals>.train_function at 0x7f77bcc8af70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_train_function.<locals>.train_function at 0x7fc9268f63a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_train_function.<locals>.train_function at 0x7feba67010d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_train_function.<locals>.train_function at 0x7fd386e0ff70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_train_function.<locals>.train_function at 0x7fa0242cd160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fc9bed15040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ff61ccb2160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f77b70a61f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f8abf217040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 3ms/step - loss: 6.2793\n",
      "2/2 [==============================] - 1s 3ms/step - loss: 6.2084\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2910\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2632\n",
      "2/2 [==============================] - 1s 12ms/step - loss: 6.2274\n",
      "2/2 [==============================] - 1s 14ms/step - loss: 6.2304\n",
      "2/2 [==============================] - 1s 7ms/step - loss: 6.1895\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2572\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "1/1 [==============================] - 0s 107ms/step\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "1/1 [==============================] - 0s 116ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fc926f65280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7feba7e8f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fa02d48f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fd385e8f430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 124ms/step\n",
      "1/1 [==============================] - 0s 126ms/step\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "1/1 [==============================] - 0s 141ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_train_function.<locals>.train_function at 0x7ff617154550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_train_function.<locals>.train_function at 0x7f77bcc8a5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_train_function.<locals>.train_function at 0x7fc9bd6ed280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_train_function.<locals>.train_function at 0x7f8abea8e550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_train_function.<locals>.train_function at 0x7fc9279af160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_train_function.<locals>.train_function at 0x7feba85790d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ff61de9c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 10ms/step - loss: 6.2467\n",
      "2/2 [==============================] - 1s 10ms/step - loss: 6.1970\n",
      "2/2 [==============================] - 1s 9ms/step - loss: 6.2344\n",
      "2/2 [==============================] - 1s 26ms/step - loss: 6.2499\n",
      "1/1 [==============================] - 0s 116ms/steps: 6.2294\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2320\n",
      "2/2 [==============================] - 1s 13ms/step - loss: 6.2287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f77bddf78b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_train_function.<locals>.train_function at 0x7fd387ed6310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fc9bef2d700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_train_function.<locals>.train_function at 0x7fa02d07f160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 155ms/step\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2486\n",
      "1/1 [==============================] - 0s 205ms/step\n",
      "2/2 [==============================] - 1s 10ms/step - loss: 6.1899\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f8ac011f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fc9279af550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 421ms/step\n",
      "1/1 [==============================] - 0s 328ms/step\n",
      "1/1 [==============================] - 0s 257ms/step\n",
      "1/1 [==============================] - 0s 203ms/step\n",
      "1/1 [==============================] - 0s 201ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7feba85794c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fd387ed6ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fa02d07f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 7ms/step - loss: 6.2112\n",
      "2/2 [==============================] - 1s 6ms/step - loss: 6.2537\n",
      "2/2 [==============================] - 1s 19ms/step - loss: 6.2748\n",
      "2/2 [==============================] - 1s 3ms/step - loss: 6.2508\n",
      "2/2 [==============================] - 1s 7ms/step - loss: 6.2181\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2402\n",
      "2/2 [==============================] - 1s 6ms/step - loss: 6.2327\n",
      "1/1 [==============================] - 0s 147ms/step\n",
      "1/1 [==============================] - 0s 150ms/step\n",
      "1/1 [==============================] - 0s 116ms/step\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2725\n",
      "1/1 [==============================] - 0s 137ms/step\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "1/1 [==============================] - 0s 95ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 110ms/step\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2513\n",
      "2/2 [==============================] - 1s 3ms/step - loss: 6.2295\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2796\n",
      "2/2 [==============================] - 1s 10ms/step - loss: 6.2569\n",
      "1/1 [==============================] - 0s 106ms/step\n",
      "1/1 [==============================] - 0s 106ms/step\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2479\n",
      "2/2 [==============================] - 1s 9ms/step - loss: 6.2450\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2582\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2657\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "1/1 [==============================] - 0s 84ms/step\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "1/1 [==============================] - 0s 93ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n",
      "/Users/pratha/opt/anaconda3/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 16ms/step - loss: 6.2293\n",
      "2/2 [==============================] - 1s 6ms/step - loss: 6.2364\n",
      "2/2 [==============================] - 1s 8ms/step - loss: 6.2733\n",
      "2/2 [==============================] - 1s 6ms/step - loss: 6.2587\n",
      "1/1 [==============================] - 0s 145ms/step\n",
      "2/2 [==============================] - 1s 10ms/step - loss: 6.2634\n",
      "1/1 [==============================] - 0s 167ms/step\n",
      "1/1 [==============================] - 0s 190ms/step\n",
      "2/2 [==============================] - 1s 4ms/step - loss: 6.2270\n",
      "2/2 [==============================] - 1s 7ms/step - loss: 6.2554\n",
      "2/2 [==============================] - 1s 5ms/step - loss: 6.2763\n",
      "1/1 [==============================] - 0s 144ms/step\n",
      "1/1 [==============================] - 0s 104ms/step\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 6.2048\n",
      "Best: -6.134137 using {'learning_rate': 0.0005, 'n_hidden': 3, 'n_neurons': 450}\n",
      "mean: -6.195307, std: (0.050007) parameters: {'learning_rate': 0.0005, 'n_hidden': 2, 'n_neurons': 350}\n",
      "mean: -6.180418, std: (0.028170) parameters: {'learning_rate': 0.0005, 'n_hidden': 2, 'n_neurons': 400}\n",
      "mean: -6.148707, std: (0.059851) parameters: {'learning_rate': 0.0005, 'n_hidden': 2, 'n_neurons': 450}\n",
      "mean: -6.139778, std: (0.019154) parameters: {'learning_rate': 0.0005, 'n_hidden': 3, 'n_neurons': 350}\n",
      "mean: -6.147486, std: (0.045773) parameters: {'learning_rate': 0.0005, 'n_hidden': 3, 'n_neurons': 400}\n",
      "mean: -6.134137, std: (0.043028) parameters: {'learning_rate': 0.0005, 'n_hidden': 3, 'n_neurons': 450}\n",
      "mean: -6.154708, std: (0.036588) parameters: {'learning_rate': 0.0005, 'n_hidden': 4, 'n_neurons': 350}\n",
      "mean: -6.142448, std: (0.049117) parameters: {'learning_rate': 0.0005, 'n_hidden': 4, 'n_neurons': 400}\n",
      "mean: -6.143894, std: (0.033279) parameters: {'learning_rate': 0.0005, 'n_hidden': 4, 'n_neurons': 450}\n",
      "mean: -6.254874, std: (0.038808) parameters: {'learning_rate': 5e-05, 'n_hidden': 2, 'n_neurons': 350}\n",
      "mean: -6.225207, std: (0.041494) parameters: {'learning_rate': 5e-05, 'n_hidden': 2, 'n_neurons': 400}\n",
      "mean: -6.217188, std: (0.037243) parameters: {'learning_rate': 5e-05, 'n_hidden': 2, 'n_neurons': 450}\n",
      "mean: -6.238644, std: (0.045350) parameters: {'learning_rate': 5e-05, 'n_hidden': 3, 'n_neurons': 350}\n",
      "mean: -6.231530, std: (0.045448) parameters: {'learning_rate': 5e-05, 'n_hidden': 3, 'n_neurons': 400}\n",
      "mean: -6.243791, std: (0.052446) parameters: {'learning_rate': 5e-05, 'n_hidden': 3, 'n_neurons': 450}\n",
      "mean: -6.245765, std: (0.050061) parameters: {'learning_rate': 5e-05, 'n_hidden': 4, 'n_neurons': 350}\n",
      "mean: -6.241878, std: (0.037976) parameters: {'learning_rate': 5e-05, 'n_hidden': 4, 'n_neurons': 400}\n",
      "mean: -6.245525, std: (0.038725) parameters: {'learning_rate': 5e-05, 'n_hidden': 4, 'n_neurons': 450}\n"
     ]
    }
   ],
   "source": [
    "# GridSearch for the ANN\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_absolute_error, make_scorer\n",
    "\n",
    "# param_distribs = {\n",
    "#     \"n_hidden\": [1, 2, 3, 4],\n",
    "#     \"n_neurons\": [900, 1100, 1300, 1500],\n",
    "#     \"learning_rate\": [1e-2, 1e-3],\n",
    "# }\n",
    "\n",
    "tolerance_value = 25\n",
    "\n",
    "param_distribs = {\n",
    "    \"n_hidden\": [2, 3, 4],\n",
    "  # \"n_neurons\": [5500, 5700, 5900],\n",
    "    # \"n_neurons\": [350, 450, 550],\n",
    "    \"n_neurons\": [350, 400, 450],\n",
    "    \"learning_rate\": [5*1e-4, 5*1e-5],\n",
    "}\n",
    "\n",
    "search_cv = GridSearchCV(estimator= keras_reg,\n",
    "                         param_grid= param_distribs, \n",
    "                         cv= 4, \n",
    "                         #scoring= make_scorer(mean_absolute_error, greater_is_better=True),\n",
    "                         scoring ='neg_mean_absolute_error',\n",
    "                         #refit=False,\n",
    "                         n_jobs= -1)\n",
    "\n",
    "\n",
    "# Best estimator is selected for further proceedings\n",
    "grid_result = search_cv.fit( X_train, y_train)\n",
    "#                   validation_data=(X_test, y_test),\n",
    "#                   callbacks=[keras.callbacks.EarlyStopping(monitor='loss', patience=tolerance_value)])\n",
    "\n",
    "\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"mean: %f, std: (%f) parameters: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "million-worse",
   "metadata": {},
   "source": [
    "The result from GS is in. And the quick look can be done in following bullets\n",
    "- number of hidden layers (n_hidden) = 2\n",
    "- number of neural nodes in each hidden layers (n_neurons) = 450\n",
    "- learning rate for Adam optimizer = 0.0005\n",
    "\n",
    "I will now extract the best performing model from the GS object. This model is already set up with the set of hyperparameters for which the loss was minimium. The outcome of the model is same as before and is appended to the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "federal-algorithm",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "History object\n",
      "\n",
      "Epoch 1/650\n",
      "3/3 [==============================] - 0s 69ms/step - loss: 6.2245 - val_loss: 6.1692\n",
      "Epoch 2/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 6.0552 - val_loss: 5.9913\n",
      "Epoch 3/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 5.8655 - val_loss: 5.7602\n",
      "Epoch 4/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 5.6121 - val_loss: 5.4387\n",
      "Epoch 5/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 5.2585 - val_loss: 4.9879\n",
      "Epoch 6/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 4.7639 - val_loss: 4.3624\n",
      "Epoch 7/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 4.0808 - val_loss: 3.5096\n",
      "Epoch 8/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 3.1539 - val_loss: 2.3642\n",
      "Epoch 9/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 1.9149 - val_loss: 1.1017\n",
      "Epoch 10/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 1.0994 - val_loss: 1.2016\n",
      "Epoch 11/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 1.2021 - val_loss: 1.2688\n",
      "Epoch 12/650\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 1.2731 - val_loss: 0.8220\n",
      "Epoch 13/650\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 0.6514 - val_loss: 0.2836\n",
      "Epoch 14/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.3769 - val_loss: 0.7394\n",
      "Epoch 15/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.6446 - val_loss: 0.5418\n",
      "Epoch 16/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.4241 - val_loss: 0.3606\n",
      "Epoch 17/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.3777 - val_loss: 0.4370\n",
      "Epoch 18/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.4334 - val_loss: 0.2599\n",
      "Epoch 19/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.2207 - val_loss: 0.3554\n",
      "Epoch 20/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.2853 - val_loss: 0.2774\n",
      "Epoch 21/650\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 0.2007 - val_loss: 0.2587\n",
      "Epoch 22/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.2536 - val_loss: 0.2246\n",
      "Epoch 23/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.2020 - val_loss: 0.3146\n",
      "Epoch 24/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.2282 - val_loss: 0.2247\n",
      "Epoch 25/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.2026 - val_loss: 0.2247\n",
      "Epoch 26/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1924 - val_loss: 0.2519\n",
      "Epoch 27/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1912 - val_loss: 0.2383\n",
      "Epoch 28/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1746 - val_loss: 0.2146\n",
      "Epoch 29/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1823 - val_loss: 0.2190\n",
      "Epoch 30/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1703 - val_loss: 0.2396\n",
      "Epoch 31/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1759 - val_loss: 0.2213\n",
      "Epoch 32/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1722 - val_loss: 0.2161\n",
      "Epoch 33/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1770 - val_loss: 0.2245\n",
      "Epoch 34/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1683 - val_loss: 0.2170\n",
      "Epoch 35/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1661 - val_loss: 0.2073\n",
      "Epoch 36/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1668 - val_loss: 0.2105\n",
      "Epoch 37/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1624 - val_loss: 0.2199\n",
      "Epoch 38/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1698 - val_loss: 0.2053\n",
      "Epoch 39/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1664 - val_loss: 0.2011\n",
      "Epoch 40/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1633 - val_loss: 0.2262\n",
      "Epoch 41/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1679 - val_loss: 0.2007\n",
      "Epoch 42/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1595 - val_loss: 0.1966\n",
      "Epoch 43/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1597 - val_loss: 0.2070\n",
      "Epoch 44/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1580 - val_loss: 0.2068\n",
      "Epoch 45/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1561 - val_loss: 0.2038\n",
      "Epoch 46/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1576 - val_loss: 0.1946\n",
      "Epoch 47/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1536 - val_loss: 0.1916\n",
      "Epoch 48/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1527 - val_loss: 0.1969\n",
      "Epoch 49/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1515 - val_loss: 0.1942\n",
      "Epoch 50/650\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 0.1549 - val_loss: 0.1916\n",
      "Epoch 51/650\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 0.1517 - val_loss: 0.1877\n",
      "Epoch 52/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1488 - val_loss: 0.1842\n",
      "Epoch 53/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1478 - val_loss: 0.1958\n",
      "Epoch 54/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1508 - val_loss: 0.1846\n",
      "Epoch 55/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1463 - val_loss: 0.1924\n",
      "Epoch 56/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1468 - val_loss: 0.1794\n",
      "Epoch 57/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1441 - val_loss: 0.1788\n",
      "Epoch 58/650\n",
      "3/3 [==============================] - 0s 16ms/step - loss: 0.1430 - val_loss: 0.1860\n",
      "Epoch 59/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1446 - val_loss: 0.1758\n",
      "Epoch 60/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1496 - val_loss: 0.1743\n",
      "Epoch 61/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1393 - val_loss: 0.1915\n",
      "Epoch 62/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1459 - val_loss: 0.1713\n",
      "Epoch 63/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1392 - val_loss: 0.1853\n",
      "Epoch 64/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1387 - val_loss: 0.1688\n",
      "Epoch 65/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1411 - val_loss: 0.1785\n",
      "Epoch 66/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1481 - val_loss: 0.1657\n",
      "Epoch 67/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1377 - val_loss: 0.1649\n",
      "Epoch 68/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1381 - val_loss: 0.1738\n",
      "Epoch 69/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1345 - val_loss: 0.1614\n",
      "Epoch 70/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1336 - val_loss: 0.1610\n",
      "Epoch 71/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1299 - val_loss: 0.1588\n",
      "Epoch 72/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1289 - val_loss: 0.1609\n",
      "Epoch 73/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1289 - val_loss: 0.1556\n",
      "Epoch 74/650\n",
      "3/3 [==============================] - 0s 15ms/step - loss: 0.1283 - val_loss: 0.1596\n",
      "Epoch 75/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1332 - val_loss: 0.1529\n",
      "Epoch 76/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1356 - val_loss: 0.1524\n",
      "Epoch 77/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1256 - val_loss: 0.1507\n",
      "Epoch 78/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1254 - val_loss: 0.1479\n",
      "Epoch 79/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1261 - val_loss: 0.1547\n",
      "Epoch 80/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1242 - val_loss: 0.1508\n",
      "Epoch 81/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1312 - val_loss: 0.1556\n",
      "Epoch 82/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1329 - val_loss: 0.1425\n",
      "Epoch 83/650\n",
      "3/3 [==============================] - 0s 13ms/step - loss: 0.1370 - val_loss: 0.1456\n",
      "Epoch 84/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1346 - val_loss: 0.1410\n",
      "Epoch 85/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1279 - val_loss: 0.1382\n",
      "Epoch 86/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1322 - val_loss: 0.1443\n",
      "Epoch 87/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1257 - val_loss: 0.1436\n",
      "Epoch 88/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1340 - val_loss: 0.1495\n",
      "Epoch 89/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1221 - val_loss: 0.1369\n",
      "Epoch 90/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1199 - val_loss: 0.1307\n",
      "Epoch 91/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1147 - val_loss: 0.1365\n",
      "Epoch 92/650\n",
      "3/3 [==============================] - 0s 14ms/step - loss: 0.1144 - val_loss: 0.1403\n",
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     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Best Model's Parameters\n",
      "{'learning_rate': 0.0005, 'n_hidden': 3, 'n_neurons': 450, 'build_fn': <function build_model at 0x7f7e52d54c10>}\n",
      "\n",
      " The mean absolute error (MAE)\n",
      "1/1 [==============================] - 0s 28ms/step - loss: 0.0540\n",
      "-0.05396534129977226\n",
      "\n",
      " The predicted values (Lattice constants)\n",
      "1/1 [==============================] - 0s 70ms/step\n",
      "[[6.053804  8.202547  5.7195916]\n",
      " [5.685912  7.8587174 5.561284 ]\n",
      " [5.5331125 7.6223245 5.381559 ]\n",
      " [5.7740006 8.072877  5.7593474]\n",
      " [5.5955563 7.7251863 5.462649 ]\n",
      " [5.5520134 7.5732355 5.308004 ]\n",
      " [5.549744  7.6771283 5.4365315]\n",
      " [5.574623  7.6555123 5.392587 ]\n",
      " [6.3184595 8.000311  5.2649827]\n",
      " [5.6143064 7.7353144 5.4617195]\n",
      " [5.6343536 7.8024015 5.5291724]\n",
      " [6.1476912 8.481462  5.9928384]\n",
      " [5.333041  7.38353   5.232565 ]\n",
      " [5.312951  7.324784  5.1748986]\n",
      " [5.8259983 8.130955  5.793334 ]\n",
      " [5.5072613 7.536712  5.295362 ]\n",
      " [5.685912  7.8587174 5.561284 ]\n",
      " [6.1568503 8.60226   6.1329145]\n",
      " [5.552518  7.5573897 5.2879257]\n",
      " [5.537159  7.6359286 5.395278 ]]\n"
     ]
    }
   ],
   "source": [
    "best_model = grid_result.best_estimator_\n",
    "\n",
    "print (\"\\nHistory object\\n\")\n",
    "best_model_history = best_model.fit(X_train, y_train, epochs=n_epochs,\n",
    "              validation_data=(X_test, y_test),\n",
    "              callbacks=[keras.callbacks.EarlyStopping(patience=tolerance_value)])\n",
    "              \n",
    "plt.figure()\n",
    "plt.plot(best_model_history.history['loss'])\n",
    "plt.plot(best_model_history.history['val_loss'])\n",
    "plt.title(\"Model loss\")\n",
    "plt.ylabel(\"Loss (MAE)\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Train\", \"Test\"])\n",
    "plt.show()\n",
    "\n",
    "print (\"\\n Best Model's Parameters\")\n",
    "print (best_model.get_params())\n",
    "print (\"\\n The mean absolute error (MAE)\")\n",
    "print (best_model.score(X_test, y_test))\n",
    "print (\"\\n The predicted values (Lattice constants)\")\n",
    "print (best_model.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "european-vulnerability",
   "metadata": {
    "id": "PyWxHUQn92zG"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "In this notebook too I deployed the ANN and found was successful in predicting the lattice constants with the mean absolute error around -0.05. The GridSearch did not perform well I would have expected it do. However, it along a few techniques like feature scaling and the commented out linear model is next task in my schedule. \n",
    "\n",
    "This notebook could not be amongst the best performer. However, especially after building an ANN I am motivated to work further on Machine Learning domain. I have all the incentives to move ahead. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comparable-township",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1] Chonghe, Li, et al. \"Prediction of lattice constant in perovskites of GdFeO3 structure.\" Journal of Physics and Chemistry of Solids 64.11 (2003): 2147-2156.\n",
    "\n",
    "[2] Alade, Ibrahim Olanrewaju, Ismail Adewale Olumegbon, and Aliyu Bagudu. \"Lattice constant prediction of A2XY6 cubic crystals (A= K, Cs, Rb, TI; X= tetravalent cation; Y= F, Cl, Br, I) using computational intelligence approach.\" Journal of Applied Physics 127.1 (2020): 015303.\n",
    "\n",
    "[3] Jiang, L. Q., et al. \"Prediction of lattice constant in cubic perovskites.\" Journal of Physics and Chemistry of Solids 67.7 (2006): 1531-1536.\n",
    "\n",
    "[4] “Introduction to Machine Learning with Python, A Guide for Data Scientists\", by Andreas C. Müller and Sarah Guido\n",
    "\n",
    "[5] “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow\", by Aurélien Géron\n",
    "\n",
    "[6] \"The Hundred-Page Machine Learning Book\", by Andriy Burkov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "general-magnet",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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