Iris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import some data to play with\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and validation sets\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1)\n",
    "\n",
    "# Convert the data to PyTorch tensors\n",
    "X_train_tensor = torch.from_numpy(X_train).float()\n",
    "y_train_tensor = torch.from_numpy(y_train).float()\n",
    "X_val_tensor = torch.from_numpy(X_val).float()\n",
    "y_val_tensor = torch.from_numpy(y_val).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(135, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train_tensor.shape[0], X_val_tensor.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the MLP architecture\n",
    "class Iris_MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Iris_MLP, self).__init__()\n",
    "        self.ly1 = nn.Linear(4, 10)\n",
    "        self.ly2 = nn.Linear(10, 10)\n",
    "        self.final = nn.Linear(10, 1)\n",
    "        \n",
    "        self.sig = nn.Sigmoid()\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.ly1(x)\n",
    "        out = self.sig(out)\n",
    "        out = self.ly2(out)\n",
    "        out = self.sig(out)\n",
    "        out = self.final(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of the MLP model\n",
    "model = Iris_MLP()\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss before training 1.58488929271698\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "y_pred = model(X_val_tensor)\n",
    "before_train = criterion(y_pred.squeeze(), y_val_tensor)\n",
    "print('Test loss before training' , before_train.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: train loss: 1.735119104385376\n",
      "Epoch 1: train loss: 1.2892640829086304\n",
      "Epoch 2: train loss: 0.9648243188858032\n",
      "Epoch 3: train loss: 0.7640462517738342\n",
      "Epoch 4: train loss: 0.6804783940315247\n",
      "Epoch 5: train loss: 0.6898640990257263\n",
      "Epoch 6: train loss: 0.7447771430015564\n",
      "Epoch 7: train loss: 0.7912411093711853\n",
      "Epoch 8: train loss: 0.7982454895973206\n",
      "Epoch 9: train loss: 0.7644335627555847\n",
      "Epoch 10: train loss: 0.7050159573554993\n",
      "Epoch 11: train loss: 0.6392527222633362\n",
      "Epoch 12: train loss: 0.583544135093689\n",
      "Epoch 13: train loss: 0.5477458834648132\n",
      "Epoch 14: train loss: 0.5328104496002197\n",
      "Epoch 15: train loss: 0.530807375907898\n",
      "Epoch 16: train loss: 0.5295795798301697\n",
      "Epoch 17: train loss: 0.519149661064148\n",
      "Epoch 18: train loss: 0.49507445096969604\n",
      "Epoch 19: train loss: 0.4596394896507263\n",
      "Epoch 20: train loss: 0.41931605339050293\n",
      "Epoch 21: train loss: 0.38069015741348267\n",
      "Epoch 22: train loss: 0.3472769856452942\n",
      "Epoch 23: train loss: 0.3186226189136505\n",
      "Epoch 24: train loss: 0.2916875183582306\n",
      "Epoch 25: train loss: 0.2632227838039398\n",
      "Epoch 26: train loss: 0.23159880936145782\n",
      "Epoch 27: train loss: 0.19761055707931519\n",
      "Epoch 28: train loss: 0.16439223289489746\n",
      "Epoch 29: train loss: 0.13654601573944092\n",
      "Epoch 30: train loss: 0.11738109588623047\n",
      "Epoch 31: train loss: 0.10397963970899582\n",
      "Epoch 32: train loss: 0.08982686698436737\n",
      "Epoch 33: train loss: 0.0736234188079834\n",
      "Epoch 34: train loss: 0.06098685786128044\n",
      "Epoch 35: train loss: 0.05550912022590637\n",
      "Epoch 36: train loss: 0.053431134670972824\n",
      "Epoch 37: train loss: 0.04928036034107208\n",
      "Epoch 38: train loss: 0.0435524508357048\n",
      "Epoch 39: train loss: 0.041906606405973434\n",
      "Epoch 40: train loss: 0.04441295191645622\n",
      "Epoch 41: train loss: 0.04313395544886589\n",
      "Epoch 42: train loss: 0.039261020720005035\n",
      "Epoch 43: train loss: 0.039355840533971786\n",
      "Epoch 44: train loss: 0.04046628624200821\n",
      "Epoch 45: train loss: 0.03799520432949066\n",
      "Epoch 46: train loss: 0.035630326718091965\n",
      "Epoch 47: train loss: 0.03646610304713249\n",
      "Epoch 48: train loss: 0.035911817103624344\n",
      "Epoch 49: train loss: 0.033344365656375885\n",
      "Epoch 50: train loss: 0.03315286338329315\n",
      "Epoch 51: train loss: 0.03390787914395332\n",
      "Epoch 52: train loss: 0.032852016389369965\n",
      "Epoch 53: train loss: 0.03242548927664757\n",
      "Epoch 54: train loss: 0.03362203389406204\n",
      "Epoch 55: train loss: 0.03369229659438133\n",
      "Epoch 56: train loss: 0.03317059949040413\n",
      "Epoch 57: train loss: 0.03388803452253342\n",
      "Epoch 58: train loss: 0.03430180251598358\n",
      "Epoch 59: train loss: 0.03366381675004959\n",
      "Epoch 60: train loss: 0.03358587622642517\n",
      "Epoch 61: train loss: 0.03381069377064705\n",
      "Epoch 62: train loss: 0.03319308161735535\n",
      "Epoch 63: train loss: 0.03265949338674545\n",
      "Epoch 64: train loss: 0.03267302364110947\n",
      "Epoch 65: train loss: 0.03228812664747238\n",
      "Epoch 66: train loss: 0.031720854341983795\n",
      "Epoch 67: train loss: 0.03166661411523819\n",
      "Epoch 68: train loss: 0.0315825417637825\n",
      "Epoch 69: train loss: 0.031224265694618225\n",
      "Epoch 70: train loss: 0.031168704852461815\n",
      "Epoch 71: train loss: 0.03125891461968422\n",
      "Epoch 72: train loss: 0.031092766672372818\n",
      "Epoch 73: train loss: 0.030991243198513985\n",
      "Epoch 74: train loss: 0.031075427308678627\n",
      "Epoch 75: train loss: 0.03099752403795719\n",
      "Epoch 76: train loss: 0.030848979949951172\n",
      "Epoch 77: train loss: 0.03085305541753769\n",
      "Epoch 78: train loss: 0.030807318165898323\n",
      "Epoch 79: train loss: 0.030650891363620758\n",
      "Epoch 80: train loss: 0.03059173934161663\n",
      "Epoch 81: train loss: 0.03057253733277321\n",
      "Epoch 82: train loss: 0.030464941635727882\n",
      "Epoch 83: train loss: 0.030397316440939903\n",
      "Epoch 84: train loss: 0.030400244519114494\n",
      "Epoch 85: train loss: 0.030350495129823685\n",
      "Epoch 86: train loss: 0.03029346466064453\n",
      "Epoch 87: train loss: 0.030298249796032906\n",
      "Epoch 88: train loss: 0.030279085040092468\n",
      "Epoch 89: train loss: 0.030227109789848328\n",
      "Epoch 90: train loss: 0.03021402284502983\n",
      "Epoch 91: train loss: 0.030198050662875175\n",
      "Epoch 92: train loss: 0.03014681115746498\n",
      "Epoch 93: train loss: 0.030116857960820198\n",
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      "Epoch 95: train loss: 0.030054841190576553\n",
      "Epoch 96: train loss: 0.030021851882338524\n",
      "Epoch 97: train loss: 0.03000757098197937\n",
      "Epoch 98: train loss: 0.029978223145008087\n",
      "Epoch 99: train loss: 0.029953034594655037\n",
      "Epoch 100: train loss: 0.02994559332728386\n",
      "Epoch 101: train loss: 0.02992876246571541\n",
      "Epoch 102: train loss: 0.029910458251833916\n",
      "Epoch 103: train loss: 0.029905205592513084\n",
      "Epoch 104: train loss: 0.029893511906266212\n",
      "Epoch 105: train loss: 0.02987733855843544\n",
      "Epoch 106: train loss: 0.02987000159919262\n",
      "Epoch 107: train loss: 0.029858486726880074\n",
      "Epoch 108: train loss: 0.029842376708984375\n",
      "Epoch 109: train loss: 0.02983299270272255\n",
      "Epoch 110: train loss: 0.029821738600730896\n",
      "Epoch 111: train loss: 0.02980717644095421\n",
      "Epoch 112: train loss: 0.029798144474625587\n",
      "Epoch 113: train loss: 0.029788611456751823\n",
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      "Epoch 115: train loss: 0.02976907044649124\n",
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      "Epoch 117: train loss: 0.029751215130090714\n",
      "Epoch 118: train loss: 0.029744144529104233\n",
      "Epoch 119: train loss: 0.02973662130534649\n",
      "Epoch 120: train loss: 0.029727479442954063\n",
      "Epoch 121: train loss: 0.029720356687903404\n",
      "Epoch 122: train loss: 0.029712814837694168\n",
      "Epoch 123: train loss: 0.029704248532652855\n",
      "Epoch 124: train loss: 0.02969735488295555\n",
      "Epoch 125: train loss: 0.029690265655517578\n",
      "Epoch 126: train loss: 0.029682686552405357\n",
      "Epoch 127: train loss: 0.029676528647542\n",
      "Epoch 128: train loss: 0.029670221731066704\n",
      "Epoch 129: train loss: 0.029663696885108948\n",
      "Epoch 130: train loss: 0.02965821884572506\n",
      "Epoch 131: train loss: 0.02965247631072998\n",
      "Epoch 132: train loss: 0.029646608978509903\n",
      "Epoch 133: train loss: 0.029641440138220787\n",
      "Epoch 134: train loss: 0.029635921120643616\n",
      "Epoch 135: train loss: 0.0296303890645504\n",
      "Epoch 136: train loss: 0.02962535060942173\n",
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      "Epoch 140: train loss: 0.02960497885942459\n",
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      "Epoch 150: train loss: 0.02956107072532177\n",
      "Epoch 151: train loss: 0.029557012021541595\n",
      "Epoch 152: train loss: 0.029552984982728958\n",
      "Epoch 153: train loss: 0.029549092054367065\n",
      "Epoch 154: train loss: 0.029545193538069725\n",
      "Epoch 155: train loss: 0.0295413751155138\n",
      "Epoch 156: train loss: 0.029537631198763847\n",
      "Epoch 157: train loss: 0.029533883556723595\n",
      "Epoch 158: train loss: 0.02953021228313446\n",
      "Epoch 159: train loss: 0.029526570811867714\n",
      "Epoch 160: train loss: 0.029522934928536415\n",
      "Epoch 161: train loss: 0.029519373551011086\n",
      "Epoch 162: train loss: 0.02951582334935665\n",
      "Epoch 163: train loss: 0.02951228804886341\n",
      "Epoch 164: train loss: 0.029508816078305244\n",
      "Epoch 165: train loss: 0.029505355283617973\n",
      "Epoch 166: train loss: 0.029501931741833687\n",
      "Epoch 167: train loss: 0.029498538002371788\n",
      "Epoch 168: train loss: 0.029495153576135635\n",
      "Epoch 169: train loss: 0.029491819441318512\n",
      "Epoch 170: train loss: 0.02948850207030773\n",
      "Epoch 171: train loss: 0.029485201463103294\n",
      "Epoch 172: train loss: 0.029481923207640648\n",
      "Epoch 173: train loss: 0.029478667303919792\n",
      "Epoch 174: train loss: 0.029475416988134384\n",
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      "Epoch 179: train loss: 0.029459483921527863\n",
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      "Epoch 200: train loss: 0.029395144432783127\n",
      "Epoch 201: train loss: 0.029392125084996223\n",
      "Epoch 202: train loss: 0.02938910201191902\n",
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      "Epoch 225: train loss: 0.029318612068891525\n",
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      "Epoch 255: train loss: 0.029219821095466614\n",
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      "Epoch 264: train loss: 0.02918786182999611\n",
      "Epoch 265: train loss: 0.029184218496084213\n",
      "Epoch 266: train loss: 0.029180575162172318\n",
      "Epoch 267: train loss: 0.029176902025938034\n",
      "Epoch 268: train loss: 0.029173225164413452\n",
      "Epoch 269: train loss: 0.02916952408850193\n",
      "Epoch 270: train loss: 0.029165808111429214\n",
      "Epoch 271: train loss: 0.029162079095840454\n",
      "Epoch 272: train loss: 0.029158318415284157\n",
      "Epoch 273: train loss: 0.029154544696211815\n",
      "Epoch 274: train loss: 0.02915075607597828\n",
      "Epoch 275: train loss: 0.0291469506919384\n",
      "Epoch 276: train loss: 0.029143115505576134\n",
      "Epoch 277: train loss: 0.02913927473127842\n",
      "Epoch 278: train loss: 0.029135404154658318\n",
      "Epoch 279: train loss: 0.02913152240216732\n",
      "Epoch 280: train loss: 0.02912762388586998\n",
      "Epoch 281: train loss: 0.029123693704605103\n",
      "Epoch 282: train loss: 0.029119759798049927\n",
      "Epoch 283: train loss: 0.029115786775946617\n",
      "Epoch 284: train loss: 0.029111815616488457\n",
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      "Epoch 286: train loss: 0.029103770852088928\n",
      "Epoch 287: train loss: 0.0290997251868248\n",
      "Epoch 288: train loss: 0.029095660895109177\n",
      "Epoch 289: train loss: 0.029091568663716316\n",
      "Epoch 290: train loss: 0.029087452217936516\n",
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      "Epoch 4801: train loss: 1.3853798918717075e-05\n",
      "Epoch 4802: train loss: 1.4844343240838498e-05\n",
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      "Epoch 4808: train loss: 3.2945834391284734e-05\n",
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      "Epoch 4814: train loss: 7.983912655618042e-05\n",
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      "Epoch 4830: train loss: 0.00010049273259937763\n",
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      "Epoch 4832: train loss: 0.00014154530072119087\n",
      "Epoch 4833: train loss: 0.00015587417874485254\n",
      "Epoch 4834: train loss: 0.00012879721180070192\n",
      "Epoch 4835: train loss: 9.226622205460444e-05\n",
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      "Epoch 4841: train loss: 6.622930231969804e-05\n",
      "Epoch 4842: train loss: 6.229356222320348e-05\n",
      "Epoch 4843: train loss: 4.4244021410122514e-05\n",
      "Epoch 4844: train loss: 2.684100400074385e-05\n",
      "Epoch 4845: train loss: 1.446086844225647e-05\n",
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      "Epoch 4848: train loss: 2.140614560630638e-05\n",
      "Epoch 4849: train loss: 3.433335950830951e-05\n",
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      "Epoch 4851: train loss: 7.134430052246898e-05\n",
      "Epoch 4852: train loss: 8.752102439757437e-05\n",
      "Epoch 4853: train loss: 0.00012232361768838018\n",
      "Epoch 4854: train loss: 0.0001275192480534315\n",
      "Epoch 4855: train loss: 0.00013927044346928596\n",
      "Epoch 4856: train loss: 0.00010856927838176489\n",
      "Epoch 4857: train loss: 6.87459614709951e-05\n",
      "Epoch 4858: train loss: 2.8955422749277204e-05\n",
      "Epoch 4859: train loss: 1.3084941201668698e-05\n",
      "Epoch 4860: train loss: 2.397717980784364e-05\n",
      "Epoch 4861: train loss: 4.6498069423250854e-05\n",
      "Epoch 4862: train loss: 6.217672489583492e-05\n",
      "Epoch 4863: train loss: 5.6843906349968165e-05\n",
      "Epoch 4864: train loss: 3.889694198733196e-05\n",
      "Epoch 4865: train loss: 1.960344161489047e-05\n",
      "Epoch 4866: train loss: 1.1119978807982989e-05\n",
      "Epoch 4867: train loss: 1.4353180631587747e-05\n",
      "Epoch 4868: train loss: 2.4961924282251857e-05\n",
      "Epoch 4869: train loss: 3.963888593716547e-05\n",
      "Epoch 4870: train loss: 4.921508298139088e-05\n",
      "Epoch 4871: train loss: 6.18855920038186e-05\n",
      "Epoch 4872: train loss: 6.30832146271132e-05\n",
      "Epoch 4873: train loss: 6.82025493006222e-05\n",
      "Epoch 4874: train loss: 6.037009006831795e-05\n",
      "Epoch 4875: train loss: 5.6941902585094795e-05\n",
      "Epoch 4876: train loss: 4.2377676436444744e-05\n",
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      "Epoch 4878: train loss: 1.7768743418855593e-05\n",
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      "Epoch 4880: train loss: 1.17354193207575e-05\n",
      "Epoch 4881: train loss: 1.6803051039460115e-05\n",
      "Epoch 4882: train loss: 2.2853380869491957e-05\n",
      "Epoch 4883: train loss: 2.5487039238214493e-05\n",
      "Epoch 4884: train loss: 2.4820743419695646e-05\n",
      "Epoch 4885: train loss: 2.044170287263114e-05\n",
      "Epoch 4886: train loss: 1.542379686725326e-05\n",
      "Epoch 4887: train loss: 1.1639168405963574e-05\n",
      "Epoch 4888: train loss: 1.0218762326985598e-05\n",
      "Epoch 4889: train loss: 1.084370887838304e-05\n",
      "Epoch 4890: train loss: 1.288472685700981e-05\n",
      "Epoch 4891: train loss: 1.5743198673590086e-05\n",
      "Epoch 4892: train loss: 1.8298544091521762e-05\n",
      "Epoch 4893: train loss: 2.101348218275234e-05\n",
      "Epoch 4894: train loss: 2.258501081087161e-05\n",
      "Epoch 4895: train loss: 2.469945866323542e-05\n",
      "Epoch 4896: train loss: 2.4983042749227025e-05\n",
      "Epoch 4897: train loss: 2.6134295694646426e-05\n",
      "Epoch 4898: train loss: 2.504775329725817e-05\n",
      "Epoch 4899: train loss: 2.4095508706523106e-05\n",
      "Epoch 4900: train loss: 2.1296342310961336e-05\n",
      "Epoch 4901: train loss: 1.8530477973399684e-05\n",
      "Epoch 4902: train loss: 1.520973728474928e-05\n",
      "Epoch 4903: train loss: 1.2603624782059342e-05\n",
      "Epoch 4904: train loss: 1.0810017556650564e-05\n",
      "Epoch 4905: train loss: 1.0070222742797341e-05\n",
      "Epoch 4906: train loss: 1.0274486157868523e-05\n",
      "Epoch 4907: train loss: 1.1100847586931195e-05\n",
      "Epoch 4908: train loss: 1.2191317182441708e-05\n",
      "Epoch 4909: train loss: 1.31994929688517e-05\n",
      "Epoch 4910: train loss: 1.411787343386095e-05\n",
      "Epoch 4911: train loss: 1.4592920706490986e-05\n",
      "Epoch 4912: train loss: 1.5045033251226414e-05\n",
      "Epoch 4913: train loss: 1.512525250291219e-05\n",
      "Epoch 4914: train loss: 1.5365018043667078e-05\n",
      "Epoch 4915: train loss: 1.5322006220230833e-05\n",
      "Epoch 4916: train loss: 1.5646022802684456e-05\n",
      "Epoch 4917: train loss: 1.5747149518574588e-05\n",
      "Epoch 4918: train loss: 1.628809332032688e-05\n",
      "Epoch 4919: train loss: 1.661619171500206e-05\n",
      "Epoch 4920: train loss: 1.74380220414605e-05\n",
      "Epoch 4921: train loss: 1.7874252080218866e-05\n",
      "Epoch 4922: train loss: 1.885246456367895e-05\n",
      "Epoch 4923: train loss: 1.9287625036668032e-05\n",
      "Epoch 4924: train loss: 2.0222654711687937e-05\n",
      "Epoch 4925: train loss: 2.0418821804923937e-05\n",
      "Epoch 4926: train loss: 2.1080690203234553e-05\n",
      "Epoch 4927: train loss: 2.0803592633455992e-05\n",
      "Epoch 4928: train loss: 2.093173134198878e-05\n",
      "Epoch 4929: train loss: 2.0197649064357392e-05\n",
      "Epoch 4930: train loss: 1.98547186300857e-05\n",
      "Epoch 4931: train loss: 1.8856295355362818e-05\n",
      "Epoch 4932: train loss: 1.8320815797778778e-05\n",
      "Epoch 4933: train loss: 1.7358946934109554e-05\n",
      "Epoch 4934: train loss: 1.6867363228811882e-05\n",
      "Epoch 4935: train loss: 1.617448651813902e-05\n",
      "Epoch 4936: train loss: 1.597090158611536e-05\n",
      "Epoch 4937: train loss: 1.567955951031763e-05\n",
      "Epoch 4938: train loss: 1.5951278328429908e-05\n",
      "Epoch 4939: train loss: 1.621043156774249e-05\n",
      "Epoch 4940: train loss: 1.7196489352500066e-05\n",
      "Epoch 4941: train loss: 1.82570620381739e-05\n",
      "Epoch 4942: train loss: 2.0508712623268366e-05\n",
      "Epoch 4943: train loss: 2.2878059098729864e-05\n",
      "Epoch 4944: train loss: 2.744581433944404e-05\n",
      "Epoch 4945: train loss: 3.199779530405067e-05\n",
      "Epoch 4946: train loss: 4.052082294947468e-05\n",
      "Epoch 4947: train loss: 4.7863475629128516e-05\n",
      "Epoch 4948: train loss: 6.145220686448738e-05\n",
      "Epoch 4949: train loss: 6.993121496634558e-05\n",
      "Epoch 4950: train loss: 8.502142009092495e-05\n",
      "Epoch 4951: train loss: 8.815427281660959e-05\n",
      "Epoch 4952: train loss: 9.355986549053341e-05\n",
      "Epoch 4953: train loss: 8.272127888631076e-05\n",
      "Epoch 4954: train loss: 7.002073834883049e-05\n",
      "Epoch 4955: train loss: 4.8664576752344146e-05\n",
      "Epoch 4956: train loss: 3.0045679523027502e-05\n",
      "Epoch 4957: train loss: 1.6189243979169987e-05\n",
      "Epoch 4958: train loss: 1.0535753062868025e-05\n",
      "Epoch 4959: train loss: 1.2330797289905604e-05\n",
      "Epoch 4960: train loss: 1.9110922949039377e-05\n",
      "Epoch 4961: train loss: 2.8599428333109245e-05\n",
      "Epoch 4962: train loss: 3.751947951968759e-05\n",
      "Epoch 4963: train loss: 4.8774632887216285e-05\n",
      "Epoch 4964: train loss: 5.631566091324203e-05\n",
      "Epoch 4965: train loss: 7.144307892303914e-05\n",
      "Epoch 4966: train loss: 7.930475112516433e-05\n",
      "Epoch 4967: train loss: 0.00010061751527246088\n",
      "Epoch 4968: train loss: 0.00010606532305246219\n",
      "Epoch 4969: train loss: 0.00012186969979666173\n",
      "Epoch 4970: train loss: 0.00011153597733937204\n",
      "Epoch 4971: train loss: 9.8706383141689e-05\n",
      "Epoch 4972: train loss: 6.743796257069334e-05\n",
      "Epoch 4973: train loss: 3.788853427977301e-05\n",
      "Epoch 4974: train loss: 1.668046934355516e-05\n",
      "Epoch 4975: train loss: 1.1390739018679596e-05\n",
      "Epoch 4976: train loss: 1.9918810721719638e-05\n",
      "Epoch 4977: train loss: 3.41278500854969e-05\n",
      "Epoch 4978: train loss: 4.6495479182340205e-05\n",
      "Epoch 4979: train loss: 4.939389691571705e-05\n",
      "Epoch 4980: train loss: 4.6661985834361985e-05\n",
      "Epoch 4981: train loss: 3.6483554140431806e-05\n",
      "Epoch 4982: train loss: 2.7249878257862292e-05\n",
      "Epoch 4983: train loss: 1.844244616222568e-05\n",
      "Epoch 4984: train loss: 1.3006014341954142e-05\n",
      "Epoch 4985: train loss: 1.0082390872412361e-05\n",
      "Epoch 4986: train loss: 9.360719559481367e-06\n",
      "Epoch 4987: train loss: 1.024244829750387e-05\n",
      "Epoch 4988: train loss: 1.2308571058383677e-05\n",
      "Epoch 4989: train loss: 1.559749034640845e-05\n",
      "Epoch 4990: train loss: 1.968167271115817e-05\n",
      "Epoch 4991: train loss: 2.5955232558771968e-05\n",
      "Epoch 4992: train loss: 3.207464760635048e-05\n",
      "Epoch 4993: train loss: 4.188177626929246e-05\n",
      "Epoch 4994: train loss: 4.8219546442851424e-05\n",
      "Epoch 4995: train loss: 5.754855374107137e-05\n",
      "Epoch 4996: train loss: 5.832100578118116e-05\n",
      "Epoch 4997: train loss: 5.855553536093794e-05\n",
      "Epoch 4998: train loss: 4.903012450085953e-05\n",
      "Epoch 4999: train loss: 3.8136771763674915e-05\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "model.train()\n",
    "epoch = 5000\n",
    "\n",
    "for epoch in range(epoch):\n",
    "    # Forward pass\n",
    "    y_pred = model(X_train_tensor)\n",
    "    # Compute Loss\n",
    "    loss = criterion(y_pred.squeeze(), y_train_tensor)\n",
    "   \n",
    "    print('Epoch {}: train loss: {}'.format(epoch, loss.item()))\n",
    "    \n",
    "    # Zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Backward pass\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss after training 0.06700544059276581\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "y_pred = model(X_val_tensor)\n",
    "after_train = criterion(y_pred.squeeze(), y_val_tensor)\n",
    "print('Test loss after training' , after_train.item())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run a test using predefined values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([5.7000, 2.6000, 3.5000, 1.0000]), 'versicolor')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train_tensor[5], iris.target_names[y_train_tensor[5].int().item()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0013], grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_input = X_train_tensor[5]\n",
    "\n",
    "model.eval()\n",
    "model(X_input)"
   ]
  }
 ],
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