HW1/HW1_SimFunc1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "05a1466d-cd82-471d-93a5-257db2e665b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xshape: torch.Size([300, 1]) \n",
      " Yshape: torch.Size([300, 1])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dinesh\\AppData\\Local\\Temp/ipykernel_5152/3457197851.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  x, y = torch.tensor(x, dtype=torch.float32), torch.tensor(y, dtype=torch.float32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "#1st Function\n",
    "#Creating the data\n",
    "\n",
    "torch.manual_seed(1) \n",
    "x = torch.unsqueeze(torch.linspace(-1, 1, 300), dim=1)  # x data (tensor), shape=(300, 1)\n",
    "y = (np.sin(5*np.pi*x))/(5*np.pi*x) #non-linear y function\n",
    "#y = (np.cos(7 * x * np.pi))\n",
    "print('Xshape:',x.shape,\"\\n Yshape:\",y.shape)\n",
    "\n",
    "#x, y = Variable(x), Variable(y) #converting data into variables as pytorch requirments\n",
    "x, y = torch.tensor(x, dtype=torch.float32), torch.tensor(y, dtype=torch.float32)\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(x, y, color = \"red\")\n",
    "plt.title('Function Plot')\n",
    "plt.xlabel('Independent varible')\n",
    "plt.ylabel('Dependent varible')\n",
    "plt.savefig('funcplt.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "n_samples, n_features = x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0cef9711-5343-4c34-a8f2-8f4408d2d6f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no of parameters in Model 1: 571\n",
      "\n",
      "Model 1 weight and bias before training\n",
      "\n",
      "hidden1.weight \t tensor([[ 0.5153],\n",
      "        [-0.4414],\n",
      "        [-0.1939],\n",
      "        [ 0.4694],\n",
      "        [-0.9414]])\n",
      "hidden1.bias \t tensor([ 0.5997, -0.2057,  0.5087,  0.1390, -0.1224])\n",
      "hidden2.weight \t tensor([[ 0.1240,  0.0221,  0.1633, -0.1743, -0.0326],\n",
      "        [-0.0403,  0.0648, -0.0018,  0.3909,  0.1392],\n",
      "        [-0.1665, -0.2701, -0.0750, -0.1929, -0.1433],\n",
      "        [ 0.0214,  0.2666,  0.2431, -0.4372,  0.2772],\n",
      "        [ 0.1249,  0.4242,  0.2952, -0.4075, -0.4252],\n",
      "        [-0.2157,  0.3927, -0.0745,  0.1914, -0.2078],\n",
      "        [ 0.4388, -0.1892,  0.3354,  0.0053, -0.2356],\n",
      "        [ 0.2299, -0.2374,  0.1315, -0.1291, -0.0490],\n",
      "        [-0.4299, -0.2132,  0.2427, -0.1087,  0.4454],\n",
      "        [ 0.3585, -0.0209, -0.2985,  0.2723,  0.1388]])\n",
      "hidden2.bias \t tensor([-0.2891,  0.2905,  0.2715,  0.3966, -0.2507, -0.0736, -0.0087,  0.0653,\n",
      "        -0.3394, -0.3174])\n",
      "hidden3.weight \t tensor([[ 0.1720, -0.0742,  0.1545,  0.0180,  0.1038,  0.0695,  0.1150,  0.1568,\n",
      "         -0.2929,  0.1592],\n",
      "        [-0.2223, -0.2386,  0.0192, -0.0539,  0.1857, -0.1831, -0.2811,  0.2301,\n",
      "         -0.0469,  0.1779],\n",
      "        [ 0.1017, -0.2371,  0.0635,  0.0760, -0.2117, -0.1500,  0.1078,  0.0566,\n",
      "         -0.1345, -0.0957],\n",
      "        [ 0.2896, -0.0585,  0.1783,  0.1369, -0.2044, -0.2689,  0.3035,  0.0165,\n",
      "          0.2168,  0.0655],\n",
      "        [ 0.1017,  0.2362,  0.2999, -0.2098,  0.0395,  0.2360,  0.2291,  0.1965,\n",
      "         -0.2289, -0.2277],\n",
      "        [-0.1912,  0.0397,  0.3152, -0.1998,  0.1685, -0.1750, -0.2973, -0.0672,\n",
      "          0.1822,  0.2936],\n",
      "        [-0.1964,  0.0686,  0.2729,  0.2095,  0.1971,  0.2247,  0.2000,  0.0817,\n",
      "         -0.2162, -0.2656],\n",
      "        [-0.1449, -0.0368, -0.1938,  0.1157,  0.0979, -0.0716,  0.1215,  0.1022,\n",
      "          0.1931,  0.2130],\n",
      "        [-0.1071,  0.3090, -0.0366, -0.0109, -0.2985, -0.2035, -0.1847, -0.1353,\n",
      "          0.2248, -0.1033],\n",
      "        [-0.2363,  0.1217,  0.1012,  0.2048, -0.1636,  0.0686, -0.1151, -0.0710,\n",
      "         -0.2520, -0.1441]])\n",
      "hidden3.bias \t tensor([-0.0968,  0.1352,  0.0578,  0.0781,  0.3156,  0.3082,  0.2157,  0.0101,\n",
      "        -0.2188,  0.2471])\n",
      "hidden4.weight \t tensor([[-0.0791, -0.0256, -0.2724, -0.0625, -0.2040,  0.2906, -0.2734, -0.2465,\n",
      "         -0.0107, -0.1710],\n",
      "        [ 0.1132, -0.1217, -0.1485,  0.0179,  0.2289, -0.2224,  0.1485,  0.2032,\n",
      "          0.3093, -0.2213],\n",
      "        [ 0.0766, -0.2338,  0.2700, -0.1227,  0.1905,  0.0094, -0.0246, -0.0101,\n",
      "          0.0538,  0.1491],\n",
      "        [ 0.0507,  0.0965, -0.2845,  0.2304,  0.2757,  0.2614,  0.2338, -0.2282,\n",
      "         -0.1172,  0.2788],\n",
      "        [-0.2409,  0.2869, -0.2487, -0.2228,  0.1546, -0.2272, -0.0724,  0.2301,\n",
      "          0.2505,  0.2991],\n",
      "        [-0.0642, -0.2458,  0.3114, -0.0674, -0.1301,  0.0771, -0.2211,  0.2079,\n",
      "          0.1982, -0.2509],\n",
      "        [-0.2597, -0.0277,  0.1328, -0.0092, -0.1604,  0.0072, -0.2972, -0.2235,\n",
      "         -0.2105,  0.2604],\n",
      "        [ 0.2788, -0.1074,  0.0142,  0.1410,  0.0379, -0.1584,  0.1824,  0.1944,\n",
      "         -0.0183, -0.0389],\n",
      "        [ 0.2874,  0.2764, -0.1793,  0.3094,  0.0783, -0.2101,  0.1731, -0.2361,\n",
      "          0.2922, -0.2033],\n",
      "        [ 0.0894,  0.0963,  0.0752,  0.2623, -0.1313, -0.1335, -0.2740, -0.0130,\n",
      "         -0.1498,  0.0127]])\n",
      "hidden4.bias \t tensor([-0.0648,  0.1049,  0.2736,  0.0933, -0.1019, -0.1552, -0.2758,  0.2661,\n",
      "        -0.0599,  0.0638])\n",
      "hidden5.weight \t tensor([[ 0.0117, -0.2015,  0.1781,  0.1772,  0.0159, -0.1797, -0.1344,  0.0044,\n",
      "         -0.1807, -0.1764],\n",
      "        [-0.0472, -0.0911,  0.0776, -0.0831, -0.0419, -0.1207, -0.2890,  0.2758,\n",
      "          0.0603,  0.2828],\n",
      "        [-0.0510, -0.0795, -0.2973, -0.1200,  0.2074, -0.3097, -0.1106,  0.1927,\n",
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      "         -0.2286, -0.0675],\n",
      "        [ 0.1616,  0.1182,  0.2692, -0.2157,  0.0027, -0.0934, -0.1769, -0.1779,\n",
      "          0.0797,  0.0331],\n",
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      "         -0.2892, -0.1856],\n",
      "        [ 0.1933,  0.0693, -0.1116, -0.1195,  0.2020,  0.2280,  0.3047,  0.0922,\n",
      "          0.1528, -0.0254],\n",
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      "          0.0890, -0.0640],\n",
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      "        [-0.0607, -0.2679,  0.2442, -0.0816, -0.2044,  0.2028,  0.1123,  0.0783,\n",
      "         -0.0627, -0.0987]])\n",
      "hidden5.bias \t tensor([ 0.0537,  0.2447,  0.0453, -0.1495,  0.3141,  0.0068,  0.2500,  0.0771,\n",
      "         0.2835,  0.1825])\n",
      "hidden6.weight \t tensor([[-0.1612,  0.2221,  0.0020, -0.2538, -0.0663, -0.2854, -0.1638, -0.2240,\n",
      "          0.2163, -0.0664],\n",
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      "          0.2181,  0.2235],\n",
      "        [-0.2490,  0.3037, -0.3110,  0.1818,  0.0223,  0.1496, -0.1710,  0.1901,\n",
      "         -0.1565, -0.2795],\n",
      "        [ 0.1059,  0.1731,  0.3135, -0.0330,  0.3042,  0.2032, -0.1568, -0.2439,\n",
      "          0.1774, -0.1733],\n",
      "        [ 0.1451, -0.2708,  0.2307,  0.0948, -0.0364,  0.1450, -0.2251, -0.0691,\n",
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      "          0.1327,  0.1537],\n",
      "        [ 0.1138, -0.1119,  0.1902, -0.2761,  0.0440,  0.1429,  0.2161, -0.1579,\n",
      "         -0.2137,  0.1185]])\n",
      "hidden6.bias \t tensor([-0.2511, -0.2628, -0.2844, -0.1533, -0.0604, -0.1873, -0.1337, -0.2800,\n",
      "         0.1944, -0.0724])\n",
      "hidden7.weight \t tensor([[-0.1995,  0.0823, -0.2364,  0.2214,  0.1818, -0.3108, -0.2482, -0.2085,\n",
      "         -0.1712,  0.0395],\n",
      "        [-0.3127, -0.2344,  0.1444, -0.1036, -0.1258, -0.2616,  0.1282,  0.0223,\n",
      "         -0.1222, -0.1937],\n",
      "        [ 0.0673,  0.0860,  0.1404,  0.0940, -0.0482, -0.2448,  0.2013, -0.0027,\n",
      "         -0.2102,  0.2727],\n",
      "        [ 0.1763, -0.2485, -0.1579, -0.1076, -0.1826,  0.1477, -0.2256,  0.2939,\n",
      "         -0.1307,  0.1867],\n",
      "        [ 0.0108, -0.1391,  0.2112, -0.2413, -0.1673,  0.0379,  0.2509, -0.1355,\n",
      "         -0.1926, -0.2019]])\n",
      "hidden7.bias \t tensor([-0.1394, -0.1092, -0.0737, -0.1799,  0.0988])\n",
      "predict.weight \t tensor([[ 0.0036, -0.2922, -0.2554,  0.0947, -0.0064]])\n",
      "predict.bias \t tensor([0.3165])\n"
     ]
    }
   ],
   "source": [
    "class M1(torch.nn.Module):\n",
    "    def __init__(self,):\n",
    "        super(M1, self).__init__()\n",
    "        self.hidden1 = torch.nn.Linear(1, 5)   # hidden layer\n",
    "        self.hidden2 = torch.nn.Linear(5, 10)\n",
    "        self.hidden3 = torch.nn.Linear (10, 10)\n",
    "        self.hidden4 = torch.nn.Linear (10, 10)\n",
    "        self.hidden5 = torch.nn.Linear (10, 10)\n",
    "        self.hidden6 = torch.nn.Linear (10, 10)\n",
    "        self.hidden7 = torch.nn.Linear (10,5)\n",
    "        self.predict = torch.nn.Linear(5, 1)   # output layer\n",
    "        #self.activation = nn.LeakyReLU()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.leaky_relu(self.hidden1(x))      # activation function for hidden layer\n",
    "        x = F.leaky_relu(self.hidden2(x))\n",
    "        x = F.leaky_relu(self.hidden3(x))\n",
    "        x = F.leaky_relu(self.hidden4(x))\n",
    "        x = F.leaky_relu(self.hidden5(x))\n",
    "        x = F.leaky_relu(self.hidden6(x))\n",
    "        x = F.leaky_relu(self.hidden7(x))\n",
    "        x = self.predict(x)                    # linear output\n",
    "        return x\n",
    "\n",
    "m1 = M1()     # define the network\n",
    "#optimizer = torch.optim.RMSprop(m1.parameters(), lr=0.0012, weight_decay = 1e-4)\n",
    "optimizer = torch.optim.Adam(m1.parameters(), lr=0.0012, weight_decay = 1e-4)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss\n",
    "\n",
    "a=[]\n",
    "for i in m1.parameters():\n",
    "    a.append(torch.numel(i))\n",
    "print('Total no of parameters in Model 1:', np.sum(a),)\n",
    "\n",
    "#Getting weights & bias\n",
    "print(\"\\nModel 1 weight and bias before training\\n\")\n",
    "for param in m1.state_dict():\n",
    "    print(param, \"\\t\",m1.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "87ee2787-4d5f-4a33-b0da-de5a8951c2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainFunc(model,x,y):\n",
    "    model.train()\n",
    "    max_epoch = 25000\n",
    "    epoch_arr,loss_arr=[],[]\n",
    "    not_converged = True\n",
    "    epoch = 0\n",
    "    while not_converged:\n",
    "        epoch += 1\n",
    "        prediction = model(x)     # input x and predict based on x\n",
    "        loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)\n",
    "        optimizer.zero_grad()   # clear gradients for next train\n",
    "        loss.backward()         # backpropagation, compute gradients\n",
    "        optimizer.step() \n",
    "        epoch_arr.append(epoch)\n",
    "        loss_arr.append(loss.detach().numpy())\n",
    "        \n",
    "        if epoch%100 == 0 : print(f'epoch: {epoch}, loss = {loss.item():.4f}', end='\\r', flush= True) #, weight = {model.weight.item()},  bias ={model.bias.item()}')\n",
    "                \n",
    "        if epoch == max_epoch:\n",
    "                print(\"Max Epoch Reached\")\n",
    "                not_converged = False\n",
    "        elif (epoch > 5) and  (loss_arr[-1] < 0.001):\n",
    "            if abs(loss_arr[-3] - loss_arr[-2]) < 1.0e-05 and abs(loss_arr[-2] - loss_arr[-1]) < 1.0e-05:\n",
    "                print(f\"Convergeance reached for loss:{loss_arr[-1]:.4f} at epoch: {epoch}\")\n",
    "                not_converged = False\n",
    "                \n",
    "    return epoch_arr,loss_arr,prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c598bf07-6206-4f83-af21-8e439765c8fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convergeance reached for loss:0.0010 at epoch: 961\n"
     ]
    }
   ],
   "source": [
    "M1epoch_arr,M1loss_arr,M1prediction = trainFunc(m1,x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e8997ed4-51aa-40f3-a367-52adecd0e55b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model 1 weight and bias after training\n",
      "\n",
      "hidden1.weight \t tensor([[ 0.3736],\n",
      "        [-0.4739],\n",
      "        [-0.2814],\n",
      "        [ 0.6192],\n",
      "        [-0.9102]])\n",
      "hidden1.bias \t tensor([0.3422, 0.0111, 0.1884, 0.0107, 0.0047])\n",
      "hidden2.weight \t tensor([[ 3.3283e-09,  4.3910e-09, -6.4955e-10,  1.7511e-08,  1.1825e-08],\n",
      "        [ 1.8294e-01,  4.0905e-01, -2.3418e-01,  5.6289e-01,  5.5227e-01],\n",
      "        [ 1.7149e-01,  1.2882e-01, -2.0818e-01,  3.6291e-01,  2.7064e-01],\n",
      "        [ 1.4215e-01,  2.7593e-01, -2.2455e-01,  2.2291e-01,  5.1010e-01],\n",
      "        [-4.5696e-07,  1.2729e-03,  7.1264e-06, -8.4037e-04, -1.3123e-03],\n",
      "        [-1.3379e-01,  1.6782e-01,  7.3053e-02,  6.7702e-02,  2.2863e-01],\n",
      "        [ 2.9684e-01,  1.8848e-01, -1.6127e-01,  4.3921e-01,  2.3934e-01],\n",
      "        [ 2.0449e-01,  2.3778e-01, -2.2015e-01,  4.9750e-01,  4.8195e-01],\n",
      "        [-1.4216e-01,  1.3503e-01,  1.7585e-01, -2.4912e-02,  2.7032e-01],\n",
      "        [ 3.0941e-02, -1.6599e-04, -2.4079e-04,  2.8581e-02, -2.6574e-04]])\n",
      "hidden2.bias \t tensor([-4.6841e-06, -2.3279e-02, -1.0519e-02,  2.5164e-03, -4.6132e-07,\n",
      "         4.5755e-02, -5.1655e-02, -3.6895e-02,  3.4801e-02, -3.0372e-02])\n",
      "hidden3.weight \t tensor([[-8.7400e-08,  4.6316e-01,  2.6054e-01,  3.1321e-01, -4.6086e-06,\n",
      "          1.5242e-01,  2.9438e-01,  4.0165e-01, -4.8711e-02,  8.9867e-03],\n",
      "        [-7.0396e-08,  2.6559e-01,  1.8229e-01,  1.7074e-01, -3.5647e-06,\n",
      "          7.1082e-02,  1.7127e-01,  2.5253e-01, -1.9360e-02,  1.0012e-02],\n",
      "        [-3.6794e-07, -3.7826e-01, -2.4521e-01, -2.3281e-01,  1.4918e-05,\n",
      "         -1.3826e-01, -2.2439e-01, -3.5816e-01,  5.4617e-02,  8.8429e-04],\n",
      "        [ 4.5047e-06,  3.9098e-01,  2.3541e-01,  2.7372e-01, -3.2747e-06,\n",
      "          7.6819e-02,  2.7891e-01,  3.6354e-01, -4.8279e-02, -6.0434e-03],\n",
      "        [-2.0290e-08,  4.1094e-01,  2.1097e-01,  1.9927e-01, -4.2875e-06,\n",
      "          9.7529e-02,  2.4603e-01,  3.3855e-01, -1.8753e-02,  1.7692e-02],\n",
      "        [-4.8319e-07, -4.1782e-01, -1.4997e-01, -2.6386e-01,  1.1275e-05,\n",
      "         -1.2259e-01, -2.1996e-01, -3.8322e-01,  1.0232e-01,  2.3192e-04],\n",
      "        [-1.1260e-07,  4.7900e-01,  2.4354e-01,  3.2268e-01, -4.7656e-06,\n",
      "          1.2517e-01,  2.8181e-01,  4.0485e-01, -3.9608e-02,  3.0095e-03],\n",
      "        [-8.6066e-08,  4.4076e-01,  2.2731e-01,  2.8466e-01, -3.4644e-06,\n",
      "          1.2857e-01,  2.8361e-01,  3.8632e-01, -4.3831e-02, -1.4139e-03],\n",
      "        [-3.7992e-08,  3.1668e-01,  2.1633e-01,  2.0604e-01, -1.5576e-05,\n",
      "          7.0059e-02,  2.2448e-01,  2.8008e-01, -1.9017e-02,  8.7406e-03],\n",
      "        [-1.2729e-07,  3.0068e-01,  1.7363e-01,  1.6384e-01, -3.2799e-06,\n",
      "          7.4479e-02,  2.1024e-01,  2.7677e-01, -1.4543e-02,  7.7889e-03]])\n",
      "hidden3.bias \t tensor([0.0341, 0.0318, 0.3942, 0.0367, 0.0268, 0.3951, 0.0734, 0.0367, 0.0318,\n",
      "        0.0350])\n",
      "hidden4.weight \t tensor([[-2.2870e-01, -1.7354e-01,  2.5064e-01, -2.0904e-01, -1.6055e-01,\n",
      "          3.0776e-01, -1.5122e-01, -2.1441e-01, -1.8910e-01, -1.6856e-01],\n",
      "        [ 4.4093e-01,  2.8136e-01,  9.4879e-04,  3.8163e-01,  3.5562e-01,\n",
      "         -5.0811e-02,  4.1599e-01,  4.0970e-01,  3.1677e-01,  2.6471e-01],\n",
      "        [ 9.5921e-02,  2.2848e-02,  7.4669e-01,  1.9287e-02,  1.0678e-01,\n",
      "          6.4277e-01,  9.2662e-02,  5.0868e-02,  3.3535e-02,  4.2035e-02],\n",
      "        [-3.5130e-01, -1.9097e-01,  2.5292e-01, -2.5000e-01, -1.9561e-01,\n",
      "          3.4461e-01, -2.1826e-01, -3.0141e-01, -2.3079e-01, -1.9348e-01],\n",
      "        [ 3.2022e-01,  2.2095e-01, -1.4054e-01,  2.9562e-01,  3.0870e-01,\n",
      "         -1.7250e-01,  3.7060e-01,  3.4100e-01,  2.6387e-01,  2.6433e-01],\n",
      "        [-1.2605e-01, -7.6605e-02,  8.7673e-02, -1.1309e-01, -9.9726e-02,\n",
      "          7.9095e-02, -1.0669e-01, -1.1727e-01, -8.9433e-02, -8.5204e-02],\n",
      "        [ 2.9938e-08,  6.5228e-08,  3.1302e-07,  1.2556e-07,  1.4460e-07,\n",
      "          1.9440e-07, -8.2761e-06,  2.4344e-07,  1.6374e-07,  6.0492e-07],\n",
      "        [ 2.8610e-01,  9.4262e-02, -6.4395e-01,  2.5611e-01,  1.8653e-01,\n",
      "         -5.8472e-01,  3.2003e-01,  2.7927e-01,  1.4632e-01,  1.4433e-01],\n",
      "        [ 3.5583e-01,  1.9917e-01, -2.6028e-01,  3.3664e-01,  3.0824e-01,\n",
      "         -2.9868e-01,  4.0340e-01,  2.8672e-01,  2.4417e-01,  2.1364e-01],\n",
      "        [ 2.7562e-09,  1.4507e-09,  9.8239e-09,  4.5344e-07,  2.8633e-09,\n",
      "          1.3366e-08, -1.6234e-06,  4.4959e-09,  2.4943e-09,  3.5611e-09]])\n",
      "hidden4.bias \t tensor([ 3.3259e-01,  6.1065e-02,  3.6370e-01,  3.0825e-01,  7.9638e-02,\n",
      "         1.5731e-01, -3.7328e-06,  2.0935e-01,  1.0151e-01, -2.5656e-08])\n",
      "hidden5.weight \t tensor([[ 3.6029e-01, -3.8223e-01,  2.0327e-01,  4.0631e-01, -3.5841e-01,\n",
      "          5.3374e-02,  2.0457e-08, -2.8489e-01, -3.5471e-01,  2.9912e-09],\n",
      "        [-1.7904e-01,  1.9533e-01,  2.1109e-01, -1.9821e-01,  2.3536e-01,\n",
      "          1.0824e-03, -3.3926e-06,  4.2082e-01,  2.9300e-01,  3.0346e-06],\n",
      "        [ 1.4949e-02,  9.1463e-03, -6.9567e-01,  1.9680e-02,  7.6256e-02,\n",
      "          9.2393e-03, -2.8528e-07,  4.1652e-01,  1.3460e-01,  1.9332e-08],\n",
      "        [ 4.2779e-08,  6.5984e-07,  7.8011e-07, -5.1017e-09,  3.9172e-07,\n",
      "          1.0343e-09, -8.0650e-15, -4.1493e-07,  2.3998e-07, -3.4382e-15],\n",
      "        [ 3.4126e-01, -2.4512e-01,  3.5782e-01,  1.5736e-01, -2.9091e-01,\n",
      "          2.7079e-01,  6.2835e-08, -3.1578e-01, -2.6991e-01,  2.5551e-09],\n",
      "        [ 3.0274e-01, -3.0263e-01,  1.8170e-01,  3.5311e-01, -2.9576e-01,\n",
      "          4.9223e-02,  1.9529e-08, -2.5567e-01, -3.0880e-01,  3.0253e-09],\n",
      "        [-1.3894e-01,  4.0856e-01,  1.6910e-01, -1.9850e-01,  3.6021e-01,\n",
      "         -4.4136e-02,  1.7981e-05,  2.3873e-01,  3.8842e-01, -5.9646e-09],\n",
      "        [ 3.2655e-01, -4.4302e-01,  9.7106e-02,  4.2235e-01, -4.3356e-01,\n",
      "          6.0881e-02, -7.2210e-06, -3.4002e-01, -4.9983e-01,  2.2982e-09],\n",
      "        [ 2.7818e-02, -3.3758e-03,  1.4965e-01, -4.1296e-02,  1.7124e-02,\n",
      "          5.3599e-02, -6.3085e-09,  2.8221e-01,  9.5982e-02,  1.7758e-09],\n",
      "        [ 2.2905e-01, -2.6270e-01,  1.1199e-01,  1.3549e-01, -1.2900e-01,\n",
      "          1.5585e-01, -7.7368e-08,  1.6217e-01, -3.8738e-02,  9.5291e-09]])\n",
      "hidden5.bias \t tensor([ 2.9930e-01,  2.2689e-01,  2.3794e-02, -1.3824e-04,  5.9804e-01,\n",
      "         3.0654e-01,  6.5950e-02,  3.5062e-01,  3.5568e-01,  4.4216e-01])\n",
      "hidden6.weight \t tensor([[-1.3993e-07,  1.8887e-06, -2.3759e-06, -2.0375e-07,  3.5756e-07,\n",
      "         -5.1807e-06,  3.3239e-06,  1.0378e-07, -1.6596e-06, -2.1565e-06],\n",
      "        [ 3.1104e-01,  8.3548e-02,  4.0599e-02, -8.9054e-07,  1.6330e-01,\n",
      "          2.7197e-01,  3.8176e-02,  3.0601e-01,  1.2421e-01,  1.3371e-01],\n",
      "        [-2.2511e-01,  3.6939e-01,  5.9430e-01, -5.4604e-06,  3.6623e-01,\n",
      "         -9.6012e-02,  7.4906e-02, -4.3244e-01,  3.6197e-01,  3.5257e-01],\n",
      "        [ 2.7730e-01,  8.9422e-02, -5.1186e-01,  2.1460e-06, -8.7193e-02,\n",
      "          2.1056e-01,  9.7064e-02,  2.9233e-01,  8.2826e-02,  1.8261e-03],\n",
      "        [-1.9368e-02,  2.9757e-01, -6.3141e-01,  7.1201e-06, -5.9154e-01,\n",
      "         -1.7943e-02,  3.0111e-01, -2.9131e-02,  1.1089e-01, -3.0579e-01],\n",
      "        [-1.6003e-06, -1.4904e-05,  5.8302e-06,  1.5031e-11, -1.2641e-05,\n",
      "         -2.3390e-06,  1.1938e-05, -2.0967e-06, -7.8597e-06, -1.2002e-05],\n",
      "        [-2.2664e-07, -3.4554e-04,  1.0583e-05, -6.4300e-12, -3.4211e-06,\n",
      "         -2.6277e-07, -4.7895e-05,  3.5247e-08, -1.0534e-07,  5.5657e-06],\n",
      "        [ 3.0938e-01,  6.3397e-02, -2.3741e-01,  1.0439e-06,  5.3547e-02,\n",
      "          2.6287e-01,  4.3494e-02,  3.4428e-01,  8.7189e-02,  7.8219e-02],\n",
      "        [ 4.5413e-01, -2.4385e-01, -2.8326e-02, -2.6670e-06,  5.3178e-01,\n",
      "          3.9153e-01, -3.5958e-01,  4.3372e-01,  6.0639e-02,  3.1625e-01],\n",
      "        [ 1.2421e-05, -6.3301e-05,  5.8599e-05, -1.7027e-06,  5.0187e-05,\n",
      "         -2.2626e-06, -8.0129e-05, -1.0249e-05, -2.8462e-05,  3.7254e-05]])\n",
      "hidden6.bias \t tensor([-2.4384e-05,  5.2383e-02,  4.5702e-02, -2.6915e-02, -3.0349e-01,\n",
      "        -1.8829e-03, -1.9701e-04, -5.5940e-03,  3.8245e-01, -1.6281e-05])\n",
      "hidden7.weight \t tensor([[ 3.4319e-07,  2.5208e-01, -9.0485e-02,  2.5739e-01,  3.1127e-01,\n",
      "         -6.9469e-05, -6.0635e-06,  2.3115e-01,  4.3323e-01, -1.7502e-06],\n",
      "        [-3.4306e-05,  4.3807e-02,  3.9846e-01, -2.1814e-01, -5.3155e-01,\n",
      "         -1.0089e-04,  1.8024e-06, -3.7896e-02, -1.8107e-01,  4.4219e-06],\n",
      "        [-4.6890e-05,  1.6755e-01,  3.7474e-01, -2.0765e-01, -5.3530e-01,\n",
      "         -3.1623e-04,  8.1160e-06,  2.7540e-02, -2.6759e-01,  1.7858e-03],\n",
      "        [-1.3532e-07,  1.8612e-01, -1.2562e-01,  1.8911e-01, -5.8266e-02,\n",
      "         -3.4826e-06,  1.3601e-06,  2.0619e-01,  3.2312e-01, -9.3431e-07],\n",
      "        [-2.5920e-07, -1.2499e-01,  3.1438e-01, -2.2230e-01, -4.3748e-01,\n",
      "         -1.1368e-05,  9.2223e-04, -1.3899e-01, -3.9036e-01, -1.5254e-05]])\n",
      "hidden7.bias \t tensor([ 0.0598,  0.1456,  0.1019, -0.0775,  0.1853])\n",
      "predict.weight \t tensor([[ 0.3173, -0.6830, -0.5215,  0.2998, -0.1517]])\n",
      "predict.bias \t tensor([0.2850])\n"
     ]
    }
   ],
   "source": [
    "#Getting weights & bias\n",
    "print(\"\\nModel 1 weight and bias after training\\n\")\n",
    "for param in m1.state_dict():\n",
    "    print(param, \"\\t\",m1.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "460a2960-d3cc-4745-8d87-bbfa6be38d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(M1epoch_arr,M1loss_arr,color=\"blue\")\n",
    "plt.title('Model 1 Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0691114d-d1eb-451c-92dc-ae6f1718c615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "\n",
    "plt.plot(x.numpy(),y.numpy(),color=\"black\")\n",
    "plt.plot(x,M1prediction.data.numpy(),color=\"yellow\")\n",
    "plt.title('Model Prediction 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4d0fb126-1223-42fd-94c7-20d842af1f49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no of parameters in Model 2: 572\n",
      "\n",
      "Model 2 weight and bias before training\n",
      "\n",
      "hidden1.weight \t tensor([[-0.1516],\n",
      "        [-0.8101],\n",
      "        [-0.7396],\n",
      "        [-0.2935],\n",
      "        [-0.2215],\n",
      "        [ 0.1141],\n",
      "        [-0.2243],\n",
      "        [ 0.3690],\n",
      "        [-0.3326],\n",
      "        [ 0.7340]])\n",
      "hidden1.bias \t tensor([ 0.2297, -0.3074,  0.8912,  0.2553, -0.6796, -0.5565,  0.6270,  0.4567,\n",
      "         0.7808, -0.0768])\n",
      "hidden2.weight \t tensor([[ 0.2212,  0.1572,  0.0978, -0.0732,  0.3048,  0.0640, -0.0816, -0.0045,\n",
      "          0.3109,  0.2124],\n",
      "        [-0.0234,  0.3100,  0.1389, -0.1684, -0.2878,  0.1838,  0.2966, -0.1842,\n",
      "          0.1299,  0.0135],\n",
      "        [-0.0676, -0.0860,  0.0880,  0.0697,  0.2136,  0.1158,  0.1439,  0.2790,\n",
      "          0.0985,  0.0469],\n",
      "        [-0.1151,  0.2319,  0.0467,  0.2038,  0.3054, -0.0707,  0.2227, -0.0642,\n",
      "          0.2854,  0.2489],\n",
      "        [-0.1133,  0.0113, -0.1381, -0.1279, -0.0640, -0.0022, -0.1102, -0.2985,\n",
      "         -0.1913, -0.0270],\n",
      "        [-0.1009,  0.2349,  0.2570,  0.2015,  0.1400, -0.2507,  0.2988, -0.2787,\n",
      "         -0.1040, -0.2226],\n",
      "        [ 0.1137, -0.2631, -0.1559, -0.2009, -0.1944, -0.1476,  0.3109, -0.0258,\n",
      "          0.0964, -0.3139],\n",
      "        [ 0.2812, -0.0689, -0.2902,  0.2179, -0.0855,  0.2872, -0.2148, -0.2936,\n",
      "         -0.2376,  0.1370],\n",
      "        [-0.0355, -0.2930,  0.0586, -0.0202,  0.1142, -0.1609, -0.2258,  0.2155,\n",
      "         -0.1522,  0.2224],\n",
      "        [ 0.2564, -0.2211, -0.1458,  0.0381, -0.2982,  0.0971, -0.1318, -0.1402,\n",
      "         -0.2445,  0.1479],\n",
      "        [-0.2015, -0.2528, -0.0832, -0.1760, -0.3074,  0.1274, -0.1203,  0.2215,\n",
      "          0.2226, -0.2017],\n",
      "        [-0.1844, -0.1122,  0.1852, -0.0109, -0.1130,  0.1959,  0.2999,  0.2338,\n",
      "         -0.2418, -0.0657],\n",
      "        [-0.0607,  0.2699, -0.1636, -0.1812,  0.0049, -0.0735,  0.1197,  0.0384,\n",
      "         -0.1888,  0.2299],\n",
      "        [ 0.1968,  0.0692, -0.2862,  0.1486,  0.2101,  0.0880, -0.1222,  0.0212,\n",
      "         -0.1215,  0.2977],\n",
      "        [ 0.3054, -0.0369, -0.2128,  0.1860,  0.1614,  0.1676, -0.2379,  0.1072,\n",
      "         -0.1350, -0.2779],\n",
      "        [-0.2208,  0.1341,  0.1184, -0.2775,  0.1793,  0.2423,  0.2401,  0.1786,\n",
      "         -0.0496,  0.2119],\n",
      "        [ 0.2476,  0.1648, -0.2274, -0.1104,  0.2505,  0.2411,  0.2018, -0.1322,\n",
      "         -0.0626, -0.2356],\n",
      "        [-0.1802,  0.2427, -0.2596, -0.0781,  0.0671, -0.1897,  0.0737, -0.0508,\n",
      "          0.1265, -0.2427]])\n",
      "hidden2.bias \t tensor([ 0.0436, -0.2883,  0.1962,  0.3072, -0.1129,  0.0468, -0.1629, -0.2023,\n",
      "        -0.1602, -0.1506,  0.0600, -0.0362,  0.0222, -0.2423, -0.1987, -0.2738,\n",
      "        -0.2273,  0.2648])\n",
      "hidden3.weight \t tensor([[ 0.0876, -0.2248, -0.0393,  0.0189,  0.1259,  0.2186,  0.0569,  0.1660,\n",
      "         -0.0839, -0.0077, -0.1423,  0.0543,  0.2176,  0.1480,  0.2345, -0.1062,\n",
      "         -0.0295,  0.0486],\n",
      "        [ 0.1895, -0.2167, -0.1158,  0.0342, -0.1362,  0.0880, -0.0161, -0.0611,\n",
      "          0.1264,  0.1684,  0.2293,  0.1067, -0.0064,  0.1927,  0.1876, -0.1181,\n",
      "          0.1074,  0.0276],\n",
      "        [-0.1019, -0.0561, -0.0557, -0.1649, -0.0719,  0.0793,  0.1730,  0.0179,\n",
      "         -0.2283,  0.1248, -0.0713,  0.2105, -0.2278, -0.0551,  0.1828,  0.1020,\n",
      "         -0.1176, -0.2041],\n",
      "        [-0.0025,  0.0911, -0.1957, -0.1972, -0.0030, -0.0483,  0.1515, -0.2284,\n",
      "         -0.0375, -0.1827, -0.0343, -0.1204,  0.0742, -0.1972, -0.1854, -0.1338,\n",
      "         -0.2064,  0.1488],\n",
      "        [-0.0337, -0.1310,  0.1652, -0.0535,  0.1219, -0.0731, -0.1972,  0.0518,\n",
      "          0.1468, -0.2298,  0.0245,  0.0470,  0.0271, -0.0058, -0.0733, -0.1830,\n",
      "          0.0384, -0.1583],\n",
      "        [-0.1947, -0.0757, -0.1614,  0.0722,  0.1679,  0.1832, -0.1461,  0.1285,\n",
      "          0.1675,  0.0026,  0.0994,  0.1334,  0.1803,  0.1261,  0.1006, -0.0895,\n",
      "         -0.1314, -0.1118],\n",
      "        [ 0.1939, -0.0571,  0.0084, -0.2198, -0.0105, -0.0665, -0.1521,  0.1825,\n",
      "         -0.1908, -0.0337, -0.0167, -0.2134,  0.1425, -0.0750,  0.1059,  0.1315,\n",
      "          0.2078,  0.0583],\n",
      "        [ 0.1770,  0.2177,  0.0467, -0.0480, -0.0645,  0.0382,  0.0848, -0.2224,\n",
      "          0.1766,  0.0372, -0.2196,  0.0755,  0.1496,  0.0881,  0.1924,  0.0049,\n",
      "         -0.1955, -0.0005],\n",
      "        [ 0.1713, -0.1134,  0.1005,  0.0543, -0.0382,  0.0017,  0.1003, -0.1613,\n",
      "          0.1488,  0.1596,  0.1323,  0.2355, -0.0103, -0.1664,  0.0108, -0.0684,\n",
      "          0.0832,  0.1994],\n",
      "        [-0.1290,  0.0099,  0.2293, -0.1882, -0.0407,  0.2289,  0.1647,  0.0684,\n",
      "         -0.1017, -0.0220, -0.2114, -0.2219, -0.2208,  0.2264,  0.1864,  0.0935,\n",
      "         -0.0592,  0.2029],\n",
      "        [-0.0623,  0.1636, -0.0077, -0.1109, -0.0658, -0.0503, -0.2157,  0.0705,\n",
      "         -0.0718,  0.1083,  0.1503, -0.0375,  0.1143,  0.0471,  0.1583, -0.1520,\n",
      "         -0.0207, -0.0785],\n",
      "        [ 0.2065, -0.2116, -0.1325,  0.0266,  0.2092,  0.1595, -0.2206, -0.1657,\n",
      "          0.2285,  0.0344, -0.2019, -0.0743,  0.1030, -0.0493, -0.1810, -0.1455,\n",
      "         -0.1221, -0.0092],\n",
      "        [ 0.0924,  0.1132, -0.1145, -0.0395, -0.1820,  0.1782,  0.2352,  0.2291,\n",
      "          0.0963, -0.0503, -0.0059,  0.0850, -0.2043,  0.0146, -0.1916, -0.2357,\n",
      "         -0.0079, -0.0750],\n",
      "        [-0.1493,  0.0391,  0.0096,  0.0360, -0.1319, -0.2309,  0.0052, -0.0962,\n",
      "         -0.0795,  0.1735,  0.0385,  0.1076, -0.0659, -0.1870,  0.0470, -0.1561,\n",
      "          0.0292, -0.1839],\n",
      "        [ 0.1777, -0.1606,  0.2046,  0.1665,  0.1782,  0.1414,  0.0546, -0.0115,\n",
      "         -0.0038, -0.0937, -0.2167,  0.2148,  0.0122,  0.1109,  0.0087,  0.0677,\n",
      "         -0.1452, -0.1832]])\n",
      "hidden3.bias \t tensor([ 0.1798,  0.0894,  0.1164, -0.2339,  0.0159, -0.2199, -0.0270, -0.0125,\n",
      "        -0.1658, -0.2334,  0.0016,  0.2120,  0.2124, -0.1435, -0.0419])\n",
      "hidden4.weight \t tensor([[-0.2159, -0.2016, -0.0708,  0.2507, -0.0674,  0.0149,  0.2429,  0.0460,\n",
      "          0.2083,  0.1716,  0.2421,  0.0443, -0.2088, -0.2221, -0.1334],\n",
      "        [ 0.2131,  0.1485,  0.0756, -0.1966, -0.1902,  0.2107, -0.0268, -0.2571,\n",
      "         -0.0319, -0.0620,  0.0039,  0.1081,  0.0994, -0.1976, -0.0028],\n",
      "        [-0.1280,  0.1747,  0.1279,  0.1417,  0.2347,  0.1268, -0.2580,  0.0652,\n",
      "         -0.1772,  0.0345,  0.0028,  0.2561, -0.1764, -0.2530, -0.0692],\n",
      "        [ 0.0416,  0.1891,  0.2093,  0.1989,  0.0815, -0.0738, -0.0546, -0.1412,\n",
      "          0.0505, -0.1796,  0.0888,  0.1667,  0.2191, -0.2545,  0.2118]])\n",
      "hidden4.bias \t tensor([-0.1622, -0.0211,  0.2357,  0.1399])\n",
      "predict.weight \t tensor([[-0.1586,  0.2952, -0.4863,  0.3909]])\n",
      "predict.bias \t tensor([0.4854])\n"
     ]
    }
   ],
   "source": [
    "class M2(torch.nn.Module):\n",
    "    def __init__(self,):\n",
    "        super(M2, self).__init__()\n",
    "        self.hidden1 = torch.nn.Linear(1, 10)   # hidden layer\n",
    "        self.hidden2 = torch.nn.Linear (10, 18)\n",
    "        self.hidden3 = torch.nn.Linear (18, 15)\n",
    "        self.hidden4 = torch.nn.Linear (15, 4)\n",
    "        self.predict = torch.nn.Linear(4, 1)   # output layer\n",
    "        #self.activation = nn.LeakyReLU()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.leaky_relu(self.hidden1(x))      # activation function for hidden layer\n",
    "        x = F.leaky_relu(self.hidden2(x))\n",
    "        x = F.leaky_relu(self.hidden3(x))\n",
    "        x = F.leaky_relu(self.hidden4(x))\n",
    "        x = self.predict(x)                    # linear output\n",
    "        return x\n",
    "\n",
    "m2 = M2()     # define the network\n",
    "#optimizer = torch.optim.RMSprop(m1.parameters(), lr=0.0012, weight_decay = 1e-4)\n",
    "optimizer = torch.optim.Adam(m2.parameters(), lr=0.0012, weight_decay = 1e-4)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss\n",
    "\n",
    "a=[]\n",
    "for i in m2.parameters():\n",
    "    a.append(torch.numel(i))\n",
    "print('Total no of parameters in Model 2:', np.sum(a))\n",
    "\n",
    "\n",
    "#Getting weights & bias\n",
    "print(\"\\nModel 2 weight and bias before training\\n\")\n",
    "for param in m2.state_dict():\n",
    "    print(param, \"\\t\",m2.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a5f96879-b837-4faf-9726-6d76786d0843",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convergeance reached for loss:0.0010 at epoch: 857\n"
     ]
    }
   ],
   "source": [
    "M2epoch_arr,M2loss_arr,M2prediction = trainFunc(m2,x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "257910c2-dcce-4dd3-9391-97f610861117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdNElEQVR4nO3de5xVdb3/8debQRAU8MKACCKoBCGp2Xi/X4PUH+d3KsNM06OHqLTbsV9Y5/TolNWpzLLSDC+nq3E8aoWGYg/1VGYqA5GIiiFgjKAMKmhIXD+/P757zmyGGWYDM7P2rPV+Ph7rsfa67fnMesB7f+e71/ouRQRmZpZfPbIuwMzMOpeD3sws5xz0ZmY556A3M8s5B72ZWc456M3Mcs5Bb4UhaYSkkNSzgn0vkfRIV9Rl1tkc9FaVJC2VtEHSwBbr55XCekRGpSHpLZJ+JalR0quSZkkavZ39fyjpmq6s0aycg96q2RLggqYFSW8D+mRXzv/aC5gBjAYGA08Av8qyILPtcdBbNfsJcHHZ8geBH5fvIGmApB+XWtcvSPpXST1K22okXStplaTFwDmtHHurpBWSXpR0jaSa9oqKiCci4taIeDUiNgLfAkZL2ndHf0FJ/yxpUekvgxmS9i+tl6RvSVopaY2kJyWNK217l6SnJb1RqvuqHf25ViwOeqtmjwH9Jb21FMDvA37aYp/vAgOAg4BTSB8Ml5a2/TNwLvB2oA54T4tjfwRsAg4p7XM2cPlO1Hky8FJEvLIjB0k6HfgqcD4wBHgBmF7afHbpfd9C+gvifUDT+98KfCgi+gHjgId2omYrEAe9VbumVv1ZwLPAi00bysL/6oh4IyKWAt8ELirtcj7w7YhYFhGvkkK16djBwATgExGxNiJWklrmk3akOEnDgBuAT+3E73YhcFtEzI2I9cDVwHGl7x82Av2AMYAi4pmIWFE6biMwVlL/iHgtIubuxM+2AnHQW7X7CfB+4BJadNsAA4FepJZwkxeAoaXX+wPLWmxrciCwG7BC0mpJq4EfAIMqLUxSLfAAcGNE/LzS48rsX15TRPyN1GofGhEPAd8jfYi8LGmapP6lXd8NvAt4QdJvJR23Ez/bCsRBb1UtIl4gfSn7LuDuFptXkVq3B5atG05zq38FcECLbU2WAeuBgRGxV2nqHxGHVlKXpL1JIT8jIr5c6e/TwvLy2iXtAezbVH9EfCci3gEcSurC+XRp/eyImEj6UPolcMdO/nwrCAe9dQeXAadHxNrylRGxmRRyX5bUT9KBpC6Upn78O4CPSRpWCuapZceuIAX1NyX1l9RD0sGSTmmvmFLLehbwh4iY2t7+JTWSdi+begG3A5dKOkJSb+ArwOMRsVTSUZKOkbQbsBb4O7BZUi9JF0oaUPoi+HVgc4U1WEE56K3qRcTzEVHfxuYrSUG4GHiEFJ63lbbdTArkPwNz2fYvgotJXT9PA68Bd5K+FG3P/wWOIoX038qm4ds5Ziqwrmx6KCIeBP4NuIv018fBNH9H0L9U/2uk7p1XgGtL2y4Clkp6HZgCfKCCmq3A5AePmJnlm1v0ZmY556A3M8s5B72ZWc456M3Mcq7d4VqzMHDgwBgxYkTWZZiZdRtz5sxZFRG1rW2ryqAfMWIE9fVtXU1nZmYtSXqhrW3uujEzyzkHvZlZzjnozcxyzkFvZpZzDnozs5xz0JuZ5ZyD3sws53IV9F/6EsyalXUVZmbVJVdB/41vwP33Z12FmVl1yVXQDxgAr7+edRVmZtUlV0Hfvz+sWZN1FWZm1SV3Qe8WvZnZ1nIV9O66MTPbVq6C3l03ZmbbylXQu0VvZratioJe0nhJCyUtkjS1le0XSnqyND0q6fCybUslzZc0T1KnDjLvFr2Z2bbaffCIpBrgBuAsoAGYLWlGRDxdttsS4JSIeE3SBGAacEzZ9tMiYlUH1t2qAQNg7VrYvBlqajr7p5mZdQ+VtOiPBhZFxOKI2ABMByaW7xARj0bEa6XFx4BhHVtmZfr3T3N335iZNask6IcCy8qWG0rr2nIZcF/ZcgAPSJojaXJbB0maLKleUn1jY2MFZW3LQW9mtq1KnhmrVtZFqztKp5GC/sSy1SdExHJJg4DfSHo2In63zRtGTCN1+VBXV9fq+7dnwIA0d9CbmTWrpEXfABxQtjwMWN5yJ0mHAbcAEyPilab1EbG8NF8J/ILUFdQpmlr0/kLWzKxZJUE/GxglaaSkXsAkYEb5DpKGA3cDF0XEc2Xr95DUr+k1cDbwVEcV35Jb9GZm22q36yYiNkm6ApgF1AC3RcQCSVNK228CPg/sC9woCWBTRNQBg4FflNb1BG6PiE4bX9ItejOzbVXSR09EzARmtlh3U9nry4HLWzluMXB4y/WdxV/GmpltK3d3xoJb9GZm5XIV9H37phul3KI3M2uWq6CXPFSxmVlLuQp68Hg3ZmYt5TLo3aI3M2uWu6AfMMAtejOzcrkLerfozcy2lrug98NHzMy2lrug95exZmZby13Qu4/ezGxruQv6vfaC9eth3bqsKzEzqw65C/p99knz117b/n5mZkWRu6Dfe+80d9CbmSUOejOznMtt0L/6arZ1mJlVi9wGvVv0ZmaJg97MLOdyF/RNDx9x0JuZJbkL+pqadC29g97MLMld0EPqvnHQm5kluQ16X3VjZpbkNujdojczSxz0ZmY556A3M8u5XAb9PvukoI/IuhIzs+zlMuj33hs2bPBQxWZmkOOgB195Y2YGOQ9699ObmTnozcxyr6KglzRe0kJJiyRNbWX7hZKeLE2PSjq80mM7g4PezKxZu0EvqQa4AZgAjAUukDS2xW5LgFMi4jDgS8C0HTi2wzU9TtB99GZmlbXojwYWRcTiiNgATAcmlu8QEY9GRFP7+TFgWKXHdoaBA9N81arO/klmZtWvkqAfCiwrW24orWvLZcB9O3qspMmS6iXVNzY2VlBW2/bcE3r3hl18GzOzXKgk6NXKulZvRZJ0GinoP7Ojx0bEtIioi4i62traCspqmwS1tbBy5S69jZlZLvSsYJ8G4ICy5WHA8pY7SToMuAWYEBGv7MixnWHQILfozcygshb9bGCUpJGSegGTgBnlO0gaDtwNXBQRz+3IsZ2lttZBb2YGFbToI2KTpCuAWUANcFtELJA0pbT9JuDzwL7AjZIANpW6YVo9tpN+l63U1sLChV3xk8zMqlslXTdExExgZot1N5W9vhy4vNJju8KgQe6jNzODnN4ZC6lF/+abaTIzK7JcBz24n97MLLdBP2hQmjvozazochv0TS1699ObWdHlPujdojezostt0Dd13bz8crZ1mJllLbdB368f9O8PDQ1ZV2Jmlq3cBj3A8OGwbFn7+5mZ5Vmug/6AA+Cvf826CjOzbOU+6N2iN7Oiy3XQDx+errpZty7rSszMspProD+gNECyv5A1syLLddAPH57m7qc3syLLddA3tejdT29mRZbroB9WekS5g97MiizXQd+7Nwwe7K4bMyu2XAc9+BJLM7PcB/3w4W7Rm1mx5T7oDzwQli6FiKwrMTPLRu6D/qCD0g1THsXSzIqqEEEPsGRJtnWYmWUl90E/cmSaL16cbR1mZlnJfdCPGJHmDnozK6rcB32fPrD//g56Myuu3Ac9pO4b99GbWVEVIugPOsgtejMrrsIEfUMDrF+fdSVmZl2vEEE/cmS6Ycp3yJpZEVUU9JLGS1ooaZGkqa1sHyPpj5LWS7qqxbalkuZLmiepvqMK3xFN19K7+8bMiqhneztIqgFuAM4CGoDZkmZExNNlu70KfAz4hzbe5rSIWLWLte40B72ZFVklLfqjgUURsTgiNgDTgYnlO0TEyoiYDWzshBp32ZAhachiB72ZFVElQT8UKB/ot6G0rlIBPCBpjqTJbe0kabKkekn1jY2NO/D27evRI9049fzzHfq2ZmbdQiVBr1bW7chYkCdExJHABOCjkk5ubaeImBYRdRFRV1tbuwNvX5nRo2Hhwg5/WzOzqldJ0DcAB5QtDwOWV/oDImJ5ab4S+AWpK6jLjR4NixbBpk1Z/HQzs+xUEvSzgVGSRkrqBUwCZlTy5pL2kNSv6TVwNvDUzha7K8aMgQ0b0tj0ZmZF0u5VNxGxSdIVwCygBrgtIhZImlLafpOk/YB6oD+wRdIngLHAQOAXkpp+1u0RcX+n/CbtGD06zRcuhEMOyaICM7NstBv0ABExE5jZYt1NZa9fInXptPQ6cPiuFNhRxoxJ82efhXPOybYWM7OuVIg7YwH23TdN/kLWzIqmMEEPqVXvoDezoilU0I8enbpuzMyKpFBBP2YMrFwJq1dnXYmZWdcpVNCXX3ljZlYUhQx6d9+YWZEUKugPPjgNbrZgQdaVmJl1nUIFfc+e8Na3wpNPZl2JmVnXKVTQAxx2GMyfn3UVZmZdp5BBv3w5rMrsMShmZl2rcEH/treluVv1ZlYUhQv6ww5Lcwe9mRVF4YJ+8GCorfUXsmZWHIULeil13zjozawoChf0kLpvFiyAzZuzrsTMrPMVNujffNMPCzezYihk0B9xRJr/6U+ZlmFm1iUKGfTjxqWhEGbPzroSM7POV8ig32231Kqvr8+6EjOzzlfIoAeoq4M5c2DLlqwrMTPrXIUO+r/9DZ57LutKzMw6V2GD/qij0tz99GaWd4UN+jFjoG9f99ObWf4VNuhrauDIIx30ZpZ/hQ16SN03c+fCxo1ZV2Jm1nkKHfTHHw9//3sKezOzvCp00J94Ypr//vfZ1mFm1pkKHfT77QeHHAKPPJJ1JWZmnafQQQ9w0kkp6H3jlJnlVUVBL2m8pIWSFkma2sr2MZL+KGm9pKt25NisnXgivPIKPPts1pWYmXWOdoNeUg1wAzABGAtcIGlsi91eBT4GXLsTx2bqpJPS3N03ZpZXlbTojwYWRcTiiNgATAcmlu8QESsjYjbQ8kLFdo/N2iGHwKBB/kLWzPKrkqAfCiwrW24oratExcdKmiypXlJ9Y2NjhW+/6yQ49VR48EGI6LIfa2bWZSoJerWyrtJIrPjYiJgWEXURUVdbW1vh23eM8eNhxQo/R9bM8qmSoG8ADihbHgYsr/D9d+XYLjN+fJrfd1+2dZiZdYZKgn42MErSSEm9gEnAjArff1eO7TJDhsDhh8P992ddiZlZx2s36CNiE3AFMAt4BrgjIhZImiJpCoCk/SQ1AJ8C/lVSg6T+bR3bWb/MrpgwAf7wB1izJutKzMw6lqIKv4Gsq6uL+i4eVvLRR+GEE+BHP4KLL+7SH21mtsskzYmIuta2Ff7O2CbHHQcjR8JPfpJ1JWZmHctBXyLBRRelyywbGrKuxsys4zjoy1xySQr866/PuhIzs47joC8zciS8//1w442wcmXW1ZiZdQwHfQuf+1x64tRHPuI7Zc0sHxz0LYwZA1/5Ctx1F3zhCw57M+v+HPSt+NSnUn/9F78I730vLFyYdUVmZjvPQd+KHj3g1ltTy/7Xv06t/BNOgO9+F158MevqzMx2jIO+DT16wNVXw5Il8LWvpTtmP/YxGDYsXXP/9a/D/Pnu2jGz6uc7Y3fAs8+mvvs774R589K6/fdPg6Kde26a9+mTaYlmVlDbuzPWQb+TGhpg1qw0ENpvfpNa/HvuCRMnwvnnwzvfCb17Z12lmRWFg76TbdoEDz8Md9wBd98Nr74K/fqlVv573gPnnOPQN7PO5bFuOlnPnnDWWXDzzfDSSzBzZmrVP/AAvPvdqXvnyith7lz36ZtZ13PQd7DddktDHt9ySwr9++5r/hB4xzvgiCPgm99MT7QyM+sKDvpO1LNn+oJ2+vQU7DfeCLvvDlddla7eGT8ebr8d3nwz60rNLM8c9F1k773hwx+Gxx+HZ55Jl24+8wxceCEMHgyXXpr6+bdsybpSM8sbB30GxoyBa65J1+j/z/+k/vy77oLTT4cRI+Czn00fAmZmHcFBn6EePeCUU9JduC+9BD//OYwbl27GGjsWjjoKvvMdaGzMulIz684c9FWib1+YNCldsdPQANddB5s3w8c/nq7aOe+81Ne/dm3WlZpZd+Ogr0L77Qef/GS6HHP+/DTI2ty5cMEFqT//wgvh3nthw4asKzWz7sBBX+XGjUtj7fz1r+nL2gsvTJdsnnceDBkCkyfDQw+lm7bMzFrjoO8mamrg1FPhBz9I/fn33JMuz/zZz+CMM2DQoPQhMH06rF6ddbVmVk08BEI3t3ZtGm/nnnvSkMqrVqXr9086Cc48M13JU1eX1plZfnmsm4LYvBkeeyyF/n33wZNPpvX9+qWre844IwX/uHHpih8zyw8HfUE1Nqbr9B98MPXj/+UvaX1tbQr8009P4X/QQSBlWqqZ7SIHvQGwbFkK/AcfTNPy5Wn9gQc2h/7pp6cvec2se3HQ2zYi4LnnmkP/4YfhtdfStre9LT0r9/zzYfTobOs0s8o46K1dmzfDn/+cQv+ee+CRR9KHweGHw/vel6aDDsq6SjNri4PedtiLL6ZHJv7Xf8Ef/5jW1dWlm7Yuuij185tZ9djlB49IGi9poaRFkqa2sl2SvlPa/qSkI8u2LZU0X9I8SU7vbmLo0DT8wqOPwtKlafydCPiXf0lDLF9wAfz2t36Qill30G7QS6oBbgAmAGOBCySNbbHbBGBUaZoMfL/F9tMi4oi2Pm2suh14IHz601BfD089BVOmpGv3Tz01Db52002wbl3WVZpZWypp0R8NLIqIxRGxAZgOTGyxz0Tgx5E8Buwlyddu5NChh8L116eunR/+MF2j/+EPp+GVv/KV5i90zax6VBL0Q4FlZcsNpXWV7hPAA5LmSJrc1g+RNFlSvaT6Ro/LW/X69oUPfjA9SOWhh+DII+Fzn4Phw9NDVRz4ZtWjkqBv7Vaalj2z29vnhIg4ktS981FJJ7f2QyJiWkTURURdrb/p6zYkOO20dCfuvHlw7rlpELaRI1ML38Mqm2WvkqBvAA4oWx4GLK90n4homq8EfkHqCrIcOvzw9PCUefPg5JNTC//gg2HatHT5ppllo5Kgnw2MkjRSUi9gEjCjxT4zgItLV98cC6yJiBWS9pDUD0DSHsDZwFMdWL9VocMOgxkz4A9/gFGj4EMfgmOPTd08Ztb12g36iNgEXAHMAp4B7oiIBZKmSJpS2m0msBhYBNwMfKS0fjDwiKQ/A08Av46I+zv4d7Aqdfzx8Lvfwe23p+EWjj0Wpk712PlmXc03TFmXeOMNuOqq1I1zxhlp3PyBA7Ouyiw/dvmGKbNd1a9femjKbbel4RWOOqp5GGUz61wOeutSl14Kv/99et7t8cfDr36VdUVm+eegty531FHpLttDD4V//MfUyjezzuOgt0wMGZJutDr7bLjssjSWjpl1Dge9ZWaPPVLXzaRJ8JnPpKkKrw0w6/b8yGjLVK9e8NOfwt57p1b9hg1w3XV+tKFZR3LQW+ZqauCGG1Lof/vb6S7a66932Jt1FAe9VQUJvvWtFPrXXZduqvre96CHOxfNdpmD3qqGBNdem8L+G99ILfvvf99hb7arHPRWVaQ0+mXPnvDVr6awnzbNYW+2Kxz0VnUk+PKXU8v+mmtgyxa45RaHvdnOctBbVZLgS19K4f7FL0L//qkP31/Qmu04B71VtS98AdasSVfh7Lsv/Nu/ZV2RWffjoLeqJqWrcF57DT7/edhnH/joR7Ouyqx7cdBb1evRA269NbXsr7wS9tsP3v3urKsy6z789ZZ1Cz17pscUHnssfOAD8NhjWVdk1n046K3b6NMnjY0zdCicdx48/3zWFZl1Dw5661Zqa2HmzHTJ5YQJ0NiYdUVm1c9Bb93OW96SWvbLlqXHEjrszbbPQW/d0oknwj33wF/+4rA3a4+D3rqtM89sDvuTT4ZFi7KuyKw6OeitWzvzTJg1C1auhGOOgYcfzrois+rjoLdu7+ST4YknYNCgFPyf+Qz8/e9ZV2VWPRz0lgsHHwyPPw7/9E/pSVXjxsF//7cfTWgGDnrLkf794eab4YEH0jX3558Pb31rGifHX9ZakTnoLXfOOgvmzWt+Fu0nPgGDB8Nxx8G//3v6AveFF9zat+JQVOG/9rq6uqivr8+6DMuJJ5+EX/4S7r0XZs9uXr/77jBiBIwc2Tw1LY8YAQMGpKEXzLoDSXMioq7VbQ56K5I33oD581P4L1oES5Y0T6tXb7t/TU36QGiaevXadurdu/X1Lbf17g19+6ZupT59Knvdp086zuPwW3u2F/Rur1ih9OsHxx+fppZWr24O/RdegLVr09U75dPGjbB+PWzYsPX0+uvbrivfr+n1zpAq/3DYfff0wdD0AdPa6+1t297r3XbzB053VVHQSxoPXA/UALdExH+02K7S9ncBbwKXRMTcSo41qxZ77QVvf3uaOsOWLenDYt06ePPNNG+aypcreV2+vHr11tvKP1g2b+64+qWtw7/8L52mD5ny103z3XZLU8+ezdOOLvfsmYarrqnZet6R66Tm9T16bL3c9Lq7ftC1G/SSaoAbgLOABmC2pBkR8XTZbhOAUaXpGOD7wDEVHmtWCD16pJZ3377paVldYfPm5tBfv37r162tq/T1+vVb/6Wzbl2ar1kDL7/cvLxuXforaNOmNG3c2LEfPllo+SGwo8vb21ZbC7/7XcfXXEmL/mhgUUQsBpA0HZgIlIf1RODHkTr8H5O0l6QhwIgKjjWzTlJT0/zhUi0iUtiXh3/T6+0tb9mSps2bt329q+s2b051RTRv37Jl6+XtbdvV5abX/ft3zjmvJOiHAsvKlhtIrfb29hla4bEASJoMTAYYPnx4BWWZWXckNXfHWNeo5Dr61nqlWl6q09Y+lRybVkZMi4i6iKirra2toCwzM6tEJZ+pDcABZcvDgOUV7tOrgmPNzKwTVdKinw2MkjRSUi9gEjCjxT4zgIuVHAusiYgVFR5rZmadqN0WfURsknQFMIt0ieRtEbFA0pTS9puAmaRLKxeRLq+8dHvHdspvYmZmrfKdsWZmObC9O2M9qJmZWc456M3Mcs5Bb2aWc1XZRy+pEXhhJw4dCKzq4HLyxOenbT43bfO5aVs1nZsDI6LVm5CqMuh3lqT6tr6MMJ+f7fG5aZvPTdu6y7lx142ZWc456M3Mci5vQT8t6wKqnM9P23xu2uZz07ZucW5y1UdvZmbbyluL3szMWnDQm5nlXG6CXtJ4SQslLZI0Net6upqkAyQ9LOkZSQskfby0fh9Jv5H0l9J877Jjri6dr4WS3pld9V1DUo2kP0m6t7TscwOUngh3p6RnS/9+jvO5SSR9svT/6SlJP5e0e7c8NxHR7SfSyJjPAweRxsD/MzA267q6+BwMAY4sve4HPAeMBb4OTC2tnwp8rfR6bOk89QZGls5fTda/Ryefo08BtwP3lpZ9btLv+yPg8tLrXsBePjcB6Ql5S4A+peU7gEu647nJS4v+f59rGxEbgKZn0xZGRKyIiLml128Az5D+oU4k/UemNP+H0uuJwPSIWB8RS0hDTB/dpUV3IUnDgHOAW8pWF/7cSOoPnAzcChARGyJiNT43TXoCfST1BPqSHpzU7c5NXoK+rWfWFpKkEcDbgceBwZEeAkNpPqi0W9HO2beB/wdsKVvnc5P+Cm4E/rPUrXWLpD3wuSEiXgSuBf4KrCA9UOkBuuG5yUvQV/xs2ryTtCdwF/CJiHh9e7u2si6X50zSucDKiJhT6SGtrMvluSG1WI8Evh8RbwfWkroj2lKYc1Pqe59I6obZH9hD0ge2d0gr66ri3OQl6Ct5rm3uSdqNFPI/i4i7S6tfljSktH0IsLK0vkjn7ATg/0haSurWO13ST/G5gfS7NkTE46XlO0nB73MDZwJLIqIxIjYCdwPH0w3PTV6CvvDPppUkUj/rMxFxXdmmGcAHS68/CPyqbP0kSb0ljQRGAU90Vb1dKSKujohhETGC9G/joYj4AD43RMRLwDJJo0urzgCexucGUpfNsZL6lv5/nUH67qvbnZt2nxnbHYSfTQup1XoRMF/SvNK6zwL/Adwh6TLSP9z3AkR67u8dpP/Um4CPRsTmLq86Wz43yZXAz0qNpMWkZz73oODnJiIel3QnMJf0u/6JNOTBnnSzc+MhEMzMci4vXTdmZtYGB72ZWc456M3Mcs5Bb2aWcw56M7Occ9CbmeWcg97MLOf+P1zXbLlXyRH/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(M2epoch_arr,M2loss_arr,color=\"blue\")\n",
    "plt.title('Model 2 Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f15045d1-547d-455d-b346-83e53ae92db7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model 2 weight and bias after training\n",
      "\n",
      "hidden1.weight \t tensor([[-2.3970e-01],\n",
      "        [-1.3580e+00],\n",
      "        [-1.1290e+00],\n",
      "        [-7.8540e-01],\n",
      "        [-3.3802e-05],\n",
      "        [-1.3315e-04],\n",
      "        [-4.4813e-01],\n",
      "        [ 9.4960e-01],\n",
      "        [-4.1677e-01],\n",
      "        [ 1.1673e+00]])\n",
      "hidden1.bias \t tensor([ 0.2114, -0.0222,  0.7718,  0.1538, -0.1023, -0.0465,  0.5481,  0.4628,\n",
      "         0.7266,  0.0050])\n",
      "hidden2.weight \t tensor([[ 1.3294e-01,  2.6491e-02,  1.7536e-02, -3.1111e-01,  2.0872e-02,\n",
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      "          4.1804e-05, -1.8076e-01,  5.3109e-01,  1.4671e-01, -1.6613e-01],\n",
      "        [-1.7959e-01, -4.4855e-01,  1.7090e-01,  3.1794e-02, -8.7453e-03,\n",
      "          6.3200e-03,  2.8322e-01,  2.5270e-01, -2.4328e-01, -2.2226e-01],\n",
      "        [-4.6039e-02,  1.6545e-01, -1.9085e-01, -6.1136e-01, -1.8925e-03,\n",
      "         -2.4585e-05,  1.4656e-01,  2.3708e-01, -1.3670e-01,  4.9997e-01],\n",
      "        [-2.2889e-04, -1.3905e-02, -8.5414e-01, -4.4632e-03,  3.8023e-05,\n",
      "          5.9123e-06, -1.8732e-01,  2.5186e-01, -1.7403e-01,  3.3976e-01],\n",
      "        [ 6.9138e-02, -3.9316e-01,  1.9815e-02,  9.4576e-02, -1.8727e-04,\n",
      "         -8.7850e-05,  7.2861e-03,  5.9412e-02,  5.0088e-02, -4.1907e-01],\n",
      "        [-1.9405e-01,  6.5188e-01,  1.7509e-01, -1.4465e-01,  3.8380e-04,\n",
      "          2.7749e-04,  1.9262e-01,  1.7775e-01, -5.5163e-02,  5.4133e-01],\n",
      "        [ 1.3713e-04,  8.3830e-04, -1.9929e-02,  6.3762e-05,  3.8960e-06,\n",
      "          1.7569e-06,  4.0584e-03, -1.8665e-03, -7.5677e-03,  2.3376e-04],\n",
      "        [-1.2195e-01,  5.0799e-01, -1.9932e-01,  1.4966e-01,  2.4146e-05,\n",
      "         -1.2454e-04,  9.5769e-02, -9.7011e-02,  1.4734e-01, -3.7883e-02]])\n",
      "hidden2.bias \t tensor([ 0.0284, -0.2911,  0.2455,  0.2672,  0.2784,  0.0013, -0.0927, -0.1154,\n",
      "        -0.0176, -0.0081,  0.1748, -0.0325,  0.1315, -0.0626,  0.0059, -0.2626,\n",
      "        -0.0269,  0.2580])\n",
      "hidden3.weight \t tensor([[ 3.3217e-02, -4.3677e-01, -4.2605e-02, -7.2082e-03,  1.8710e-01,\n",
      "          1.1774e-01,  3.8288e-03,  7.7609e-02, -6.0251e-01,  2.8381e-03,\n",
      "          1.7366e-01,  1.2455e-01, -2.1694e-01,  4.0360e-02,  4.4547e-01,\n",
      "         -4.4683e-01, -4.8085e-03, -1.2758e-03],\n",
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      "          9.4637e-02, -3.7524e-04, -1.2650e-02,  1.9815e-01,  1.0017e-02,\n",
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      "          7.9314e-02, -8.8896e-02,  4.0768e-03],\n",
      "        [ 1.5483e-01, -1.1946e-01,  1.5350e-02, -3.4286e-03, -2.4321e-01,\n",
      "         -1.7166e-02, -2.1729e-03, -1.3495e-02,  5.4447e-01,  2.8508e-02,\n",
      "          4.6728e-02,  1.4838e-01,  2.7039e-01, -5.6541e-01, -1.3717e-02,\n",
      "          1.4982e-01,  1.2204e-02, -3.2834e-02],\n",
      "        [ 1.1909e-02,  1.2490e-01,  3.9594e-01,  2.7187e-03, -3.3234e-01,\n",
      "          3.3227e-01, -2.2653e-03, -9.4093e-03, -3.8682e-02, -7.4576e-04,\n",
      "         -5.7199e-01, -5.4634e-01, -9.3171e-02, -6.6139e-03, -5.2333e-02,\n",
      "          1.5843e-01, -1.4104e-03,  3.0214e-01],\n",
      "        [ 3.9819e-02, -2.1177e-01,  2.1161e-01,  9.2344e-02, -2.0271e-01,\n",
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      "          3.2214e-01,  2.2678e-01, -8.2100e-02, -4.0072e-02,  2.0930e-01,\n",
      "         -1.1966e-01, -1.5989e-04,  9.7632e-02],\n",
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      "          1.2695e-01, -1.1956e-02, -8.1708e-02,  3.2590e-01, -1.9209e-03,\n",
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      "         -2.5090e-01, -3.4684e-02, -1.0912e-01],\n",
      "        [ 4.6727e-02,  5.2262e-02, -1.5945e-01, -8.2338e-02, -7.4135e-02,\n",
      "          1.3096e-01,  3.6403e-02,  1.6948e-01,  2.5079e-02, -1.4380e-03,\n",
      "         -2.8995e-02,  5.3692e-02, -2.0988e-01,  3.0060e-02, -6.7308e-02,\n",
      "         -2.6475e-01,  1.6382e-02, -1.2226e-01],\n",
      "        [-9.4415e-03,  2.6915e-01,  1.0024e-02,  1.0337e-01,  3.5336e-01,\n",
      "          1.7988e-01,  6.4944e-04,  2.7782e-03,  2.7692e-01,  1.8991e-04,\n",
      "          2.8024e-01,  1.1967e-01,  4.8398e-02, -5.5863e-01,  2.4341e-01,\n",
      "          2.2140e-01,  4.0907e-04,  8.9710e-02],\n",
      "        [ 1.1524e-01, -3.5657e-01,  1.8208e-01,  1.2873e-01,  1.6592e-01,\n",
      "          3.8395e-02,  6.3569e-03,  2.5243e-03, -4.5820e-01, -2.3040e-02,\n",
      "          6.6883e-02,  2.6621e-01, -3.4231e-01,  3.3492e-02,  2.8038e-01,\n",
      "         -2.0267e-01, -3.2876e-02, -2.1990e-01]])\n",
      "hidden3.bias \t tensor([ 0.1759,  0.1122,  0.2649, -0.0331,  0.0516, -0.0748, -0.0950,  0.0244,\n",
      "        -0.2477, -0.0429,  0.2034,  0.2650,  0.1687, -0.1325, -0.0590])\n",
      "hidden4.weight \t tensor([[-0.8158,  0.2320, -0.5819, -0.0014,  0.4968, -0.0012, -0.5511,  0.2633,\n",
      "         -0.6240,  0.3136,  0.2734,  0.5885, -0.0122, -0.3551, -0.6445],\n",
      "        [ 0.2117,  0.1161,  0.3918, -0.0924, -0.2148,  0.0600, -0.0065, -0.2984,\n",
      "         -0.0461, -0.0111,  0.1801,  0.0750,  0.0741, -0.0229, -0.0107],\n",
      "        [-0.1526,  0.2114, -0.3704,  0.0907,  0.2867,  0.0768, -0.6165,  0.1447,\n",
      "         -0.3489,  0.1610, -0.1612,  0.3229, -0.1372, -0.5434, -0.0633],\n",
      "        [ 0.0725,  0.1632,  0.6959,  0.1530,  0.0614, -0.0419,  0.3884, -0.2455,\n",
      "          0.2227, -0.5386,  0.3231,  0.1106,  0.1803,  0.0796,  0.2121]])\n",
      "hidden4.bias \t tensor([ 0.2434, -0.0343,  0.2470,  0.1330])\n",
      "predict.weight \t tensor([[-0.7799,  0.3414, -0.5368,  0.4288]])\n",
      "predict.bias \t tensor([0.4764])\n"
     ]
    }
   ],
   "source": [
    "#Getting weights & bias\n",
    "print(\"\\nModel 2 weight and bias after training\\n\")\n",
    "for param in m2.state_dict():\n",
    "    print(param, \"\\t\",m2.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fe77cb49-2bf6-4ac3-a63c-d2516864e1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(x.numpy(),y.numpy(),color=\"black\")\n",
    "plt.plot(x,M2prediction.data.numpy(),color=\"yellow\")\n",
    "plt.title('Model Prediction 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "22f286f9-d8f4-4946-bc72-b8e2d6fac656",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no of parameters in Model 3: 571\n",
      "\n",
      "Model 3 weight and bias before training\n",
      "\n",
      "hidden1.weight \t tensor([[-0.0454],\n",
      "        [ 0.1646],\n",
      "        [-0.2346],\n",
      "        [-0.6331],\n",
      "        [-0.4523],\n",
      "        [-0.0664],\n",
      "        [-0.8181],\n",
      "        [-0.8082],\n",
      "        [ 0.1097],\n",
      "        [-0.6507],\n",
      "        [-0.5439],\n",
      "        [-0.0086],\n",
      "        [-0.5270],\n",
      "        [ 0.9256],\n",
      "        [-0.3610],\n",
      "        [-0.5560],\n",
      "        [-0.2664],\n",
      "        [-0.8159],\n",
      "        [ 0.5152],\n",
      "        [ 0.8592],\n",
      "        [-0.5767],\n",
      "        [-0.3717],\n",
      "        [ 0.9333],\n",
      "        [-0.7234],\n",
      "        [ 0.2708],\n",
      "        [-0.8148],\n",
      "        [-0.8900],\n",
      "        [ 0.0015],\n",
      "        [-0.5707],\n",
      "        [ 0.5416],\n",
      "        [-0.3093],\n",
      "        [ 0.0099],\n",
      "        [-0.5221],\n",
      "        [ 0.4109],\n",
      "        [ 0.9265],\n",
      "        [-0.1503],\n",
      "        [-0.6172],\n",
      "        [-0.9753],\n",
      "        [ 0.9583],\n",
      "        [ 0.4215],\n",
      "        [ 0.4130],\n",
      "        [ 0.1703],\n",
      "        [ 0.5018],\n",
      "        [ 0.3551],\n",
      "        [ 0.4883],\n",
      "        [ 0.6254],\n",
      "        [ 0.1418],\n",
      "        [ 0.8613],\n",
      "        [ 0.6218],\n",
      "        [-0.0306],\n",
      "        [-0.4466],\n",
      "        [ 0.0663],\n",
      "        [ 0.7788],\n",
      "        [ 0.6335],\n",
      "        [-0.4052],\n",
      "        [-0.2088],\n",
      "        [ 0.3970],\n",
      "        [ 0.3768],\n",
      "        [ 0.9522],\n",
      "        [ 0.4856],\n",
      "        [ 0.3186],\n",
      "        [ 0.2311],\n",
      "        [-0.9398],\n",
      "        [-0.3684],\n",
      "        [ 0.1058],\n",
      "        [-0.6957],\n",
      "        [-0.8762],\n",
      "        [ 0.6748],\n",
      "        [ 0.3401],\n",
      "        [-0.8070],\n",
      "        [ 0.0594],\n",
      "        [ 0.5889],\n",
      "        [ 0.5761],\n",
      "        [ 0.9132],\n",
      "        [-0.3954],\n",
      "        [ 0.4453],\n",
      "        [-0.8112],\n",
      "        [-0.1233],\n",
      "        [-0.6360],\n",
      "        [-0.1904],\n",
      "        [ 0.5693],\n",
      "        [ 0.4985],\n",
      "        [-0.5260],\n",
      "        [-0.2480],\n",
      "        [-0.2001],\n",
      "        [-0.3804],\n",
      "        [-0.5478],\n",
      "        [ 0.7343],\n",
      "        [ 0.6912],\n",
      "        [ 0.8348],\n",
      "        [-0.5491],\n",
      "        [ 0.7323],\n",
      "        [-0.3032],\n",
      "        [-0.2349],\n",
      "        [-0.2692],\n",
      "        [-0.0486],\n",
      "        [-0.4379],\n",
      "        [ 0.1368],\n",
      "        [-0.6445],\n",
      "        [ 0.4113],\n",
      "        [-0.8386],\n",
      "        [-0.3625],\n",
      "        [-0.6985],\n",
      "        [ 0.6746],\n",
      "        [ 0.6870],\n",
      "        [-0.7954],\n",
      "        [-0.5457],\n",
      "        [ 0.8950],\n",
      "        [ 0.2914],\n",
      "        [ 0.6886],\n",
      "        [-0.7667],\n",
      "        [ 0.4513],\n",
      "        [ 0.6365],\n",
      "        [-0.2320],\n",
      "        [-0.0461],\n",
      "        [ 0.7292],\n",
      "        [ 0.2727],\n",
      "        [ 0.2869],\n",
      "        [ 0.0030],\n",
      "        [-0.4175],\n",
      "        [-0.6121],\n",
      "        [ 0.9979],\n",
      "        [-0.0790],\n",
      "        [ 0.8874],\n",
      "        [ 0.8182],\n",
      "        [-0.5229],\n",
      "        [ 0.0680],\n",
      "        [-0.2558],\n",
      "        [-0.0182],\n",
      "        [ 0.6591],\n",
      "        [ 0.5507],\n",
      "        [-0.5680],\n",
      "        [-0.4948],\n",
      "        [-0.7104],\n",
      "        [-0.8490],\n",
      "        [ 0.6415],\n",
      "        [-0.6179],\n",
      "        [ 0.0131],\n",
      "        [-0.0014],\n",
      "        [ 0.1711],\n",
      "        [-0.0585],\n",
      "        [ 0.5189],\n",
      "        [ 0.1373],\n",
      "        [ 0.7171],\n",
      "        [ 0.2159],\n",
      "        [-0.0125],\n",
      "        [ 0.1417],\n",
      "        [ 0.6665],\n",
      "        [ 0.1509],\n",
      "        [ 0.7045],\n",
      "        [-0.0948],\n",
      "        [ 0.0087],\n",
      "        [ 0.8401],\n",
      "        [-0.9441],\n",
      "        [ 0.0998],\n",
      "        [ 0.9134],\n",
      "        [-0.5018],\n",
      "        [-0.1845],\n",
      "        [-0.0602],\n",
      "        [ 0.3294],\n",
      "        [ 0.3780],\n",
      "        [ 0.2265],\n",
      "        [ 0.3945],\n",
      "        [-0.4291],\n",
      "        [ 0.4207],\n",
      "        [-0.3171],\n",
      "        [ 0.6283],\n",
      "        [-0.2935],\n",
      "        [-0.8515],\n",
      "        [ 0.0714],\n",
      "        [ 0.5203],\n",
      "        [-0.0392],\n",
      "        [ 0.0130],\n",
      "        [ 0.0035],\n",
      "        [ 0.5307],\n",
      "        [ 0.0782],\n",
      "        [-0.8933],\n",
      "        [-0.7132],\n",
      "        [ 0.0591],\n",
      "        [-0.0803],\n",
      "        [ 0.9559],\n",
      "        [-0.2351],\n",
      "        [ 0.7653],\n",
      "        [-0.8434],\n",
      "        [-0.9504],\n",
      "        [-0.0920],\n",
      "        [-0.6332],\n",
      "        [-0.2916],\n",
      "        [ 0.1126],\n",
      "        [-0.8674]])\n",
      "hidden1.bias \t tensor([ 0.6872, -0.6069,  0.7673,  0.3269, -0.1875, -0.8751,  0.1655, -0.5587,\n",
      "         0.8643, -0.8413, -0.2642, -0.5511, -0.6432, -0.5216, -0.0991,  0.0676,\n",
      "         0.1694,  0.5110, -0.4085, -0.6205, -0.2107,  0.9424, -0.1222, -0.8419,\n",
      "         0.7847,  0.2433,  0.1736,  0.1716,  0.6977, -0.4553,  0.7288,  0.9616,\n",
      "        -0.2579,  0.6311,  0.5517,  0.9365, -0.2327, -0.5949, -0.3712, -0.6803,\n",
      "         0.0343,  0.9641,  0.9701,  0.7554,  0.0990,  0.0217,  0.6621,  0.5938,\n",
      "        -0.0992, -0.2529,  0.3954, -0.0347, -0.7025, -0.4704,  0.8110,  0.8328,\n",
      "        -0.3897, -0.2146, -0.4956, -0.3265,  0.9116, -0.8894,  0.4772, -0.8988,\n",
      "        -0.4771, -0.2036,  0.0693, -0.1624, -0.4571, -0.4460, -0.6082, -0.6980,\n",
      "        -0.6230,  0.9589, -0.4712, -0.8316,  0.6873,  0.3880,  0.6879, -0.9947,\n",
      "        -0.9519, -0.7097, -0.5268, -0.0022,  0.4326,  0.5855, -0.8601,  0.3988,\n",
      "        -0.5783,  0.2299,  0.3140,  0.1600,  0.3538, -0.0499,  0.6602,  0.2086,\n",
      "         0.8761,  0.2168, -0.5294,  0.6422,  0.0542,  0.9664, -0.1106,  0.9894,\n",
      "        -0.5838,  0.5026, -0.7460,  0.6865,  0.1815,  0.5677, -0.8612, -0.9948,\n",
      "         0.4818, -0.4114,  0.3960, -0.5933, -0.3091,  0.0045,  0.6022,  0.5647,\n",
      "        -0.0893, -0.2829, -0.0173,  0.5154, -0.8476, -0.3056, -0.9640,  0.1443,\n",
      "        -0.1183,  0.5665,  0.7989, -0.8550, -0.4426, -0.1608, -0.0100, -0.2283,\n",
      "        -0.6287, -0.3864, -0.0258,  0.8042, -0.2549,  0.6604, -0.8084,  0.0447,\n",
      "        -0.7804, -0.9384,  0.0991, -0.4579,  0.7362,  0.7899, -0.9953, -0.0697,\n",
      "        -0.1708, -0.5700, -0.6981, -0.4232,  0.4415,  0.2378,  0.6019, -0.0596,\n",
      "         0.5194,  0.1934, -0.2181, -0.0585, -0.9095, -0.0230, -0.3285, -0.3347,\n",
      "        -0.4016, -0.2186, -0.7383,  0.0587, -0.0406, -0.2634,  0.8681,  0.6711,\n",
      "         0.2824, -0.3683,  0.1275, -0.6881,  0.6968,  0.9340, -0.1493,  0.7712,\n",
      "         0.9385, -0.0601, -0.3713,  0.0505,  0.1653, -0.9257])\n",
      "predict.weight \t tensor([[-0.0045, -0.0262,  0.0484, -0.0021,  0.0477,  0.0645, -0.0227,  0.0156,\n",
      "         -0.0062, -0.0316, -0.0402,  0.0017,  0.0220, -0.0128,  0.0123,  0.0453,\n",
      "          0.0302,  0.0299,  0.0204, -0.0558, -0.0439, -0.0538,  0.0477, -0.0639,\n",
      "         -0.0542,  0.0478,  0.0429, -0.0686,  0.0419, -0.0166,  0.0156, -0.0454,\n",
      "         -0.0110,  0.0161,  0.0553,  0.0440, -0.0394, -0.0476, -0.0044,  0.0228,\n",
      "          0.0398, -0.0723, -0.0059,  0.0026, -0.0339,  0.0572, -0.0193, -0.0692,\n",
      "         -0.0433,  0.0023, -0.0540,  0.0166,  0.0706,  0.0030, -0.0179,  0.0683,\n",
      "          0.0536,  0.0482,  0.0055,  0.0033, -0.0563,  0.0074,  0.0706,  0.0038,\n",
      "         -0.0647,  0.0398,  0.0651,  0.0608,  0.0034,  0.0136,  0.0117, -0.0297,\n",
      "         -0.0106,  0.0592,  0.0074,  0.0684, -0.0482, -0.0106,  0.0009, -0.0432,\n",
      "          0.0560,  0.0507, -0.0391,  0.0639,  0.0676, -0.0427, -0.0025,  0.0723,\n",
      "         -0.0471, -0.0114, -0.0498,  0.0523,  0.0118,  0.0302,  0.0521, -0.0115,\n",
      "         -0.0725,  0.0036, -0.0056,  0.0263,  0.0339, -0.0361, -0.0286,  0.0457,\n",
      "          0.0207,  0.0166,  0.0323,  0.0337, -0.0524,  0.0058,  0.0069, -0.0701,\n",
      "          0.0154, -0.0010,  0.0563, -0.0692,  0.0682, -0.0355,  0.0427,  0.0536,\n",
      "         -0.0090, -0.0371, -0.0120, -0.0452,  0.0090,  0.0305,  0.0150, -0.0418,\n",
      "          0.0396,  0.0666, -0.0221,  0.0377, -0.0048,  0.0292, -0.0517, -0.0619,\n",
      "         -0.0021, -0.0611,  0.0261,  0.0679, -0.0142,  0.0007,  0.0110, -0.0351,\n",
      "         -0.0037,  0.0187,  0.0587, -0.0602, -0.0014, -0.0592, -0.0064, -0.0019,\n",
      "         -0.0303,  0.0445,  0.0498, -0.0281, -0.0101, -0.0222,  0.0020,  0.0239,\n",
      "          0.0671, -0.0665,  0.0295, -0.0437, -0.0592,  0.0559, -0.0216, -0.0407,\n",
      "          0.0199, -0.0151, -0.0664, -0.0378, -0.0306, -0.0057, -0.0479, -0.0699,\n",
      "          0.0403, -0.0623, -0.0088,  0.0623, -0.0267, -0.0480,  0.0194,  0.0633,\n",
      "         -0.0080, -0.0043,  0.0339,  0.0091,  0.0139,  0.0384]])\n",
      "predict.bias \t tensor([0.0330])\n"
     ]
    }
   ],
   "source": [
    "class M3(torch.nn.Module):\n",
    "    def __init__(self,):\n",
    "        super(M3, self).__init__()\n",
    "        self.hidden1 = torch.nn.Linear(1, 190)   # hidden layer\n",
    "        self.predict = torch.nn.Linear(190, 1)   # output layer\n",
    "        #self.activation = nn.LeakyReLU()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.leaky_relu(self.hidden1(x))      # activation function for hidden layer\n",
    "        x = self.predict(x)                    # linear output\n",
    "        return x\n",
    "\n",
    "m3 = M3()     # define the network\n",
    "#optimizer = torch.optim.RMSprop(m1.parameters(), lr=0.0012, weight_decay = 1e-4)\n",
    "optimizer = torch.optim.Adam(m3.parameters(), lr=0.0011, weight_decay = 1e-4)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss\n",
    "\n",
    "a=[]\n",
    "for i in m3.parameters():\n",
    "    a.append(torch.numel(i))\n",
    "print('Total no of parameters in Model 3:', np.sum(a))\n",
    "\n",
    "\n",
    "#Getting weights & bias\n",
    "print(\"\\nModel 3 weight and bias before training\\n\")\n",
    "for param in m3.state_dict():\n",
    "    print(param, \"\\t\",m3.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1f65a4e6-d144-41e7-b556-3da041196e70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convergeance reached for loss:0.0010 at epoch: 2186\n"
     ]
    }
   ],
   "source": [
    "M3epoch_arr,M3loss_arr,M3prediction = trainFunc(m3,x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "648fc988-c22d-459f-a316-fe8bf86b5f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9tehqzMx6xkGf0+zZsHEj3H570ZWYmfWMgz6nKVNg1Ch335hZ/+Ogz6mpCc49FxYsgBdeKLoaM7P8HPQ98MEPpkssb7ml6ErMzPJz0PfA298OEybAj39cdCVmZvk56HtAgvPPh7vuglWriq7GzCwfB30PfehD6fn0PilrZv1FrqCXdIak5ZLaJM2rslySLs2WL5E0udPyJkkPS+r3FycefjicfLK7b8ys/+g26CU1AZcB04BJwGxJkzo1mwZMyKY5wOWdln8GWLbH1daJ88+H3/0OHn206ErMzLqX54j+BKAtIlZExGbgemB6pzbTgWsjWQQMkzQKQNIY4CzgqhrWXahzz4W99/ZRvZn1D3mCfjRQeepxdTYvb5vvAv8HeGNXHyJpjqRWSa3t7e05yirOiBEwbRr89KcekMTM6l+eoFeVeZ2Hy67aRtLZwLqIeKi7D4mIKyKiJSJaRo4cmaOsYn3kI7BmDSxcWHQlZma7lifoVwNjK34eA6zJ2eYU4H2SniJ1+bxbUik6PN77XjjoILiqNB1SZlZWeYL+QWCCpPGSmoFZwPxObeYDF2RX35wEvBgRayPiSxExJiLGZevdGRHn1/ILFKW5OR3V/+IXaahBM7N61W3QR8RWYC6wkHTlzA0RsVTSRZIuypotAFYAbcCVwKd6qd668vGPw9at8KMfFV2JmVnXFNG5u714LS0t0draWnQZuUyZAmvXwhNPpDtnzcyKIOmhiGiptsx3xu6hT34S2trgnnuKrsTMrDoH/R6aORP23x+uvLLoSszMqnPQ76H99oMPfxhuvBHWrSu6GjOznTnoa2Du3PSc+iuuKLoSM7OdOehr4Kij4PTT4fLLYcuWoqsxM9uRg75GLr443Snr0afMrN446Gtk2jQ47DD43veKrsTMbEcO+hppakp99ffdBw8/XHQ1ZmbbOehr6MIL01U4Pqo3s3rioK+hYcPgggvS44vr/EnLZtZAHPQ1NncubNqUrsAxM6sHDvoaO/poOPtsuPRS2Lix6GrMzBz0veJLX4Lnn/djEcysPjjoe8E73gGnngrf+lbqxjEzK5KDvpd8+cvw9NNw3XVFV2Jmjc5B30umToXjj4dLLkmDk5iZFcVB30sk+Nu/Tc+q/8lPiq7GzBqZg74XTZ8OkyfDP/xDerqlmVkRHPS9SIKvfhWefBJ+8IOiqzGzRuWg72VnnAEnn5wC//XXi67GzBqRg76XdRzVr17tgUnMrBgO+j7w7nfDu96VAv+ll4quxswajYO+j3zzm+lBZ9/4RtGVmFmjcdD3kZYWOP98+M53YOXKoqsxs0bioO9DX/966rP/8peLrsTMGomDvg+NHQtf+EJ6Xv0DDxRdjZk1Cgd9H/viF+Hgg9Ng4tu2FV2NmTUCB30fGzIk9dM/8IAvtzSzvuGgL8Ds2XDaaem59c88U3Q1ZlZ2DvoCSPBv/wavvZb67M3MelOuoJd0hqTlktokzauyXJIuzZYvkTQ5mz9W0n9JWiZpqaTP1PoL9FdHHpmO6H/6U7jjjqKrMbMy6zboJTUBlwHTgEnAbEmTOjWbBkzIpjlAx9DYW4EvRMRfACcBf11l3YY1bx5MnAgf+xi88ELR1ZhZWeU5oj8BaIuIFRGxGbgemN6pzXTg2kgWAcMkjYqItRGxGCAiXgaWAaNrWH+/ts8+aQSqZ56BT3+66GrMrKzyBP1oYFXFz6vZOay7bSNpHHAccH+1D5E0R1KrpNb29vYcZZVDS0saoOS66+Dmm4uuxszKKE/Qq8q86EkbSYOBm4HPRkTVx3pFxBUR0RIRLSNHjsxRVnl85Stp2ME5c2DVqu7bm5n1RJ6gXw2Mrfh5DLAmbxtJA0gh/5OIuGX3Sy2vAQPSSdnNm+G882DLlqIrMrMyyRP0DwITJI2X1AzMAuZ3ajMfuCC7+uYk4MWIWCtJwNXAsoj4Tk0rL5kjj4SrroLf/jZdjWNmVit7d9cgIrZKmgssBJqAayJiqaSLsuXfBxYAZwJtwKvAhdnqpwAfBn4n6ZFs3pcjYkFNv0VJnHce/OY38O1vwymnwIwZRVdkZmWgiM7d7cVraWmJ1tbWossoxKZN8Jd/CY8/DvfeC8ceW3RFZtYfSHooIlqqLfOdsXVm4EC47TYYPhzOPhvWdD4bYmbWQw76OjRqFNx+e7qJ6n3vg5dfLroiM+vPHPR16phj4Prr4ZFHYPp0eP31oisys/7KQV/Hzj4bfvhDuOsu+MAHfNmlme0eB32dO//89KTL229PjzfetKnoisysv+n28kor3kUXpa6bz30u9dffcgsMGlR0VWbWX/iIvp/47Gfh6qvhP/4Dpk6F554ruiIz6y8c9P3Ixz4GN9wAixfDCSfAY48VXZGZ9QcO+n5m5ky4++7UlXPyyXDrrUVXZGb1zkHfD514Ijz4YBq0ZMYMmDs3DUtoZlaNg76fGj06PSLh85+Hyy5Lz7Vv0KdGmFk3HPT92MCB6QFod9wBGzakI/2LL4YXXyy6MjOrJw76Epg6FZYtg099Kh3dT5yYrtDZurXoysysHjjoS2L//eF734MHHoBDD4VPfAKOPjo9RmHbtqKrM7MiOehLpqUlDV5y661p5KrZs2HCBPjud+GlqoM4mlnZOehLSEoPQnv0UbjpJjjkkHRX7ZgxqXtn0SKow2EIzKyXOOhLrKkpXXd/772pS2f69PSQtJNPhqOOgr/7u3SZ5htvFF2pmfUmB32DePvb4brr4Jln4Jpr0uWZX/tausN29OjUp3/jjfDss0VXama15qEEG9hzz8Evf5mejPmrX23vw584EU49FaZMgeOPT338e/mQwKyu7WooQQe9AelZ94sXp8cr3H136u7pCP7Bg9NAKJMnw9velrp9jjoKRo5M5wPMrHgOeuuxrVth6dIU/g8/nF4feQQ2btzeZtiw7aE/fny6rPPNb06vY8emG7rMrG846K0mtm2DlSth+fIdpyeeSIOYd/6n9KY3peA/5JD0vnIaNSq9HnywdwhmtbCroPfAI5ZbUxMcdliapk3bcdmmTfD002lH8Kc/pdeVK2HVKmhrS11BXT1Df/jwFPojR3Y/jRiR7g8ws/wc9FYTAwdu3wl0ZcsWWLcO1q5NV/90TB0/t7en7qL2dli/vutr/YcN2x76eXYO++7bK1/ZrN9w0FufGTAgXco5enT3bbdtS2Hf3r7r6ckn0z0Czz3X9bN9Bg3Kt0PomAYP9klmKxcHvdWlpqbtwZtHBLzwQvc7hrVrYcmS9L6rgdYHDqy+AzjwwDQdcMDO7wcN8s7B6peD3kpBSn39w4fDkUd23z4CXnkl/SXQ3c7hD39Ir6+80vXva27eHvrVdgQd7w84AIYOTQ+h63htbq7ddjCrxkFvDUmCIUPSNH58vnU2bUrP/X/++e3T+vXV37e1wf33p/ebN+/69w4cuGPwDx26885gV68dU1PTnm8XKycHvVlOAwduvzw0rwh49dXtO4ANG9KNaC++mF4r31e+rlixY5s8zyMaNCjfTmHIENhvv+3ToEHVf95nH3dHlYWD3qwXSSk4Bw1K9xTsjoh0o1pXO4Vd7TBWr97+8666nrrS3c4gzw6jctpnn7TDrDZ5p9J7HPRmdU5KVwINHpxuPttd27bByy+nwH/11R2njRvzzeuYv3592olUztu4cc+ehDpgQNc7gV1Nzc2w9971OdXLzitX0Es6A/gXoAm4KiK+0Wm5suVnAq8CH42IxXnWNbO+0dSU7kEYNqx3fn9Euleiq53Fxo3w+uvpXMfuTh07mcp5mzenndjWrTtPRdtrr13vCJqaUpuO6aCD4J57al9Ht0EvqQm4DJgKrAYelDQ/Ih6vaDYNmJBNJwKXAyfmXNfMSkBKR9fNzb23M+mJiPQXRrUdQD1MW7ak+jqmbdvSuZTekOeI/gSgLSJWAEi6HpgOVIb1dODaSA/OWSRpmKRRwLgc65qZ1ZyUjpibmvw8pTxPGR8NrKr4eXU2L0+bPOsCIGmOpFZJre3t7TnKMjOzPPIEfbXTCZ2fQtJVmzzrppkRV0RES0S0jMx7O6SZmXUrT9fNamBsxc9jgDU52zTnWNfMzHpRniP6B4EJksZLagZmAfM7tZkPXKDkJODFiFibc10zM+tF3R7RR8RWSXOBhaRLJK+JiKWSLsqWfx9YQLq0so10eeWFu1q3V76JmZlV5RGmzMxKYFcjTOXpujEzs37MQW9mVnJ12XUjqR1YuZurjwC6GJ20YXmbVOftUp23y876wzY5NCKqXptel0G/JyS1dtVP1ai8TarzdqnO22Vn/X2buOvGzKzkHPRmZiVXxqC/ougC6pC3SXXeLtV5u+ysX2+T0vXRm5nZjsp4RG9mZhUc9GZmJVeaoJd0hqTlktokzSu6nr4m6SlJv5P0iKTWbN4Bkn4t6Q/Z6/CK9l/KttVySf+juMprR9I1ktZJeqxiXo+3gaTjs23ZJunSbKjMfquL7fL3kp7O/r08IunMimWl3y6Sxkr6L0nLJC2V9Jlsfjn/vUREv59ID0z7I3AY6dHIjwKTiq6rj7fBU8CITvO+CczL3s8DLsneT8q20UBgfLbtmor+DjXYBlOAycBje7INgAeAk0njKfwSmFb0d+uF7fL3wN9UadsQ2wUYBUzO3g8Bnsi+eyn/vZTliP7Pwx1GxGagY8jCRjcd+FH2/kfA+yvmXx8RmyLiSdJTR0/o+/JqKyLuAdZ3mt2jbZANgTk0In4b6X/xtRXr9EtdbJeuNMR2iYi1EbE4e/8ysIw0+l0p/72UJehzD1lYYgHcIekhSXOyeQdHGheA7PWgbH4jba+eboPR2fvO88torqQlWddORxdFw20XSeOA44D7Kem/l7IEfe4hC0vslIiYDEwD/lrSlF209faqwfCX/dzlwOHAscBa4NvZ/IbaLpIGAzcDn42Il3bVtMq8frNdyhL0eYY7LLWIWJO9rgN+TuqKeTb705LsdV3WvJG2V0+3wersfef5pRIRz0bEtoh4A7iS7V13DbNdJA0ghfxPIuKWbHYp/72UJegbeshCSYMkDel4D5wOPEbaBh/Jmn0EuC17Px+YJWmgpPHABNIJpTLq0TbI/lx/WdJJ2dUTF1SsUxodYZaZQfr3Ag2yXbLvcDWwLCK+U7GonP9eij4bXKuJNJThE6Sz4V8pup4+/u6Hka4IeBRY2vH9gQOB/wT+kL0eULHOV7JttZw6vEpgN7fDz0jdEFtIR1of351tALSQgu+PwL+S3UHeX6cutst1wO+AJaQQG9VI2wV4J6mLZQnwSDadWdZ/L34EgplZyZWl68bMzLrgoDczKzkHvZlZyTnozcxKzkFvZlZyDnozs5Jz0JuZldz/B/6ubWunSMLYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(M3epoch_arr,M3loss_arr,color=\"blue\")\n",
    "plt.title('Model 3 Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7a70937b-f84c-40a5-950e-4df86c57676b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model 3 weight and bias before training\n",
      "\n",
      "hidden1.weight \t tensor([[-2.3371e-03],\n",
      "        [ 1.9513e-10],\n",
      "        [-6.2470e-02],\n",
      "        [-5.1103e-01],\n",
      "        [-7.6421e-01],\n",
      "        [ 4.4952e-08],\n",
      "        [-1.0274e+00],\n",
      "        [-1.3180e+00],\n",
      "        [-2.6312e-03],\n",
      "        [-1.4882e-05],\n",
      "        [-6.4233e-01],\n",
      "        [ 3.1291e-11],\n",
      "        [-1.2683e-07],\n",
      "        [ 9.0053e-01],\n",
      "        [-9.6483e-01],\n",
      "        [-3.9866e-01],\n",
      "        [-5.8649e-01],\n",
      "        [-3.6529e-01],\n",
      "        [ 1.1653e+00],\n",
      "        [ 1.8475e-01],\n",
      "        [-1.0174e+00],\n",
      "        [-8.3216e-02],\n",
      "        [ 4.0773e-01],\n",
      "        [-9.7738e-05],\n",
      "        [ 6.0134e-02],\n",
      "        [-6.8029e-01],\n",
      "        [-5.8089e-01],\n",
      "        [ 2.4336e-03],\n",
      "        [-1.0753e-01],\n",
      "        [ 2.2699e-02],\n",
      "        [-1.1831e-02],\n",
      "        [ 1.0039e-03],\n",
      "        [-8.6869e-01],\n",
      "        [ 1.3841e-03],\n",
      "        [ 5.6310e-01],\n",
      "        [-4.0756e-02],\n",
      "        [-1.0577e+00],\n",
      "        [-1.5748e+00],\n",
      "        [ 1.2686e+00],\n",
      "        [ 3.3217e-09],\n",
      "        [ 2.5694e-01],\n",
      "        [ 5.7215e-02],\n",
      "        [-2.2671e-03],\n",
      "        [-2.9390e-03],\n",
      "        [ 8.7054e-01],\n",
      "        [ 4.7201e-01],\n",
      "        [-7.5512e-04],\n",
      "        [ 3.2229e-01],\n",
      "        [ 9.3455e-01],\n",
      "        [-1.3452e-23],\n",
      "        [-1.1977e-01],\n",
      "        [ 4.2713e-05],\n",
      "        [ 8.1451e-01],\n",
      "        [ 1.2188e+00],\n",
      "        [-8.7155e-03],\n",
      "        [-8.2480e-02],\n",
      "        [ 7.4844e-03],\n",
      "        [ 8.1253e-01],\n",
      "        [ 9.5213e-01],\n",
      "        [ 1.1403e+00],\n",
      "        [ 7.5252e-02],\n",
      "        [ 5.0127e-08],\n",
      "        [-8.3318e-01],\n",
      "        [ 8.8381e-08],\n",
      "        [ 7.7920e-13],\n",
      "        [-6.6369e-01],\n",
      "        [-5.1295e-01],\n",
      "        [ 5.7104e-01],\n",
      "        [ 1.2327e-12],\n",
      "        [-1.1282e+00],\n",
      "        [ 2.1494e-10],\n",
      "        [ 2.0692e-06],\n",
      "        [ 1.1865e-06],\n",
      "        [ 2.7004e-01],\n",
      "        [ 4.1753e-11],\n",
      "        [ 4.0290e-08],\n",
      "        [-2.2443e-01],\n",
      "        [-2.1531e-03],\n",
      "        [-2.4052e-03],\n",
      "        [ 1.6812e-07],\n",
      "        [ 1.1665e-06],\n",
      "        [ 3.8463e-08],\n",
      "        [-1.1064e-01],\n",
      "        [-2.8963e-01],\n",
      "        [-7.7875e-02],\n",
      "        [-6.0278e-02],\n",
      "        [-2.1875e-07],\n",
      "        [ 5.1942e-01],\n",
      "        [ 1.7920e-01],\n",
      "        [ 1.0996e+00],\n",
      "        [-1.1379e+00],\n",
      "        [ 5.7843e-01],\n",
      "        [-5.9368e-03],\n",
      "        [-5.0389e-01],\n",
      "        [-7.5583e-02],\n",
      "        [-1.9392e-03],\n",
      "        [-1.4364e-01],\n",
      "        [-2.2180e-03],\n",
      "        [-1.3094e+00],\n",
      "        [ 2.0340e-02],\n",
      "        [-7.3126e-01],\n",
      "        [-4.0928e-02],\n",
      "        [-9.6169e-01],\n",
      "        [ 1.3339e-01],\n",
      "        [ 5.8369e-01],\n",
      "        [-2.3692e-01],\n",
      "        [-3.1992e-07],\n",
      "        [ 4.3485e-01],\n",
      "        [ 8.0202e-01],\n",
      "        [ 1.4321e-01],\n",
      "        [-2.2294e-04],\n",
      "        [ 2.1912e-07],\n",
      "        [ 2.3089e-01],\n",
      "        [ 2.6724e-13],\n",
      "        [-2.6377e-02],\n",
      "        [ 2.6426e-01],\n",
      "        [-2.3742e-12],\n",
      "        [ 7.4986e-01],\n",
      "        [-1.3324e-02],\n",
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      "        [-9.3432e-01],\n",
      "        [ 1.2501e+00],\n",
      "        [-7.6221e-01],\n",
      "        [ 9.0692e-01],\n",
      "        [ 5.4055e-04],\n",
      "        [-1.0309e+00],\n",
      "        [ 1.1446e-07],\n",
      "        [-6.8951e-01],\n",
      "        [ 1.0428e-19],\n",
      "        [ 5.1289e-01],\n",
      "        [ 1.5515e-02],\n",
      "        [-8.4412e-07],\n",
      "        [-5.8719e-01],\n",
      "        [-4.9333e-01],\n",
      "        [-1.0168e+00],\n",
      "        [ 1.0725e+00],\n",
      "        [-5.3724e-06],\n",
      "        [ 6.2478e-16],\n",
      "        [-5.4797e-02],\n",
      "        [ 4.0221e-02],\n",
      "        [-6.8312e-24],\n",
      "        [-1.8487e-03],\n",
      "        [ 1.6655e-08],\n",
      "        [ 9.5857e-01],\n",
      "        [ 1.1017e-08],\n",
      "        [ 9.0003e-08],\n",
      "        [ 5.5026e-01],\n",
      "        [ 1.4263e+00],\n",
      "        [-2.3546e-03],\n",
      "        [ 2.0974e-01],\n",
      "        [ 1.5853e-07],\n",
      "        [-9.4183e-01],\n",
      "        [ 1.0965e+00],\n",
      "        [-6.0556e-01],\n",
      "        [ 2.4593e-09],\n",
      "        [ 6.4732e-01],\n",
      "        [-2.5417e-03],\n",
      "        [-6.6767e-03],\n",
      "        [-2.9799e-03],\n",
      "        [ 2.9684e-01],\n",
      "        [ 1.0893e-01],\n",
      "        [ 7.6051e-02],\n",
      "        [ 8.8110e-01],\n",
      "        [-7.8536e-01],\n",
      "        [ 9.1449e-08],\n",
      "        [-3.1628e-01],\n",
      "        [ 7.1505e-01],\n",
      "        [ 4.1288e-13],\n",
      "        [-9.9703e-01],\n",
      "        [-2.5402e-26],\n",
      "        [ 1.5326e-07],\n",
      "        [-3.5982e-03],\n",
      "        [-6.1679e-02],\n",
      "        [ 3.0346e-23],\n",
      "        [ 1.0064e-01],\n",
      "        [ 2.6422e-02],\n",
      "        [-6.8755e-01],\n",
      "        [-4.9518e-01],\n",
      "        [-7.8573e-04],\n",
      "        [ 2.2539e-09],\n",
      "        [ 1.0392e-01],\n",
      "        [-4.2953e-02],\n",
      "        [ 9.9668e-01],\n",
      "        [-5.7455e-01],\n",
      "        [-1.7619e-02],\n",
      "        [-8.1515e-01],\n",
      "        [-1.0561e+00],\n",
      "        [-8.6074e-01],\n",
      "        [-4.8257e-03],\n",
      "        [-1.1360e-03]])\n",
      "hidden1.bias \t tensor([ 2.1255e-02, -3.5294e-06,  3.2908e-01,  1.4292e-01, -1.8360e-01,\n",
      "        -1.3246e-03, -3.4486e-03, -3.1932e-01,  3.0990e-02, -7.5621e-04,\n",
      "        -1.6985e-01, -3.9585e-07, -1.2525e-05, -2.4145e-01, -3.4409e-02,\n",
      "         1.1195e-01,  1.6308e-01,  2.5290e-01, -2.6626e-01, -1.6256e-01,\n",
      "        -6.7937e-03,  2.1836e-01, -1.7726e-02, -7.6624e-04,  1.6244e-01,\n",
      "         1.9065e-01,  1.6391e-01,  3.0442e-02,  2.5820e-01, -1.9945e-02,\n",
      "         1.1178e-01,  1.8090e-01, -2.3676e-01,  8.3304e-02,  3.8978e-01,\n",
      "         3.6596e-01, -6.7154e-03, -7.5334e-01, -5.1998e-02, -3.4979e-05,\n",
      "         7.3060e-02,  3.0133e-01,  3.1032e-02,  2.8537e-02, -3.3741e-02,\n",
      "        -2.0197e-02,  3.0384e-02,  2.8341e-01, -3.2782e-02,  3.3303e-23,\n",
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      "         4.4176e-01, -6.5818e-03, -1.8373e-01, -2.5524e-01, -2.8590e-01,\n",
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      "        -1.8223e-01,  1.4570e-01, -1.5400e-01, -1.4851e-09, -3.0710e-01,\n",
      "        -3.9694e-06, -5.0345e-05, -6.2491e-06,  4.1919e-01, -4.7894e-09,\n",
      "        -7.2950e-04,  1.9750e-01,  1.6758e-02,  1.9132e-02, -4.7316e-03,\n",
      "        -3.2362e-03, -7.6346e-05, -9.7262e-02, -6.4887e-02,  2.6039e-01,\n",
      "         8.6351e-02, -1.0040e-03,  3.5967e-01, -1.5756e-01, -3.8557e-02,\n",
      "         5.4420e-01,  1.6447e-01,  4.4837e-02, -1.1282e-01,  3.0498e-01,\n",
      "         1.4299e-02,  2.7194e-01,  1.7423e-02, -2.9366e-01,  1.4695e-01,\n",
      "        -1.7314e-02,  1.4839e-01, -7.2894e-03,  3.6358e-01, -4.0369e-01,\n",
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      "        -1.0264e-03, -4.7367e-03,  1.5992e-01, -2.8036e-11,  2.2051e-01,\n",
      "        -2.3259e-01,  1.6276e-11, -3.0746e-02,  2.4488e-01,  2.6777e-01,\n",
      "        -2.0025e-02, -2.3895e-02, -3.3075e-02,  4.3378e-01, -8.5008e-04,\n",
      "        -2.4703e-01, -3.5501e-03, -2.3883e-02, -1.5255e-20,  3.5518e-01,\n",
      "         3.5520e-02, -9.7230e-04, -4.0413e-01, -1.3666e-01, -3.3039e-02,\n",
      "        -2.7389e-02, -7.6565e-06, -2.1700e-12,  2.8949e-01,  4.1882e-01,\n",
      "         2.0720e-23,  1.4982e-02, -4.6536e-04, -3.8167e-02, -2.8401e-04,\n",
      "        -2.7016e-03,  1.5559e-01, -6.8228e-01,  2.1582e-02,  1.8449e-01,\n",
      "        -4.8037e-03, -3.9501e-02, -2.8029e-02, -4.1864e-01, -5.8161e-05,\n",
      "        -1.7484e-01,  2.2257e-03,  1.5682e-02,  2.6109e-02, -8.0241e-02,\n",
      "         2.7818e-01,  6.6834e-02, -2.1276e-01, -1.3904e-02, -1.8813e-03,\n",
      "        -8.6806e-02, -1.9122e-01, -4.0660e-12, -2.7279e-01, -1.2164e-27,\n",
      "        -1.2514e-04,  3.1601e-03,  3.2278e-01,  9.6054e-24,  1.5771e-01,\n",
      "         1.8375e-01,  1.9308e-01, -1.3668e-01,  5.9765e-03, -4.7703e-05,\n",
      "         9.1392e-02,  1.8908e-01, -4.1147e-02,  3.9777e-01,  3.9360e-02,\n",
      "        -3.4291e-02, -2.5446e-01, -3.0828e-02,  4.2573e-02, -2.3776e-03])\n",
      "predict.weight \t tensor([[ 1.8550e-02, -1.3066e-07,  1.1216e-01,  2.6316e-01,  5.2718e-01,\n",
      "         -3.2484e-05, -3.8343e-01,  7.7871e-01,  2.0611e-02, -5.9354e-05,\n",
      "          4.0727e-01, -1.8753e-08, -1.3084e-06,  5.1548e-01, -5.5751e-01,\n",
      "          1.8255e-01,  3.8709e-01,  1.5603e-01,  8.8924e-01, -6.8795e-02,\n",
      "         -5.6372e-01,  1.2556e-02, -9.9033e-02, -9.4919e-05, -1.8277e-03,\n",
      "          2.8907e-01,  2.1535e-01, -2.1135e-03,  9.4802e-02, -2.6217e-02,\n",
      "          5.6178e-02,  1.3523e-02,  5.3648e-01,  4.2759e-02,  2.0627e-01,\n",
      "          1.0663e-01, -5.6546e-01, -9.9009e-01, -5.5623e-01, -1.3626e-06,\n",
      "          1.4077e-01, -7.3719e-03,  1.7090e-02,  2.2164e-02, -4.3538e-01,\n",
      "         -1.9231e-01,  1.4414e-02, -4.4326e-02, -4.2481e-01,  5.0315e-23,\n",
      "         -3.2761e-02, -1.2577e-05,  8.2803e-01,  8.4868e-01,  2.2031e-02,\n",
      "          1.3743e-01, -9.9821e-03,  6.9396e-01,  5.3119e-01,  8.2872e-01,\n",
      "         -2.6878e-03, -3.5377e-05,  3.0801e-01, -5.3748e-05, -4.9175e-10,\n",
      "          3.4880e-01,  1.3902e-01,  2.9549e-01, -3.8629e-10,  6.2452e-01,\n",
      "         -1.4546e-07, -3.2588e-06, -7.0743e-07,  9.4872e-02, -1.4288e-08,\n",
      "         -1.9376e-05, -2.8225e-02,  1.5445e-02,  1.7403e-02, -1.2154e-04,\n",
      "         -7.7635e-05, -2.8627e-06, -1.5163e-01,  1.9825e-01,  1.3076e-01,\n",
      "          1.2306e-02, -4.6986e-05,  2.2808e-01, -2.1886e-01, -4.2434e-01,\n",
      "         -6.1378e-01,  2.5768e-01,  3.7034e-02,  3.7990e-01,  1.1563e-01,\n",
      "          1.3822e-02, -1.6892e-04,  1.6931e-02,  9.0109e-01,  5.9498e-02,\n",
      "         -2.6102e-01,  2.1058e-02, -3.9179e-01,  8.6391e-02,  5.6256e-01,\n",
      "          1.1022e-01, -7.9026e-06,  1.5869e-01, -5.2004e-01,  7.3897e-02,\n",
      "         -5.8212e-05, -1.1564e-04,  1.1396e-01, -1.4090e-10,  1.1631e-01,\n",
      "         -2.6798e-01,  1.6508e-11, -4.5249e-01,  9.9853e-02,  1.1136e-01,\n",
      "         -3.8603e-01, -5.0457e-01, -6.3605e-01, -3.6519e-01, -4.0500e-05,\n",
      "          7.1734e-01, -7.9440e-05, -4.6528e-01, -7.4302e-20,  2.1286e-01,\n",
      "          5.2888e-03, -3.4519e-05,  8.0583e-01,  2.4803e-01, -3.5771e-01,\n",
      "         -6.0093e-01, -2.9138e-06, -3.8605e-13,  2.9353e-01,  1.2716e-01,\n",
      "          7.3712e-23,  1.3828e-02, -1.1914e-05, -3.7168e-01, -7.6182e-06,\n",
      "         -6.2943e-05,  4.5717e-01, -1.1407e+00,  1.8763e-02, -3.3446e-02,\n",
      "         -1.1217e-04, -9.3374e-01, -4.3368e-01,  4.0049e-01, -1.7277e-06,\n",
      "          3.4963e-01, -2.2572e-03,  1.1756e-02,  2.3419e-02,  2.1070e-01,\n",
      "          1.0750e-01, -5.4117e-02,  7.0226e-01, -4.1682e-01, -4.8988e-05,\n",
      "          2.0110e-01,  4.6684e-01, -1.5423e-10,  5.1831e-01, -2.2872e-26,\n",
      "         -5.6854e-06, -1.8671e-03,  3.2672e-01,  2.5179e-23, -5.9020e-03,\n",
      "         -4.5856e-03,  2.8128e-01,  2.4639e-01,  5.9737e-03, -1.5821e-06,\n",
      "         -1.0437e-02,  1.5680e-02, -4.2528e-01,  2.0691e-01,  1.8812e-02,\n",
      "         -7.0386e-01,  6.8977e-01, -4.9039e-01,  3.7103e-02, -5.3644e-05]])\n",
      "predict.bias \t tensor([0.1207])\n"
     ]
    }
   ],
   "source": [
    "#Getting weights & bias\n",
    "print(\"\\nModel 3 weight and bias before training\\n\")\n",
    "for param in m3.state_dict():\n",
    "    print(param, \"\\t\",m3.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6f1e1f98-dafa-41f3-bcf7-37a837c03996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(x.numpy(),y.numpy(),color=\"black\")\n",
    "plt.plot(x,M3prediction.data.numpy(),color=\"yellow\")\n",
    "plt.title('Model Prediction 3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5162c787-ffeb-4aaf-a4ce-ba322e9a68c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(M1epoch_arr,M1loss_arr, color = \"red\")\n",
    "plt.plot(M2epoch_arr,M2loss_arr, color = \"blue\")\n",
    "plt.plot(M3epoch_arr,M3loss_arr, color = \"green\")\n",
    "plt.title('Model Loss')\n",
    "plt.legend(['First Model', 'Second Model','Thrid Model'])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.savefig('EpochVSLoss.png')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(x,M1prediction.data.numpy(),color=\"red\")\n",
    "plt.plot(x,M2prediction.data.numpy(),color=\"blue\")\n",
    "plt.plot(x,M3prediction.data.numpy(),color=\"green\")\n",
    "plt.plot(x,y.numpy(),color=\"black\")\n",
    "plt.title('Model Prediction')\n",
    "plt.legend(['First Model', 'Second Model','Thrid Model','Orginal Value'])\n",
    "plt.xlabel('Function')\n",
    "plt.ylabel('Target')\n",
    "plt.savefig('Model_Prediction.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "62f5e259-42cc-4ae0-a07e-3a8ca4d2eb26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xshape: torch.Size([300, 1]) \n",
      " Yshape: torch.Size([300, 1])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dinesh\\AppData\\Local\\Temp/ipykernel_5152/3631774031.py:12: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  x2, y2 = torch.tensor(x2, dtype=torch.float32), torch.tensor(y2, dtype=torch.float32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2nd Function\n",
    "#Creating the data\n",
    "\n",
    "torch.manual_seed(1) \n",
    "x2 = torch.unsqueeze(torch.linspace(-1, 1, 300), dim=1)  # x data (tensor), shape=(300, 1)\n",
    "y2 = np.sign(np.sin(5*np.pi*x)) #non-linear y function\n",
    "#y = (np.cos(7 * x * np.pi))\n",
    "\n",
    "print('Xshape:',x.shape,\"\\n Yshape:\",y.shape)\n",
    "\n",
    "#x, y = Variable(x), Variable(y) #converting data into variables as pytorch requirments\n",
    "x2, y2 = torch.tensor(x2, dtype=torch.float32), torch.tensor(y2, dtype=torch.float32)\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(x2, y2, color = \"red\")\n",
    "plt.title('2nd Function Plot')\n",
    "plt.xlabel('Independent varible')\n",
    "plt.ylabel('Dependent varible')\n",
    "plt.savefig('funcplt.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "n_samples, n_features = x2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "fe631883-68d9-43a8-a482-39da78f8f240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convergeance reached for loss:0.0010 at epoch: 8146\n"
     ]
    }
   ],
   "source": [
    "f2m1 = M1()     # re-initialize the network\n",
    "optimizer = torch.optim.Adam(f2m1.parameters(), lr=0.009, weight_decay = 1e-4)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss\n",
    "\n",
    "F2M1epoch_arr,F2M1loss_arr,F2M1prediction = trainFunc(f2m1,x2,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "c180a9f9-1c4a-459b-ba65-d6e2f1163313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(F2M1epoch_arr,F2M1loss_arr,color=\"blue\")\n",
    "plt.title('Function-2 Model 1 Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "9aeb9eb9-2f82-447e-9375-b6cb0eafa83c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model 1 weight and bias after training for F2\n",
      "\n",
      "hidden1.weight \t tensor([[-1.4887e+00],\n",
      "        [-1.0478e+00],\n",
      "        [ 1.3800e+00],\n",
      "        [-1.3573e+00],\n",
      "        [-6.4101e-04]])\n",
      "hidden1.bias \t tensor([-0.5303, -0.8163, -1.0856,  0.8356, -0.0666])\n",
      "hidden2.weight \t tensor([[ 1.4531e+00, -1.9147e+00,  1.4935e+00, -3.1488e-01,  7.9532e-02],\n",
      "        [ 1.6953e-03,  1.3584e-05,  3.4212e-05, -7.1975e-03,  6.2784e-07],\n",
      "        [-1.5636e+00,  1.3983e+00, -1.2528e+00,  2.6446e-01,  3.7572e-02],\n",
      "        [-1.5636e-01, -7.2167e-03, -3.6842e-03, -1.7334e-01,  3.3238e-05],\n",
      "        [-2.4695e+00,  1.0326e+00, -7.1526e-01,  5.9335e-01,  5.8167e-02],\n",
      "        [ 9.5885e-01,  3.8370e+00,  1.7590e-02, -5.0174e-02,  6.4184e-03],\n",
      "        [-9.4028e-02,  1.8867e-02,  1.2745e-02, -1.7435e-01, -6.2591e-05],\n",
      "        [ 1.0133e+00, -1.4235e+00,  1.2869e+00, -2.2326e-02,  1.8310e-02],\n",
      "        [ 1.5520e+00, -1.2697e+00,  1.2017e+00, -1.2861e-01,  6.8428e-03],\n",
      "        [ 2.6662e-01, -1.9802e+00,  1.0289e+00, -7.3296e-01, -7.3295e-02]])\n",
      "hidden2.bias \t tensor([ 0.1993, -0.0038,  0.1552, -0.0170, -0.4862, -0.4737, -0.2345,  0.0025,\n",
      "         0.0099,  0.4272])\n",
      "hidden3.weight \t tensor([[-5.9222e-01, -4.8964e-03,  5.1195e-01,  1.3553e-01, -1.8204e+00,\n",
      "          1.6330e+00, -5.3403e-02, -3.0812e-02, -4.1769e-02, -1.0020e+00],\n",
      "        [ 1.8376e-01, -1.9284e-04, -1.9054e-01, -1.3578e-01,  1.8178e+00,\n",
      "         -1.4773e+00,  1.3490e-01,  2.0001e-01,  2.7949e-01,  4.7920e-01],\n",
      "        [ 2.8020e-03, -9.8044e-08,  4.1342e-03, -1.5001e-06,  7.7329e-05,\n",
      "         -4.7794e-06, -3.5562e-06,  1.2388e-03,  1.1126e-03,  5.9157e-03],\n",
      "        [ 2.5540e-01, -7.1513e-03, -1.2066e-01, -1.6897e-01,  2.2230e+00,\n",
      "         -1.3445e+00,  1.4284e-01,  1.4561e-01,  4.0115e-01,  2.7407e-01],\n",
      "        [-4.5200e-02,  4.5120e-06, -1.1361e-01,  6.0481e-05, -5.9961e-04,\n",
      "          2.7066e-04,  1.0002e-04,  8.4343e-04,  1.7586e-03, -9.5800e-02],\n",
      "        [-2.9290e-01, -2.9178e-03,  1.6481e-01,  2.0250e-01, -2.0610e+00,\n",
      "          5.6621e-02, -2.2173e-01, -3.6504e-01, -4.1390e-01, -3.9376e-01],\n",
      "        [-4.1251e-01,  1.6973e-03,  4.5866e-01,  2.4941e-01, -2.4718e+00,\n",
      "          7.3578e-01, -2.0242e-01, -3.6984e-01, -3.7316e-01, -4.9043e-01],\n",
      "        [-2.5549e-02,  2.6376e-06,  7.5786e-03, -1.2211e-05,  4.4942e-03,\n",
      "         -1.4417e-04, -4.2963e-05, -1.9107e-03, -2.7604e-03, -2.0421e-03],\n",
      "        [-4.3335e-01,  3.1419e-04,  1.8337e-01,  1.8065e-02, -3.1644e+00,\n",
      "         -8.3494e-03, -7.3553e-02, -3.9387e-01, -8.2847e-01, -2.9517e+00],\n",
      "        [-4.9368e-01, -2.2695e-04,  3.8365e-01,  2.9112e-01, -2.4520e+00,\n",
      "          3.9614e-01, -2.0530e-01, -1.3306e-01, -5.3378e-01, -3.6860e-01]])\n",
      "hidden3.bias \t tensor([ 0.1866,  0.0423, -0.0160,  0.0257, -0.1454,  0.3336,  0.3118, -0.2013,\n",
      "         0.0285,  0.3058])\n",
      "hidden4.weight \t tensor([[ 1.4687e+00, -7.9064e-01, -4.7381e-03, -1.1085e+00,  6.0204e-02,\n",
      "          4.4887e-01,  5.8032e-01,  1.6849e-02,  1.1047e+00,  7.3309e-01],\n",
      "        [-1.2103e+00, -3.3154e-01, -5.9292e-03, -7.6511e-01,  1.3414e-01,\n",
      "          4.6196e-01,  7.3469e-01, -7.2915e-02,  2.3926e+00,  8.0571e-01],\n",
      "        [ 2.4838e-01,  3.9164e-01, -8.9551e-04,  3.8398e-01, -1.2952e-01,\n",
      "          1.8421e-01, -2.3842e-01,  4.9433e-02, -2.2252e+00, -3.7350e-01],\n",
      "        [-1.3878e-08,  1.1427e-07, -2.6923e-10,  4.4960e-08, -1.2786e-09,\n",
      "          1.1604e-08, -9.3178e-09,  3.9156e-10,  8.5920e-08, -6.5021e-09],\n",
      "        [ 1.2364e-02, -1.7970e-01, -1.4859e-03, -5.9298e-01,  8.7477e-02,\n",
      "          6.6665e-01,  8.5291e-01, -3.1743e-02,  1.1233e+00,  1.0578e+00],\n",
      "        [-2.6608e-01, -2.3902e-01, -2.5798e-03, -5.8556e-01,  8.2347e-02,\n",
      "          6.5257e-01,  9.0816e-01, -1.7281e-02,  1.9684e+00,  1.0042e+00],\n",
      "        [ 6.3432e-01, -4.4783e-01, -1.0138e-03, -4.0020e-01, -1.2771e-02,\n",
      "          1.2047e-01,  4.2100e-01, -1.5606e-02, -6.0122e-01,  1.8888e-01],\n",
      "        [ 1.4905e-04,  7.0360e-04, -2.2037e-08,  2.7701e-04,  9.4858e-07,\n",
      "          7.5911e-04,  7.1809e-04, -8.5520e-06, -7.0789e-04,  7.7227e-04],\n",
      "        [ 1.5017e+00, -3.6552e-01,  2.1257e-03, -4.9942e-01,  1.6196e-02,\n",
      "          3.5655e-01,  4.3062e-01,  1.7706e-02, -1.0005e+00,  5.2719e-01],\n",
      "        [-1.5844e+00, -1.0537e-01,  1.0044e-03,  7.5670e-02, -3.8277e-02,\n",
      "         -9.7098e-02, -4.3253e-01,  2.9907e-02,  2.0048e-01, -5.4062e-01]])\n",
      "hidden4.bias \t tensor([-2.5871e-01,  2.3269e-02,  2.4089e-01, -1.0618e-05,  2.1548e-01,\n",
      "         2.2276e-01,  1.2265e-01, -2.2517e-02,  5.4828e-02,  4.0303e-01])\n",
      "hidden5.weight \t tensor([[-2.8839e-02, -2.9952e-03,  7.2657e-03,  4.0425e-10, -3.5932e-03,\n",
      "         -5.2183e-03, -2.0140e-03, -4.9541e-07, -2.3397e-02,  1.7186e-03],\n",
      "        [-2.1371e-01, -1.7050e+00,  6.8977e-01, -2.8695e-07, -7.7265e-01,\n",
      "         -6.4242e-01, -4.1409e-01, -4.1629e-03, -4.5614e-01,  2.6815e-01],\n",
      "        [ 4.1753e-01,  1.0393e+00, -1.0965e+00,  1.8081e-06,  4.4478e-01,\n",
      "          8.3627e-01, -2.0285e-01, -1.4131e-02, -4.3038e-01,  3.6059e-01],\n",
      "        [-1.6373e-02, -3.8023e-02,  5.3930e-02, -2.9584e-09, -4.5612e-02,\n",
      "         -5.6730e-02, -1.7016e-02, -4.0508e-06, -2.1419e-03, -8.0972e-03],\n",
      "        [-1.3223e-03, -1.4351e-02,  2.1066e-02, -6.0003e-10, -1.2169e-02,\n",
      "         -2.8858e-02, -8.1725e-03, -2.0877e-06,  2.5784e-03, -6.7939e-03],\n",
      "        [-2.1914e-03,  1.4884e-04,  6.6602e-04,  2.0780e-10, -3.2017e-03,\n",
      "         -3.2445e-03,  6.2354e-04,  3.2297e-07, -3.0958e-03,  1.1567e-04],\n",
      "        [-4.5622e-02, -1.5429e-02, -2.5049e-02,  9.5975e-10, -7.5768e-02,\n",
      "         -7.4758e-02, -2.8264e-02,  1.7707e-06, -6.4982e-02, -6.6521e-02],\n",
      "        [-8.3389e-03, -7.5105e-03,  1.0861e-02,  2.0178e-10, -4.6155e-03,\n",
      "         -6.9517e-03,  7.5112e-04,  6.4828e-07, -4.3295e-03,  2.5294e-03],\n",
      "        [-1.6397e-01, -4.9921e-02,  5.9862e-01, -9.0564e-07, -2.0014e+00,\n",
      "         -1.9195e+00, -4.9207e-01, -8.8719e-03, -8.6811e-01,  5.6257e-01],\n",
      "        [-2.4409e+00,  4.2028e-01,  3.0874e-01,  1.5214e-06, -1.2757e-01,\n",
      "         -6.1455e-02, -1.1150e-01,  6.0385e-03, -7.5496e-01,  1.0402e+00]])\n",
      "hidden5.bias \t tensor([-0.0650,  0.2310, -0.0779, -0.0714, -0.1173, -0.0097, -0.0104, -0.0584,\n",
      "         0.2005,  0.4041])\n",
      "hidden6.weight \t tensor([[ 4.3674e-04,  1.6795e-01, -3.5773e-01,  8.2081e-04, -2.6521e-04,\n",
      "          4.9089e-05, -6.1508e-05, -2.6025e-04, -2.2644e-01, -5.2143e-01],\n",
      "        [-6.0002e-02,  1.2648e+00, -4.5598e-01, -2.7423e-03, -4.9952e-03,\n",
      "          9.2018e-04, -1.2414e-02, -1.6382e-03, -1.6271e+00, -8.7571e-01],\n",
      "        [-7.2637e-04, -2.5149e-01,  1.8590e-01, -2.4548e-03, -1.7679e-03,\n",
      "         -1.9947e-04, -2.9457e-03, -1.6776e-04, -3.0221e-01, -2.4420e-01],\n",
      "        [ 4.6109e-05,  2.1150e-02, -1.2898e-01,  1.3950e-04,  1.1757e-04,\n",
      "          8.9448e-06,  6.8856e-04, -2.1035e-05,  4.1662e-02, -7.2561e-02],\n",
      "        [ 1.9390e-05, -8.0108e-02, -4.5290e-02, -8.7029e-06,  4.4560e-05,\n",
      "          2.1173e-06,  1.5697e-05,  5.9888e-07, -1.4737e-02, -1.0909e-01],\n",
      "        [ 1.5674e-03, -1.5174e-01, -5.1525e-01, -6.0591e-03, -1.1695e-02,\n",
      "         -2.1258e-02, -6.2441e-03, -1.1291e-03, -6.1053e-01, -8.9189e-01],\n",
      "        [ 1.0149e-05, -5.4651e-03, -3.2362e-02, -4.1499e-05, -1.7531e-05,\n",
      "         -4.7289e-06, -5.7169e-05,  1.0234e-05, -1.1163e-02, -1.8704e-03],\n",
      "        [ 1.9275e-02,  9.2026e-01, -1.1255e+00,  6.9031e-03,  9.0571e-03,\n",
      "          8.2677e-05,  9.6490e-03,  1.7572e-02, -2.6314e+00,  2.0855e-01],\n",
      "        [ 6.2879e-03, -5.2967e-01, -1.9401e-01, -1.6679e-02, -2.1621e-02,\n",
      "          1.8507e-03, -4.6462e-03, -1.3590e-03, -2.5873e-01,  1.7952e-01],\n",
      "        [ 1.8719e-02,  1.0828e+00, -5.4613e-01,  1.8447e-01,  6.4432e-02,\n",
      "          2.0095e-03, -6.7858e-02,  4.2593e-02, -1.1116e+00, -1.3524e+00]])\n",
      "hidden6.bias \t tensor([ 0.2398,  0.6202, -0.0380, -0.1923, -0.1611,  0.6550, -0.0350, -0.1509,\n",
      "         0.4954,  0.9447])\n",
      "hidden7.weight \t tensor([[-5.8583e-02, -5.5118e-01,  1.7242e-01, -3.1256e-02,  5.2960e-03,\n",
      "         -6.3365e-01, -7.3693e-03,  4.8180e-02,  5.8737e-01,  5.1248e-01],\n",
      "        [-1.1598e-01, -6.5288e-01,  1.6814e-01, -4.1535e-02,  1.4134e-02,\n",
      "         -5.4383e-01, -1.0212e-02, -1.5339e-02,  6.7104e-01,  5.6036e-01],\n",
      "        [-3.9563e-03, -1.0217e-02, -3.2997e-03,  1.9928e-03,  3.0703e-05,\n",
      "         -8.7863e-03,  2.7986e-05, -1.3715e-02,  2.3858e-02, -4.7609e-02],\n",
      "        [-3.1364e-01, -1.8759e+00, -1.3925e-02,  1.1581e-01,  1.6021e-01,\n",
      "         -7.8721e-01, -3.3729e-02, -2.2719e+00,  2.1505e-01, -3.4122e+00],\n",
      "        [-1.4559e-02, -2.7011e-01,  1.0587e-01, -8.8578e-04,  2.3485e-03,\n",
      "         -1.0902e-01, -1.2881e-03,  4.9575e-02,  7.0424e-02,  5.3822e-02]])\n",
      "hidden7.bias \t tensor([ 0.2720,  0.4368, -0.0677,  0.3796, -0.0005])\n",
      "predict.weight \t tensor([[-0.2629, -0.3795,  0.2719,  3.9894, -0.0507]])\n",
      "predict.bias \t tensor([-0.5557])\n"
     ]
    }
   ],
   "source": [
    "#Getting weights & bias\n",
    "print(\"\\nModel 1 weight and bias after training for F2\\n\")\n",
    "for param in f2m1.state_dict():\n",
    "    print(param, \"\\t\",f2m1.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "898fb85e-6841-41cf-93e1-3bfb19ad54ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(x2.numpy(),y2.numpy(),color=\"black\")\n",
    "plt.plot(x2,F2M1prediction.data.numpy(),color=\"yellow\")\n",
    "plt.title('Function-2 Model Prediction 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "63db7ed3-a939-4da5-a247-dd33aa919808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Epoch Reacheds = 0.0025\n"
     ]
    }
   ],
   "source": [
    "f2m2 = M2()     # re-initialize the network\n",
    "optimizer = torch.optim.Adam(f2m2.parameters(), lr=0.009, weight_decay = 1e-4)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss\n",
    "\n",
    "F2M2epoch_arr,F2M2loss_arr,F2M2prediction = trainFunc(f2m2,x2,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a0825ef7-b194-4255-b7ac-a55385dbbc31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(F2M2epoch_arr,F2M2loss_arr,color=\"blue\")\n",
    "plt.title('Function-2 Model 2 Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5fa82fa5-19d5-4de4-afb2-c0a90fc5cc8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model 2 weight and bias after training for F2\n",
      "\n",
      "hidden1.weight \t tensor([[ 1.2143e+00],\n",
      "        [-2.1335e-07],\n",
      "        [ 1.1174e+00],\n",
      "        [ 3.1706e+00],\n",
      "        [ 1.2930e+00],\n",
      "        [ 2.0173e+00],\n",
      "        [-2.2454e+00],\n",
      "        [ 1.7999e-02],\n",
      "        [-2.6711e-08],\n",
      "        [-2.2191e+00]])\n",
      "hidden1.bias \t tensor([-7.2527e-01, -8.1431e-07,  1.2138e+00,  1.1368e-02,  1.0454e+00,\n",
      "        -1.4261e+00,  4.5853e-01, -2.3134e-02, -6.5442e-08, -1.7380e-02])\n",
      "hidden2.weight \t tensor([[ 1.0326e+00,  7.3763e-08, -4.1561e-01,  4.5225e-01, -2.7838e-01,\n",
      "          2.0893e+00,  3.6887e-01,  1.8844e-04,  1.3771e-09,  3.2478e-01],\n",
      "        [-9.2097e-04, -2.5097e-09, -4.6514e-02, -3.6280e-03, -4.0297e-02,\n",
      "         -4.7022e-04,  3.5780e-03, -3.2587e-06,  3.4272e-09,  5.8768e-03],\n",
      "        [ 2.4664e-04, -1.0047e-09, -4.5757e-03,  3.5531e-03,  5.2341e-04,\n",
      "          7.3881e-04, -8.4726e-03, -1.8639e-06, -2.0253e-10, -4.1205e-04],\n",
      "        [-5.0765e-01,  3.8534e-07,  4.6706e-01, -7.7197e-01,  3.2211e-01,\n",
      "          2.2845e+00, -4.6893e-01,  8.5603e-04,  1.0793e-08, -6.1018e-01],\n",
      "        [-1.7515e-03, -6.4605e-07, -1.9395e-01, -1.2610e-01, -1.8060e-01,\n",
      "          1.1801e-02, -1.6090e-01,  1.6976e-03, -1.3159e-08, -5.7217e-02],\n",
      "        [-1.1137e+00,  6.8685e-06, -5.8787e-01,  1.9796e+00, -8.4715e-01,\n",
      "         -1.1107e+00, -9.2259e-03,  2.2719e-02,  1.0332e-07,  5.9963e-01],\n",
      "        [-8.8667e-01,  1.2194e-06,  1.7215e-01,  3.7804e-01, -6.9333e-02,\n",
      "         -3.9531e-01,  6.7256e-01, -1.0557e-04, -2.2035e-08, -1.3353e+00],\n",
      "        [ 1.0711e-02,  7.2255e-08, -1.0748e+00,  2.3341e-02, -9.1228e-01,\n",
      "          1.3135e-02,  2.6475e-01,  2.4413e-04,  1.1820e-08,  1.6466e-01],\n",
      "        [ 1.4536e-01,  1.4379e-06, -1.3083e-03,  2.9158e-01,  1.2979e-02,\n",
      "         -1.8791e-01, -3.8836e+00,  1.0860e-03,  2.8396e-08,  7.2654e-02],\n",
      "        [ 8.2586e-03,  5.2920e-07, -6.1958e-02, -2.7080e+00,  6.8744e-02,\n",
      "          1.0298e-02,  1.8502e-01,  5.2263e-03, -8.5285e-09, -3.1343e-01],\n",
      "        [-3.3386e-03,  4.0746e-07, -1.3430e+00, -2.1201e-01, -1.6549e+00,\n",
      "         -1.4245e-02,  8.1625e-01, -1.2115e-03,  5.5967e-09,  1.2223e+00],\n",
      "        [ 6.7710e-04, -2.1875e-09, -6.8517e-02, -3.0409e-02, -5.5832e-02,\n",
      "          1.9447e-04,  1.0225e-02, -1.4538e-05,  1.9233e-09,  8.2286e-03],\n",
      "        [ 8.4763e-01,  9.9230e-08, -9.5186e-02,  1.2642e-01, -5.9737e-02,\n",
      "          1.6133e+00,  4.4017e-02,  1.1787e-04, -2.7945e-09,  1.1679e-03],\n",
      "        [ 9.0094e-03,  6.5483e-06, -1.0898e+00, -2.1440e-02, -1.7565e+00,\n",
      "         -9.2288e-04,  2.7820e-01, -2.1085e-02,  1.4003e-08,  6.7119e-01],\n",
      "        [-1.3853e+00, -2.5409e-07,  2.4915e-01, -9.3986e-01,  2.4424e-01,\n",
      "         -9.9304e-03, -4.6929e-02,  5.9110e-04,  3.0613e-09, -3.1033e-01],\n",
      "        [ 2.1512e-02, -2.1939e-08, -1.7376e+00, -5.8857e-03, -2.0938e+00,\n",
      "          2.8781e-02,  3.0426e-01,  1.3755e-03,  9.6895e-09,  4.6223e-01],\n",
      "        [-9.7627e-03,  2.8831e-08, -6.0675e-01,  4.9699e-02, -9.5946e-01,\n",
      "         -4.6642e-03,  8.6460e-01, -8.7462e-04, -5.1669e-09,  1.0474e+00],\n",
      "        [-1.1280e-04,  1.5547e-10, -6.8934e-03,  3.9050e-03, -3.2298e-03,\n",
      "         -7.8370e-05, -1.9334e-03,  1.3260e-06,  2.5160e-11, -1.3547e-03]])\n",
      "hidden2.bias \t tensor([-2.4574e-01, -2.3532e-02, -5.9657e-03,  1.5132e-02, -2.0177e-01,\n",
      "        -2.2233e-01, -1.7772e-01, -5.1990e-01, -1.3606e-01, -1.9480e-02,\n",
      "        -2.5382e-01, -6.3002e-02, -4.7760e-01, -3.7830e-01,  9.0460e-01,\n",
      "        -9.1176e-01,  1.4078e-01,  6.3206e-04])\n",
      "hidden3.weight \t tensor([[-5.8520e-02,  7.3060e-03,  2.8276e-03, -2.3946e-01, -1.5096e-01,\n",
      "          1.4545e+00,  5.0753e-01, -5.1558e-01, -9.1023e-01, -1.1916e-01,\n",
      "         -4.5331e-02, -5.4747e-03, -1.6191e-01, -1.0056e+00, -6.5760e-02,\n",
      "         -1.3327e+00,  1.5324e-01,  1.5917e-03],\n",
      "        [-3.8690e-02, -8.0990e-03, -2.0389e-03, -5.1082e-01,  1.4943e-01,\n",
      "          2.0251e+00,  1.4504e+00,  1.2620e-01,  3.8431e-01, -6.0598e-02,\n",
      "          3.2706e-01,  4.0422e-03, -4.1183e-02,  6.3773e-01, -1.7199e+00,\n",
      "          4.9743e-01,  1.0052e-01, -1.1407e-03],\n",
      "        [ 2.2721e-01,  1.5748e-03,  1.1856e-04, -2.8575e-01, -2.5823e-02,\n",
      "          7.5097e-01, -3.1352e-02,  8.7461e-01,  2.2909e-01, -1.4998e-02,\n",
      "          7.3964e-02,  2.8129e-03, -6.3932e-02,  6.2481e-01, -9.1164e-01,\n",
      "          1.6324e+00, -1.8699e-01,  3.8619e-04],\n",
      "        [-6.8934e-02, -2.2348e-03,  8.5500e-04, -3.1011e-01, -2.3312e-01,\n",
      "          1.4856e+00,  6.8421e-01, -5.9303e-01,  1.8864e-01, -1.3274e-01,\n",
      "         -9.0799e-02,  1.7716e-02, -1.8386e-01, -8.5089e-01, -1.3626e-01,\n",
      "         -1.3199e+00,  1.3476e-01,  3.3285e-03],\n",
      "        [-1.7707e-02,  1.1817e-02, -9.1121e-03, -8.0809e-02, -2.5353e-02,\n",
      "          6.7372e-01, -2.5936e-01, -3.7120e-01, -1.2891e-01,  5.7572e-02,\n",
      "          1.0102e+00,  7.5778e-03, -1.9628e-01, -7.1752e-01, -1.1595e+00,\n",
      "         -1.1055e+00, -3.3414e-02,  9.7350e-04],\n",
      "        [-1.0595e+00,  5.0533e-02,  2.6685e-02,  1.2084e-02,  2.1746e-01,\n",
      "         -4.4341e-01,  1.1755e-01, -3.4004e-02,  7.9341e-01,  1.1709e+00,\n",
      "          1.6568e-02, -8.1213e-02, -6.6642e-01, -8.6763e-03, -2.1174e-02,\n",
      "         -7.6526e-02, -2.7567e+00,  6.0167e-03],\n",
      "        [ 7.5194e-02, -3.0717e-03, -1.6114e-03, -6.4160e-01,  2.1068e-01,\n",
      "          7.1636e-01,  1.8559e-01,  2.1836e-02, -2.1734e-01, -5.7773e-03,\n",
      "         -1.5792e-02,  6.6437e-03,  1.0555e-01,  1.9182e-02,  1.6894e-01,\n",
      "          4.2811e-02, -1.9636e-01, -1.0227e-03],\n",
      "        [ 1.8675e-02,  6.9893e-03,  2.4190e-03, -3.9254e-01, -1.7939e-01,\n",
      "          1.5457e+00,  4.9554e-01, -4.6279e-01, -7.8028e-01,  7.5957e-02,\n",
      "         -1.1017e-01, -8.1948e-03, -1.7876e-01, -1.0231e+00, -1.1185e-02,\n",
      "         -1.2095e+00,  1.4876e-01,  1.7869e-03],\n",
      "        [-3.8232e-03, -1.8732e-04,  1.6778e-04,  2.2515e-01, -1.5059e-01,\n",
      "         -4.1102e-01,  1.6010e-01, -6.8532e-03,  2.9203e-01, -6.5212e-03,\n",
      "         -1.2948e-02, -2.3356e-03, -4.4576e-03, -1.3139e-02, -1.9030e-01,\n",
      "         -1.3445e-02, -1.7772e-02,  9.3716e-05],\n",
      "        [ 2.8178e-03, -1.3053e-04, -1.2482e-05, -1.9325e-04, -2.9231e-04,\n",
      "          2.4700e-03,  2.5325e-03, -1.9209e-03, -4.3260e-04, -3.8279e-04,\n",
      "         -2.3219e-03, -1.6461e-05,  1.7094e-04,  6.7358e-04, -1.7683e-02,\n",
      "          1.2415e-03, -5.4363e-03,  2.1706e-05],\n",
      "        [-2.2797e+00,  1.5704e-03, -1.2615e-04, -2.1795e+00, -2.4560e-03,\n",
      "          7.7106e-01, -2.7426e-01,  4.8841e-02,  1.4204e-01,  4.6562e-02,\n",
      "          1.0920e-01,  4.9291e-03, -1.6195e+00,  9.0868e-02, -2.0847e+00,\n",
      "          1.0423e-01,  1.7613e-01,  7.5864e-05],\n",
      "        [ 9.4925e-03, -6.3086e-03, -3.2288e-03,  2.9334e-01,  2.1709e-01,\n",
      "         -8.6400e-01, -1.6389e-02, -3.5734e-02,  6.7832e-01,  2.6122e-01,\n",
      "          2.0261e-01, -3.7647e-03,  3.2942e-03,  1.9588e-01,  3.9677e-01,\n",
      "          1.2925e-01, -2.9686e-01, -3.1183e-03],\n",
      "        [-7.1309e-05, -6.0035e-07, -2.7901e-08, -1.3171e-05, -1.2185e-05,\n",
      "         -1.0497e-04, -3.2926e-05,  1.0838e-05, -4.6399e-05,  3.0073e-05,\n",
      "         -1.0041e-04, -1.1631e-06, -1.1868e-04,  7.2830e-05,  3.0340e-06,\n",
      "          1.8780e-05, -1.2263e-04, -3.8242e-08],\n",
      "        [-1.7551e-02,  6.7519e-03,  2.3748e-03, -7.4844e-01, -1.7658e-01,\n",
      "          1.1750e+00,  2.5833e-01, -3.4057e-01, -2.5254e-01,  1.2441e-01,\n",
      "         -1.8101e-01, -6.7619e-03, -1.1883e-01, -8.3204e-01, -1.3357e-01,\n",
      "         -8.6262e-01,  1.9693e-01,  1.5655e-03],\n",
      "        [-4.7027e-01, -6.1480e-02,  1.1011e-02, -1.0397e-02, -2.1358e-01,\n",
      "         -3.8258e-01,  5.3401e-01,  1.4850e-01, -8.1474e-01, -8.8075e-01,\n",
      "         -3.9994e+00,  9.1713e-02, -1.0927e-02,  2.4863e-01,  1.0585e-01,\n",
      "          4.5651e-01, -1.4637e-01, -8.9420e-03]])\n",
      "hidden3.bias \t tensor([-1.7648e-01,  2.9168e-01, -5.3599e-01, -8.2062e-03, -7.8444e-01,\n",
      "         1.2556e-01,  4.3653e-03, -7.2038e-02,  6.4409e-02, -1.3634e-02,\n",
      "        -4.5591e-01,  2.3902e-01, -1.0849e-04,  1.1402e-01,  2.9781e-01])\n",
      "hidden4.weight \t tensor([[ 2.5343e-01, -2.7569e+00,  1.5887e-01, -1.1094e+00, -8.5986e-02,\n",
      "          2.2989e+00, -5.8100e-01, -1.0106e+00,  4.5469e-01,  7.6485e-03,\n",
      "          2.1989e-01,  9.4657e-01, -5.9655e-05, -1.2837e+00, -1.8692e+00],\n",
      "        [ 4.1299e-01, -3.9751e-01,  3.2732e-01,  3.7721e-01,  8.7077e-01,\n",
      "         -1.2114e+00, -4.6026e-01,  3.4976e-01,  2.6417e-01, -9.8647e-04,\n",
      "         -3.7386e+00, -2.0257e-01,  1.4106e-05,  2.0342e-01,  1.3821e+00],\n",
      "        [ 6.6193e-04,  2.5597e-03, -1.2034e-03,  6.1639e-04, -3.1697e-04,\n",
      "          1.4601e-02,  9.8376e-04,  8.7305e-04, -1.4326e-03, -1.6905e-04,\n",
      "         -6.6653e-03, -8.5517e-02, -3.0188e-06,  5.8533e-04, -5.2270e-02],\n",
      "        [ 5.4648e-01, -8.0388e-01, -2.4223e+00,  5.2797e-01,  1.9559e+00,\n",
      "         -8.2812e-01, -4.1536e-01,  5.5993e-01,  2.4737e-01,  2.3400e-03,\n",
      "         -1.8662e+00,  1.2590e-01, -4.5396e-05,  5.1458e-01,  1.6069e+00]])\n",
      "hidden4.bias \t tensor([ 0.3648, -0.1343, -0.0938, -0.3041])\n",
      "predict.weight \t tensor([[-3.9063,  2.6651,  0.0056,  2.8478]])\n",
      "predict.bias \t tensor([-1.6193])\n"
     ]
    }
   ],
   "source": [
    "#Getting weights & bias\n",
    "print(\"\\nModel 2 weight and bias after training for F2\\n\")\n",
    "for param in f2m2.state_dict():\n",
    "    print(param, \"\\t\",f2m2.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dcb97843-ccd3-4e04-b550-7da4e5242f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(x2.numpy(),y2.numpy(),color=\"black\")\n",
    "plt.plot(x2,F2M2prediction.data.numpy(),color=\"yellow\")\n",
    "plt.title('Function-2 Model Prediction 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "2e33ea39-655f-4069-bc9e-9fc31d107094",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Epoch Reacheds = 0.0893\n"
     ]
    }
   ],
   "source": [
    "f2m3 = M3()     # re-initialize the network\n",
    "optimizer = torch.optim.Adam(f2m3.parameters(), lr=0.009, weight_decay = 1e-4)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss\n",
    "F2M3epoch_arr,F2M3loss_arr,F2M3prediction = trainFunc(f2m3,x2,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b777169e-ab14-4003-95d5-8f8a2fd4ec14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(F2M3epoch_arr,F2M3loss_arr,color=\"blue\")\n",
    "plt.title('Function-2 Model 3 Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8520e393-91b2-4673-92e8-6864002a0278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model 3 weight and bias after training for F2\n",
      "\n",
      "hidden1.weight \t tensor([[-1.6798e+00],\n",
      "        [-3.1055e+00],\n",
      "        [ 2.4000e+00],\n",
      "        [-3.1324e-06],\n",
      "        [-4.2720e+00],\n",
      "        [-2.6525e-08],\n",
      "        [-2.3412e-10],\n",
      "        [-1.2295e-05],\n",
      "        [-8.1793e-06],\n",
      "        [-2.4514e+00],\n",
      "        [ 7.4385e-01],\n",
      "        [ 9.3226e-01],\n",
      "        [ 1.9455e-10],\n",
      "        [-4.2460e-11],\n",
      "        [-2.6318e+00],\n",
      "        [ 2.2987e+00],\n",
      "        [-2.0807e+00],\n",
      "        [-4.5834e+00],\n",
      "        [ 1.8851e+00],\n",
      "        [-1.4442e-40],\n",
      "        [ 2.7867e+00],\n",
      "        [-1.1045e-40],\n",
      "        [-3.8622e-35],\n",
      "        [-7.3433e-01],\n",
      "        [-2.2352e+00],\n",
      "        [ 1.9334e+00],\n",
      "        [-1.8715e+00],\n",
      "        [-5.1289e-28],\n",
      "        [ 8.7471e-01],\n",
      "        [ 1.7516e-40],\n",
      "        [ 5.5267e-08],\n",
      "        [-7.1312e-01],\n",
      "        [-9.4183e-01],\n",
      "        [ 3.8930e+00],\n",
      "        [-1.0548e-32],\n",
      "        [ 2.1013e+00],\n",
      "        [-2.3801e+00],\n",
      "        [ 9.6352e-01],\n",
      "        [ 1.0978e-11],\n",
      "        [ 5.9058e-01],\n",
      "        [ 9.0225e-01],\n",
      "        [ 5.7848e-23],\n",
      "        [ 9.0314e-01],\n",
      "        [ 3.4298e+00],\n",
      "        [-2.3578e+00],\n",
      "        [-2.2463e+00],\n",
      "        [ 1.9418e+00],\n",
      "        [ 9.5840e-01],\n",
      "        [-1.5489e-22],\n",
      "        [ 7.3438e-41],\n",
      "        [ 2.2416e+00],\n",
      "        [-2.3456e+00],\n",
      "        [-2.4644e+00],\n",
      "        [ 1.9190e+00],\n",
      "        [ 1.5434e+00],\n",
      "        [ 1.7453e+00],\n",
      "        [ 7.0213e-01],\n",
      "        [-2.2211e+00],\n",
      "        [ 3.0562e+00],\n",
      "        [-1.7618e+00],\n",
      "        [-8.6629e-01],\n",
      "        [-1.3721e-28],\n",
      "        [-4.0679e-06],\n",
      "        [ 1.4606e+00],\n",
      "        [-2.3488e+00],\n",
      "        [ 2.4100e+00],\n",
      "        [-5.7045e-07],\n",
      "        [-3.5887e-13],\n",
      "        [ 1.7957e+00],\n",
      "        [ 2.2067e+00],\n",
      "        [ 4.0314e-02],\n",
      "        [-2.8224e-13],\n",
      "        [ 2.4985e-10],\n",
      "        [ 2.2537e-13],\n",
      "        [ 2.2945e+00],\n",
      "        [-2.4042e+00],\n",
      "        [ 1.4718e+00],\n",
      "        [ 9.9562e-11],\n",
      "        [-2.3031e+00],\n",
      "        [-8.5391e-01],\n",
      "        [-4.7552e-29],\n",
      "        [ 5.3185e-03],\n",
      "        [ 2.2350e+00],\n",
      "        [-5.1078e-23],\n",
      "        [-2.5862e+00],\n",
      "        [-2.8264e+00],\n",
      "        [ 1.9860e+00],\n",
      "        [-1.8554e+00],\n",
      "        [ 2.5931e+00],\n",
      "        [ 2.1507e+00],\n",
      "        [ 1.4320e-16],\n",
      "        [ 2.6327e+00],\n",
      "        [-2.1558e+00],\n",
      "        [ 2.5236e-21],\n",
      "        [-1.3640e-07],\n",
      "        [ 1.3512e-09],\n",
      "        [ 2.5054e+00],\n",
      "        [-2.2833e+00],\n",
      "        [-7.6403e-41],\n",
      "        [ 2.3832e+00],\n",
      "        [ 1.8785e+00],\n",
      "        [-3.9065e-06],\n",
      "        [-3.3223e+00],\n",
      "        [ 4.8689e-06],\n",
      "        [-2.4236e+00],\n",
      "        [ 2.2903e+00],\n",
      "        [ 6.7014e-04],\n",
      "        [-1.2741e-12],\n",
      "        [ 4.5176e-10],\n",
      "        [-1.9496e+00],\n",
      "        [ 2.0548e+00],\n",
      "        [-2.3603e+00],\n",
      "        [-4.0458e+00],\n",
      "        [-1.8999e+00],\n",
      "        [-6.3269e-08],\n",
      "        [ 2.2654e-29],\n",
      "        [ 2.1821e+00],\n",
      "        [ 2.3415e+00],\n",
      "        [ 2.0147e-03],\n",
      "        [ 3.2672e+00],\n",
      "        [ 9.4074e-17],\n",
      "        [-1.1997e-33],\n",
      "        [ 1.9121e+00],\n",
      "        [-2.0788e+00],\n",
      "        [-2.1372e+00],\n",
      "        [ 8.5704e-05],\n",
      "        [-2.3342e+00],\n",
      "        [ 1.5256e+00],\n",
      "        [ 9.0583e-01],\n",
      "        [ 2.1297e+00],\n",
      "        [ 1.5177e+00],\n",
      "        [ 3.1975e-09],\n",
      "        [ 2.3911e+00],\n",
      "        [ 1.4691e+00],\n",
      "        [-8.9873e-11],\n",
      "        [ 2.1782e+00],\n",
      "        [-2.2911e-41],\n",
      "        [ 1.5523e-40],\n",
      "        [ 2.5818e+00],\n",
      "        [ 1.8815e-03],\n",
      "        [-3.3069e+00],\n",
      "        [-2.1701e-08],\n",
      "        [-2.5095e+00],\n",
      "        [ 4.9659e-03],\n",
      "        [ 1.5918e+00],\n",
      "        [ 7.0270e-19],\n",
      "        [-4.9205e-32],\n",
      "        [-1.2094e+00],\n",
      "        [-1.2920e+00],\n",
      "        [-1.3756e-23],\n",
      "        [ 8.2364e-01],\n",
      "        [-7.6930e-19],\n",
      "        [ 2.2607e+00],\n",
      "        [ 2.0155e-33],\n",
      "        [ 1.7932e+00],\n",
      "        [-2.3448e+00],\n",
      "        [ 2.4435e+00],\n",
      "        [ 1.3314e-19],\n",
      "        [-5.9837e-21],\n",
      "        [-8.7542e-01],\n",
      "        [-6.6205e-01],\n",
      "        [ 7.3285e-27],\n",
      "        [-2.2201e+00],\n",
      "        [-1.4248e-40],\n",
      "        [-4.2338e-07],\n",
      "        [ 2.3056e+00],\n",
      "        [ 2.1811e+00],\n",
      "        [-3.2283e+00],\n",
      "        [ 2.4385e-11],\n",
      "        [ 7.5833e-15],\n",
      "        [ 2.0570e-12],\n",
      "        [-3.6084e-16],\n",
      "        [-2.2762e+00],\n",
      "        [ 2.8463e+00],\n",
      "        [-9.9831e-29],\n",
      "        [ 2.5071e+00],\n",
      "        [-1.9171e+00],\n",
      "        [ 1.5984e+00],\n",
      "        [-1.7792e+00],\n",
      "        [ 6.5904e-03],\n",
      "        [ 2.2957e+00],\n",
      "        [-8.3095e-09],\n",
      "        [ 7.3535e-04],\n",
      "        [-3.2700e+00],\n",
      "        [-1.7080e+00],\n",
      "        [ 8.0626e-14],\n",
      "        [-1.1331e-11],\n",
      "        [-3.3134e-37],\n",
      "        [ 9.8742e-41],\n",
      "        [ 2.1072e+00]])\n",
      "hidden1.bias \t tensor([-1.2969e+00, -5.4220e-01, -1.8463e+00, -1.4583e-03,  1.8434e+00,\n",
      "        -4.6739e-07, -4.9271e-10, -7.1305e-04, -2.1691e-04, -9.0713e-01,\n",
      "         4.6577e-01,  5.8571e-01, -4.9980e-08, -1.0309e-11, -6.0947e-01,\n",
      "         5.4114e-02, -1.3159e+00,  1.9982e+00, -1.1927e+00, -1.7531e-40,\n",
      "        -6.4544e-01, -1.2118e-40,  1.1204e-35,  5.6168e-01, -5.1772e-01,\n",
      "        -1.2230e+00, -1.4436e+00, -7.7528e-28,  5.4766e-01, -2.8767e-40,\n",
      "        -1.4507e-07,  5.4524e-01, -4.0838e-01, -3.2454e+00, -1.6671e-32,\n",
      "        -7.7260e-01,  1.0271e+00,  6.0488e-01, -8.8043e-11,  3.7015e-01,\n",
      "         5.6743e-01, -2.8522e-22,  5.6636e-01, -1.9550e+00, -1.4911e+00,\n",
      "         5.1081e-01,  3.3019e-01,  6.0382e-01, -3.3176e-22, -1.1018e-40,\n",
      "         5.0831e-01, -1.4834e+00,  5.5986e-01,  3.2632e-01,  3.4632e-01,\n",
      "        -1.1043e+00,  4.3989e-01, -3.8752e-01, -9.2588e-02, -3.1165e-01,\n",
      "         5.4595e-01, -2.4693e-28, -5.4655e-06, -9.2430e-01, -4.0842e-01,\n",
      "         1.0399e+00, -1.3776e-07, -2.0738e-12,  3.0524e-01,  4.9838e-01,\n",
      "        -1.5427e-01, -4.3885e-13, -3.1538e-09, -8.7228e-13, -8.4304e-01,\n",
      "         6.5222e-02,  3.3026e-01, -2.2258e-10,  6.1851e-02,  5.3780e-01,\n",
      "        -1.8500e-28, -7.8495e-02, -1.7211e+00, -5.1600e-22,  5.9136e-01,\n",
      "         6.4190e-01, -3.4429e-01, -3.2815e-01,  1.1281e+00,  4.8642e-01,\n",
      "        -6.1226e-15,  1.1487e+00, -1.3634e+00, -6.2175e-20, -2.0431e-07,\n",
      "         4.3566e-12, -4.2552e-01, -1.3078e+00, -1.1234e-40,  1.0283e+00,\n",
      "         3.1936e-01, -3.5715e-05, -1.8954e+00, -3.3151e-05, -8.9702e-01,\n",
      "        -1.3098e+00, -8.0342e-04, -4.3028e-12, -1.4307e-09, -1.5026e+00,\n",
      "        -1.5845e+00, -8.6891e-01, -3.3698e+00, -1.4650e+00, -9.3695e-08,\n",
      "        -5.0410e-29, -1.3803e+00, -8.6320e-01, -9.3697e-02, -9.1385e-02,\n",
      "        -8.3437e-18, -6.2889e-33, -3.3173e-01,  5.4708e-02,  4.8034e-01,\n",
      "         9.4828e-04, -8.5971e-01, -2.6859e-01,  5.6705e-01, -1.6411e+00,\n",
      "        -8.6798e-01, -2.6406e-08,  1.0317e+00, -5.4509e-01,  1.2323e-11,\n",
      "         4.8858e-01, -3.0693e-41, -2.1326e-40, -4.4674e-01, -5.7741e-03,\n",
      "        -9.9850e-02, -3.0127e-08,  5.7187e-01, -2.0609e-02, -2.7957e-01,\n",
      "        -6.8652e-18, -2.3200e-31,  2.9067e-02, -4.7911e-01, -1.9761e-23,\n",
      "         5.1557e-01, -8.2693e-19,  5.1400e-01, -4.2240e-32, -3.1264e-01,\n",
      "        -1.8024e+00,  1.0542e+00, -3.9742e-16, -8.3469e-20,  5.5176e-01,\n",
      "         4.1684e-01, -4.3295e-25,  5.9062e-02, -1.8501e-40, -5.3013e-07,\n",
      "        -8.4878e-01,  4.9417e-01, -9.7483e-02, -8.3627e-10, -1.0833e-14,\n",
      "        -9.6166e-08, -7.5544e-16,  6.0796e-02, -4.9227e-01, -1.4834e-27,\n",
      "         1.0839e+00, -1.0984e+00, -5.9307e-01, -1.3738e+00, -1.9056e-02,\n",
      "         5.3939e-02, -8.8690e-09, -1.8834e-03, -8.5286e-02, -9.7947e-01,\n",
      "        -5.1331e-13, -6.4280e-12, -3.0683e-35, -1.9298e-40, -1.3331e+00])\n",
      "predict.weight \t tensor([[-2.3661e+00,  2.7721e+00,  3.7050e+00,  2.9656e-03, -3.0134e+00,\n",
      "         -2.3574e-08,  4.9441e-10, -2.5341e-03,  2.8018e-03, -2.8996e+00,\n",
      "         -6.6021e-01, -7.6854e-01,  3.4273e-09, -8.6657e-12, -2.8753e+00,\n",
      "          1.6584e+00, -2.8324e+00, -2.7908e+00,  2.6717e+00,  1.2627e-39,\n",
      "          3.0623e+00,  2.1126e-41, -5.8706e-36,  6.5874e-01, -2.6378e+00,\n",
      "          2.7616e+00, -2.6497e+00, -7.0615e-28, -7.7340e-01, -1.9421e-41,\n",
      "         -1.8220e-07,  6.4423e-01,  1.3365e+00, -7.8100e+00,  6.1534e-33,\n",
      "          2.6975e+00, -1.9552e+00, -8.0497e-01,  8.3461e-11, -5.7798e-01,\n",
      "         -7.3156e-01, -6.2751e-23, -7.5618e-01, -6.3941e+00, -3.2264e+00,\n",
      "          1.6604e+00,  1.4655e+00, -7.6330e-01,  7.9028e-23,  1.2565e-40,\n",
      "         -1.5238e+00, -3.2241e+00,  1.9053e+00,  1.3974e+00, -1.3328e+00,\n",
      "          2.4570e+00, -6.6991e-01,  2.3760e+00, -2.2616e+00,  1.8767e+00,\n",
      "          7.5639e-01, -7.9180e-29,  8.7027e-07,  2.0126e+00,  2.5338e+00,\n",
      "          1.7308e+00, -9.9526e-08, -3.3009e-13,  1.4465e+00, -1.5464e+00,\n",
      "          1.3282e-01,  5.2396e-13, -1.8270e-10, -2.2443e-13,  2.8870e+00,\n",
      "         -1.7451e+00, -1.2700e+00,  2.0599e-09, -1.7134e+00,  7.0697e-01,\n",
      "         -1.0993e-28, -9.2449e-02,  3.4361e+00,  2.1014e-22,  1.8581e+00,\n",
      "          2.3192e+00, -2.1107e+00,  1.9556e+00,  1.6877e+00, -1.4759e+00,\n",
      "          1.2428e-17,  1.6717e+00, -2.9550e+00, -4.6482e-20, -1.9909e-07,\n",
      "         -3.3620e-11, -2.7539e+00,  3.4994e+00,  3.1241e-41,  1.7546e+00,\n",
      "          1.3645e+00, -1.2451e-05,  5.1685e+00, -1.2676e-03, -2.8705e+00,\n",
      "         -3.6086e+00, -9.2651e-05, -1.3484e-11,  2.3710e-10, -2.7651e+00,\n",
      "          3.1441e+00, -3.2352e+00,  7.7226e+00, -2.6915e+00,  6.7669e-08,\n",
      "         -1.3476e-29,  3.1487e+00,  2.6721e+00, -1.4583e-02, -2.6835e+00,\n",
      "         -5.4708e-17, -4.7963e-33, -2.0573e+00, -1.7421e+00,  1.7874e+00,\n",
      "         -6.7022e-04, -3.1641e+00, -1.5984e+00, -7.6874e-01,  3.2653e+00,\n",
      "         -2.2136e+00,  1.1808e-08,  1.7235e+00,  1.7664e+00, -1.6531e-10,\n",
      "         -1.6280e+00,  4.8137e-41,  1.5044e-41, -2.3978e+00,  3.3674e-03,\n",
      "          2.8727e+00,  1.9120e-08,  1.8723e+00,  7.2932e-05, -1.6585e+00,\n",
      "         -2.0078e-18,  5.2332e-32, -1.1201e+00, -1.5652e+00,  1.0275e-23,\n",
      "         -7.1335e-01,  4.8471e-19, -1.5033e+00,  7.7413e-35, -1.8792e+00,\n",
      "         -3.3529e+00,  1.6936e+00, -1.5666e-18, -4.7276e-20,  7.4332e-01,\n",
      "          6.7738e-01,  2.2764e-25, -1.6873e+00, -1.2617e-40,  6.2041e-09,\n",
      "          2.7352e+00, -1.4820e+00,  2.8868e+00,  1.1333e-09, -2.9283e-15,\n",
      "          4.6876e-09,  7.9539e-16, -1.6775e+00, -2.5203e+00,  8.7249e-28,\n",
      "          1.7013e+00,  2.7786e+00,  1.9839e+00, -2.5137e+00,  2.6612e-02,\n",
      "          1.6554e+00,  2.9861e-09, -1.7254e-03,  3.0453e+00,  2.4031e+00,\n",
      "         -1.1522e-12, -9.1152e-13, -2.9792e-35, -8.8198e-42,  3.0245e+00]])\n",
      "predict.bias \t tensor([-0.0562])\n"
     ]
    }
   ],
   "source": [
    "#Getting weights & bias\n",
    "print(\"\\nModel 3 weight and bias after training for F2\\n\")\n",
    "for param in f2m3.state_dict():\n",
    "    print(param, \"\\t\",f2m3.state_dict()[param])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "089408b3-6d9f-41be-b87d-fa0dae1805ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V82f4XIH35lFWyKSKIOsC/iF5PiLnkpZRPQbe0NDgTQVN7vb+HvOHfV7OJa0EypjpfD1eu/qT23kPJtnn3WFOLaK9wKrA2hlMwj+OfHtyXRiFr4AE1s4AVwLPBF7lo0oLJl5aIUcmK4K0a9qdc0mTLR3ff4os5geS57U5l5Tek3n3eHm+k1+tws/bzoEs4e8ZLy+cdlYy6y5PujFnoY6OlxdOOw9j5pu8gcV6bkRM+DkyWQW5SfhhDWylVtNBOZdUm/DDitlWwk+3uXe8vHBifhzT1nlfBqEree4bLy+cmPdhLMW8e+zuiAk/RyaroD6glfxPlw550DY9yC3LuaSDk2f/Cn9yzHvIf4YO1FP44cScjlscXHf9+ZOKo/SktpBiTv9zee/b7ogJP0fqWzp5q3swO9woZjAvtIEtW72ayZZOODG7UfhhxWwr+dUq/LBitrVvuyMm/Bypb+nYSviQ2idhdXvTRJB33CuS5xAtHTcKP6yYbSW/WoUfVsy29m13xISfI/XtDRs7xwHJc+94eeF0e/swF4OawwVjZkUr5kAXmqVTJru0bd50YOzATOGHETO4VPjhxGzLrnRHTPg54tbSgeqE71sF2Y8Zqk9ECifm/ZgZKzYSvmBi9j9Lx529kW4vWjo2iAk/RybP2+3FRcIPa362rZgBOjEnIvnt6te2s63LKqQsp9rSCaudIX+1G46lU///DFHhR4BiKvz63V5bf4gs4YczPzu9rnve01BTlhGC2p3a0sl7/w7H0okKPzItvhJ+WANbNi2dMBR+bczpZQCKFDPYG6sJR+HX79U0YMZWFicx4edIpVKZcN0Ndwo/rIEt+8kvnJjThN9pqbQuwosZ7Fl3rZi7uflX+FPv24vzLFuICT9XVHXCqef9uJiWGdbAlhsPP5yYB5J3u6Zcf2F0Un0SUhgxgz3rTqi+gFp4MS9eOwdiws8VVa3q/g1gkr6NHSTtSoc4D9+Nhx9OzLYVfogxg90De3oBtRBjXrwDthATfq7UWjo2B3gmq6Dwur02CNHSSRV+0Swdmwf2Iu7bbogJP0dqFb7tEf0uQhrYyk5CGqAolo5pa9sJP41ZA2pnsKt2M4UfTjtDVPiRGtwpfKhO+CFMXau1N2xaOubSuSHMz84GbZvJ/wJ5KZ0Ya3AooHYGu2o3u+tVOO0MUeFHaqgdtLV9GnZtwg/jT+HiIAfQH0TyyywdWwO2VG17IKB2BrtqNwxLx22vxg0x4eeIW0snDBVUq/xsx5zaJgMBxdyPPTuHqm33BxQz2FW7oQ7aRoUfqaKIlk6t8rN9NcEs4YcT8wBuEn5IMZeAIZa6wq+NWYkJP1JDfUvHTcL3rYJqLR2bHj74tjfcWjohxuxi3w5n0NbEPISZlBAtnUhCkWfpREvHFpmHH07Mti8i1k2aYMOJefFfRwdiws+VWksn/VPYSgaZhx9Ot7eolo4Lhe93oLqIg/P19+2o8CMJkxV+F/Z+4i5CUkFFtHTcDtqGofDdJL/sAmrhtHNU+JEJTB60tblz1E7XC2PqWjEsHbeDtmG1c62lY1vh9wXUzjHhRyYwedDWRcL3q4ImH+SaMFc8tEEYlo6fQVv/7QyuenLZXa/Caedo6UQmMNnSsZnwsz+F767+5BNTbF0+Nlo6vmN2M1ZTK2aqy3dJtHQi0+LH0vE/sOXuIJfeeKLf6+3+MrVrLvNgV+G3YHpNYQxU1yY/+wo/3bd8JPw4aBuZliJaOm5jTu82FIbCFxlM3rGp8CG9YmYYMbtQ+LWDttXlu8Tt+JQbYsLPEbeWTpbwfXf13VwjPcVcPdJ3zACNjcNVdbKJuQlKCDFnCr8d0/OwQe2gbXX5Lpk8UC3Yb2u7xISfI24tnTAGtibHbLvLaxK+75ihWuHbtHQgrJhThW+znScrfB9xTx6oXty3N4SY8HNlssK3+acIR+G7vXxslvz8q123lo7vdoaJyc8W6e/ZF4Slk/VqFredAzHh50qmdkvAMEUYtC2ipeNH4ftvZ6i2N2yKmQaqbazq8l3itlfjhpjwcyRTuy4GeMKZn100S8e9wvffq6lvb9jEXEDNp6XjtlfjhpjwcyRTuy4SfkvyCGUevmLmpBfH0nE9S8d3O4NLtWuuFRWOpRMVfmQCmdp1NYXLXDfc92Ce+UMMAhVcWjr+BzCLY+lMnofvQuFnlk44g7aLm5jwcyRTu7bnKaeYP4Vvtes25iz5+Ve7tm9gnhJSr6Z4Cj9L+FHhR6pwa+lAtQryPw/f9tmXKf4HbSdbOm6mZYYQszuFb3qvYc3Djwo/UoV7Sycb2PI/D99lzKHMSR/AzMtut1xiFzBMQ4MGEHM6A832gd3/oG0WM0RLpwoROV1EtorINhG5uM7np4jIfhH5TfL4eB7lhoZ7hZ+poGJZOiWamioBxDyAudSD7ZNxjGXU1lYOQOGnNpa78anq8l2SnVE9hplqvfgtnQWfGy0ijcBlwKnATmCjiGxQ1fsmrPpzVX3dQssLGT9q9wnvUxTdWzp+k1/WzrYvjZxiYm5pGfOe8Bsb04TvRuGHMA+/sTG17qLCB3gxsE1VH1LVUeAq4OwctrvocDsPH0IYtHV/kMsSvs+DnInZ9qWRU8xBpbW1NF6+azJ7w/UMtNryXZL1avqTdxa/ws8j4R8K7Kha3pm8N5GXisgWEblRRJ471cZE5EIR2SQim3bv3p1D9dxRq3Zt3ggkJVNB/u0Ntwm/tbUUwEC1W4Xf2jo2Xr5rJls6LhR+heZmfwe5tMympqHknajwob6BObF1NgNHqOoLgH8Brp1qY6r6ZVVdp6rrVq5cmUP13FGrdl1caMn/PPws+bm6XnhIlo4rhZ9ZOmn5rpmsdl0ofGhtHa0p3yXZuQeuxi3sk0fC3wmsqVo+DNhVvYKq9qpqf/L6BqBZRFbkUHZQ1KpdFzuHmb3R2KgBJL8+suvV2yRT+P4tHdv3s00JydJxZW+YmFtaRmvKd4n7cQv75JHwNwLHiMhRItICnAdsqF5BRA6RZLhdRF6clLsnh7KDotbScbFzmD9Fe7vfGStZzF24mrFSREsnVfh+LZ3iKPws4S+dQdsFz9JR1ZKIXAT8EGgELlfVe0XkPcnnXwLeAPyVmEm8Q8B56uvfapHJlo5t0oTvT+1mMbs6MSW1dEKI2a2lk3r4fueku1L4Zl9qbh6pKd8lmaWzdAZtc7llTWLT3DDhvS9Vvf4C8IU8ygqZWrV7gIMSzZ+ivd3v/Gy3p56HMUWxqArf3Syd1NIZqSnfJZOvirr4FX480zZHfCl831MU3d4gIvWzizRomyY//4O25uziDkxn3iZmX0o9fL+Dtv0shdsbQkz4ueJn0NZYOv6Tn6uYM3vD76AtmCuEukgCZiA8hOQn4uoywbUK3//MpC6WQrpc/BEERO0UxWIk/NpLK7hIBGny82vpdHY2YGYfu7B0GoG2ICwdN/c8gMzDH55Qvjtqzz1Y/HYOxISfK0btgju1a8rwOYDp3tJpANq9ztKpVCp0jed5V938Lpqb/U9RNB6+S4UfQq+mn6UwYAsx4eeKqtLWpri5EQhkHn6RLB2ATlpa/Fo6neN53oXCBxNzCMnPVTubO7qlCt+/pRMVfmQCRvmlf0a3Cd+vwnc5LRNM8vN7kOvuTs83cKXwO2lu9jto6/5m3lmvJir8fIgJP0eM8kv/jC4Tvj8/u1Kp0NoKbi8f2xmAh5/VxQ2d3pOf+55cdxAK3yT8qPAjE1BVxwq/CWjzaum4jxnShJ+W7xo/lo5ftetH4XcHMmgbE36kDpVKhY6OcrLkagfp8n5dme7xUN0lfN8X1XI/aNvpfYqi+1v9dXlN+NnvvDTuZwsx4eeKe0sHTML3a+l0daUxu7N0fPvZPgZtm5r8HuSMdTeKS4Xf1OR/Hn60dCJ18ZXwfXr4JmbXlk6X1xkrxsZKl9xPy/QV87Jl6UC1S4U/NF6+a1SV1lYQGSMq/Mgkai0ddyrI91mn3d3uPfxU4ftSux0dabkuB239qt0s4btU+H4tHfd2pV1iws+Rolo6WcwuLR2/JyH5maXj70JilUqFAw5wr/DThO+rnbOEHxV+ZAKVSoX29lThu5u94VvhZx5+MWbpmIOcYk4OanZUahcNDRWam4um8P1ZOpVKhWXjoUaFH5mAqiaWjourCaZ00do6GojCd5fwGxvLNDX5S37G0nF1UIe0J9HVFULyc3fiVWNjyetBLlo6kSnJEr7LnaPbq6VTa2O5m7EC0Nnpcx6+4vZyuf5jdp/8TDm+DnKmV5MuRUsnMgGjdku4Tfhmxop/S6cTd70av8kvG7R1r/A7O31aOumSO0sH/PZqosKPTImqJh6+24Tf3FymsdHf7f46OlxdLC4lszf8WjouFb45uPg8yLlPfibm7u4iHeTsEhN+jphE4F7hA1Vz4d1i7A3XBzn/9ob7hO/Xw6+dfuuqZ+Pf0okKPzIl2Swdtx4+kKhs92QevksFFIKlU6Folo5Jfi6tu0zh++/VuGxre8SEnyPG0vGl8B0WWUV2spmfmKOl44Ys+bk8sJt9yqelc8ABsFRubwhLJYpAMArfT8L32e0tmqVj2tmPwvc5bmEsneLs29l9D5aGnQMx4eeKT4Xvs9vrXuH7tzd8efh+Fb5SNIVvBm2XxoAtxISfKyIV2tr8ePg+Fb5J+MXx8E3M6VRUV/i1dIqo8LOEHxV+pA5tbaXklZ8/hT+1WyxLp6GhRHOz63n4baiK116NudGNywN7B6oNAQzaRoUfqYP7m5+AbxUkUqK11fU8/A7An6WTXsfHrcIXSqVWr362Sfgu21kol9s9j1tAVPiRuri/NDL49vDNmAW4jbmRUqnZm8JvbU1jdjtVr1xu82rpuFf4UC63e9u3Y8KPTItvhe9DBfmJGa9qt7XVh8KHUqnNs6XjuicH5XKH5wvGuT/I2SQm/Bzxk/waGRtr9vanaGtLk5/rhN/iLfllCt9twi+X27y1c0PDGC0t4Evh+zvIQVT4kbr4Urujoy0eFb4PSwfGxlq92RuZh+/a0mn1dpBraxtNXrlt50qlw5ulIzKa3Mc3KvxIHdzfCMQwNubP3sg8fNddfZ/Jz5fCb/d4kEsTvh9Lx09Pzk/MNokJP0ey69m4T/i+VJCvXs3YmL8BTJ+Dtv4O7MVT+C0tI8mrqPAjdTCXGABfCr9Ig7apwvczbuHPw/c3buFH7VYq/gZtW1vThB8VfqQOnZ0VKhUB2p2W69PS8eXh+5yx4m/Q1p+l48/D7/Q2aBstnci0dHZWGB5uBmTGdfPEb8IvosJ3faN6Q6XiM+H7mY1VqXQm+3Z55pVzJlP40dKJ1CFL+G4ZG2vzpoI6OsqMjjYAbuP2eRJSa2sJ81O3OS03s3TcJ7/29jThu/fwARoahpyWC9UeflT4kTr4TPi+FH5nZ5mhIfcx+7R02tpKDA014ronV6m009AAIsNOy4Xqk83cK3yAhoZBp+VCdcxR4Ufq0NmpjIy0OC83PevUj8KvMDTU5LzccrmV1lZQHZ155ZxpayszPOzqrk8ZqdoVcZ/8MoXv1sZSNQm/qcm9wm9riwq/LiJyuohsFZFtInJxnc9FRD6ffH63iLwwj3JDo6urwsiIH4Vvkt/IzCvnTGdnmeFhHwnfDIz7SH6+En65bCwkH2q3vX2MgYEGXGtEVXOAaWz0ofBTMbE0bm8IObSeiDQClwFnAMcC54vIsRNWOwM4JnlcCHxxoeWGSGenMjzsQ+GnXnK/87KNwnd/kEuTHww4L7utrZxYOm5R9ednZwnfLZmH7+PAPsrAgODuHr72yaMFXwxsU9WH1PSvrwLOnrDO2cA31HAncKCIrMqh7KDo6vJl6fhL+F1dZS/jFpWKidmPvVFmZKR4ls7AgI+DnFHXPiyd1taxJOEvHfJI+IcCO6qWdybvzXWdRU8RE35Hh5+Bat+Wjo9xi0rFxOxH7ZY8Jfx00NZHr2aUvr6lNcyZRzT1DoETp4vMZh2zosiFIrJJRDbt3r17wZVzia+EnyU/9/aGr5lJqaVTRIXvI/l1dJQYHPSp8P3M0vFhY9kkj2h2Amuqlg8Dds1jHQBU9cuquk5V161cuTKH6rlihJYWvCT8sbFU4fc5L7urq8LQkPuYU7XrJ+FXvAxUq6YK34fa9ZPw0xkyTU3up6K2tY3R3x8T/kQ2AseIyFEi0gKcB2yYsM4G4IJkts5LgP2q+lgOZQdELwCjo63OS04VvntLp5JMRfXh4fuepeMj4fuzNzo6fHn4plfT2OhnoHpwcGkl/AXvtapaEpGLgB9ihrMvV9V7ReQ9yedfAm4A1gPbgEHgLxZabngYde0z4bu3dMwBxqel4yf5VTxZOqad/SS/MoOD7g9yIs0MDkJzs495+EvP0smlBVX1BkxSr37vS1WvFXhvHmWFi0n4Pgdt3Sd8E/PwsPuDXDZF0bXCr3i0dHx5+Jp4+O5jbmhooL8fGhvdWzrt7WP097u9fIZtltbhyys+FX46Xc+1pWNsrGJZOqa80VH3MYu0MjrqI/mN0NSkXs49EBH6+nydaTvmadzCHjHh54a/hJ/NSfej8H30arIpiq4TgfmNR0Z82BvCwICPmM2B3afCb252fZAbpaWlsuQsnaUVjVf8JXxoZGCgWAlfpInhYRDxk/B9WDppwnev8E07+zj3wJ/CNzFHhR+ZArODZFMk3ZGqIPcJ3yg/Hx6+P7VrbDMflk5DQ4OnhO9P4YsI/f3Q1OT6OlHm/+xjZpJNYsLPDX8KP1VBDQ1+FP7oqHuFnw3mFUvh+xnATAfn/Rzk+vp8WDrpQS4m/EhdQlD4rgcw/R7k/Cp8Pwe5gQEf9oZ/he8+4fuzsWwSE35u9DE8DKr+/hTuFX5q6fjw8NOE7zoRmN94dLRIHr5pZ58efnOza0vHxBwtncgU9NHba3ZQ12QJ373CHxuDSsWnn+1rlo6PaZmppePHz/ZxYK+1dFze0S0O2kamQbUv8dHd/6Tpn8JHwu/vF0Tcx+xP4fsftHV/XRm/Ct+ImQrg8kCXWjru29kmMeHnRi99fcVT+KZcPwc5nwrfh4efHuTcJ/w+KhV/B7m+8WsCurw4YBy0jUxDqvCLlfB76e0VbzH7SfhG4fs68cqPpdObXETMT0+uf/wEcpdnksdB28i09Hq1dHwqfL8J3/2g7ciIOfHLNZmlMwaUHZbcx8BAo7d29qXwzX2Lo8KP1MWvwjcHmxKufc6+PvFs6bhP+AMDDV4Pcmk93NHL4GCjVzFjcKvwBwf9HORsEhN+Toj4HbT186fo9TozyQzaloCSw5L7GRz0e5AzuEz4ffT3+zvI+VH4fQwONnlpZ5ssrWi80u/dw0/r4Q7/lo7BZfIbYGDA37hF1s5uFb7PXo0vMRMVfmQKKogUM+GH0atxmfz6GRryO1Cd1sMdxsP3OeU4rYc7oqUTmRLzL/Q9D9/g6k+hQB/792sAyc+twi+epdObnG9RJEvH37iFTZZWNN4wO2KxFP4gUPF6kPOV8H0mPz+9mj4GBhoC6Mm579VEhR+pQ5bwi/OnMDH39hZN4fczOBjCQc5VOytmyrE/D39kBMrlBqLCXzhLKxpvFFHh+4/Zj5/tV+G7P8gNA2WvA9WQntnsWuH7OcjZJCb8XPCf/LKE70oFmVPP9+/XANSue4Xvv51dxWzaub/fn6UD6SW4Xe3bY8BwtHQiU5HaGyEM2rq2dEJQ+O6nZfo/yLlq5zThF0nhp3e78nOQs8nSisYb/hX+4PhVFYpo6bhK+KNAif5+f+MWWTu7ijlt5xASviuFb8rxdbKZTWLCzwX/yU8VSqU2fCh8/2rXVfIzv61PP7tSgVKpGfeWjr9eDbhW+OnNT2LCj9TF/ywdgHK5HdcefrEUvinHXNLBXzuPjbVSNIU/MuJe4UdLJzIFvaiaBOTzT1EqtVMkS6dchnLZpdo1v61PSwdgbMy92vXZzuAn4UdLJzIFfah2Ar4VvltLJz3I+Yy5VHKpdkNR+C24Vvi+rDtI5+I348PSiQo/Uocs4RdH4fcC3TXluySL2WXySy+h4VfhGz/bvYfvS+2ahO9e4fuysWwSE34u9FGpdAG+k18bbv8UJmb/frarg1w6aOu3nd1aOn2Av54cmHKNwh/GzaWw/R/kbBETfi70Uan4t3Rcz9JRDeEg597S6evzd7IZwNiY61k6y6hU/PRqwLT18HB6P10X+3cctI1MS5bw/St8lwl/WU35LvFj6YQxaOvW0ukDulH1m/CNwgc3+3cv0M7YmL+YbRETfi4UMeH3jit8/5aOW4XvO+G7HbQ1Cl/VT68GUkunJVlyYVn6P8jZIib8XAjD0hkbSz18dVBqGAPVbpNfGJbO6KjLGSsm+VUqFc+WTnrTeFeWjt+DnC2WVjTe6KNS6QB8J782oIybG5n3oVq0WTr9QANDQ36SX2bpuPfwfVs6mYfvQuGbGWg+D3K2iAk/F/ool/0rfDOACa58zjB6Na4VfheqfmM2Hn4Jc20f22QK36elMzSUKnxXlk5U+JG6jAEjgSh8lwk/jFk65rK5LhV+pze1m5116noA07/Cdx9zVPiRuhjFkSr8MBK+bRU0AowFpPBHMFaWbQaCGLcwlo6pj31M8vM9aOvW0omDtpEpSRO+Ufj+B23BvgoyMYdwspmZkw5ukp+xdMC3peMqZnOjejMP3++g7dBQY7IUB20XwtKKxgu1Cd///Gyw/6cwZyKGMBXVXcxpGf7beWTE1YwVc6N632rXWDouPfyla+k0zbzK1IjIcuA7wJHAI8AbVfXpOus9gmmpMlBS1XULKTcsTPILIeG78/BThR+CpeNW4aseAPg+yLmKOU2u/ufhVyoAndjft0vAEL7HLWyx0Ba8GPiJqh4D/CRZnopXquofL61kD2FZOq48/HDGLdz62ZnC93nlSHcx9ybP/ufhVyqmp+Fq3/Y9bmGLhUZzNvD15PXXgT9b4PYWIWYHMVeq9J38XCn8cCwdM2gL7hS+v55cWm52EpJbhe8zZlXFjJ+46b36HrewxUIT/jNU9TGA5PngKdZT4EcicpeIXDjdBkXkQhHZJCKbdu/evcDquSBEhV8cS8etwh/wet+DtFx3Hn6m8H1bOibhu1D4YcRsixk9fBH5MXBInY8+OodyXqaqu0TkYOBmEfmdqt5ab0VV/TLwZYB169a5uEbAAjE7YDpDxq/CbwQEVwk/hHEL15aOz/Mt0nKzhO9O4RfR0lmKCn/GhK+qr57qMxF5QkRWqepjIrIKeHKKbexKnp8Uke8BLwbqJvzFRzjJT1VwM7CVDlQXSeGXgSGv8/DTct1ZOqna9T9om1k6T1guLQwbyxYLbcENwNuS128Dvj9xBRHpFJHu9DVwGnDPAssNiD6gmUrFJB6fyc9dtzc9yPkft8jOwLSd/AYBxj18n8lvbMxtT8632nWr8Je2pbPQaP4eOFVEHgROTZYRkdUickOyzjOA20RkC/Ar4HpVvWmB5QZEdq0R8Jv8TB1cDWx1oNpQU75L3Cd8s31Vfwe5tNxKRTE9ObcKPw7aLn4WNA9fVfcAr6rz/i5gffL6IeAFCyknbLLTsMG3pePyT7FsPGa/lo4re8NsPwQP3/zuLhJ+H0YTtgdi6bhX+Est4S+t/ooXahW+z+TnTuFnZyKC34NcuSyAi3u8mu1XKkbh+z0JyWU7LwMkEEsnjdnmXI5aGytaOpEJhKbwXXn4IcXsQu2mlo7/QVu3Ct/c8yAMS6cbk+wHLZbWB7QBzVHhR+oRjr1RREvHXfILR+G7izm7NHJatg9qZ+mAXUFjeq9AHLSN1KOIg7bhWDqmDu4Ufprww7A3XCR8vz25tNxslg7Y3b+NmAGW5KBtTPgLJjR7w5XC7y6Ywg9x0Hbp9+TScjNLJ62XLWoVfkz4kQnUqiD/yS/18G0PbBVN4RfV0vHbzmm5Wa8GXCn8aOlEJqCYnS+k5NeFuYb5sMUSa5WfTxXkWuH7PNksLdedpRNGO9cO2qb1skWm8KOlE5lA7Q0iIBRLB+ypoPR64X57NWm57gdt/V0zKS3XnaXjv/ealutm34ZQZibZIib8BVE7ZxdCmYcP9v4Uk2MuhtodANqoVEys/ufhd2IEhy3rrvb2hhBCO7tQ+LWDttHSiVRRe7MECEHh2/5T1F5cqrp817hVu+Z+tmHFrJjelg0Gku2HMUvHncKPg7aRKZmc8P0P2tr+U9Seel5dvmvcWzqdAcWctrOtuCcf2MOJ2ZaYKWN6TXHQNlKXMOyNyYO2UCxLpxMYwfxhbWEUflgxg4sDezgxtyQP273XOGgbqUuIlo67hB+O8kuTn02VX6vw/dsbtmMOUeGDSca29+1M4ceEH6kijOQ3eR5+dd3ypvbysRCC8nOR8AcolqWTXRo5nHYGu9eKqlX40dKJTCAMe8OtpTPZw1/6ajfddpEsnTB6r2m5mcK3eSZ5tm9DtHQikwhR4UdLxw6hDdrajrn25idp2T6YbOnY771CVPiRSaQ7iF/lV6vw2zHNWrRBWyiWwnc1SyekdgZXCt93r8YWMeEviD5MsmkMaNBWMH8KWyqoF2gFWrz/KdxaOqEO2tpOfiGdbwGuFL7vmG0RE/6CqD0NG0KwdMCuCgoj5rRcNwOYSniDti4snUagLaCYwbXCj5ZOpIraCy1BCJYO2E/42ann1eW7xt0A5ihmjn9Ilo6LaZnZ7Q3Tsn3gY5aO75htERP+gpisdv1fORKKp/BtJ7/0twzJ0mkBmrGr8ENrZ7BrV/YRil1pi5jwF0SIyQ/sqqAwejVpuW7UbrrdrsDa2eY1hELsyYHZ98Ywva68CecgZ4ulFY1zsoQf1p/CrcL3r3bdKfyw2tnmNYQmJz//7Qx2r6cTzkHOFjHhL4hQFb4bDz+cmJuxa2+EqvBtXhY6xHYGu/e1jQo/Mi1FVPghWjpgV+2m2w1R4RetnW1eOiQq/Mi0hGhvgP2ZDCHGbDPhhzhoa+rjUuGHEbPNM8nD2bdtERP+vMlu9Qf+u4D1LZ2874ZUSbYbYlffRcIPzdJx6+GHEbNNhR9OzLZYWtE4JU0CIXZ7u7BzN6SQY7aZ/J5OnpcHFnM32clCeVJ7YA8rZtsKP4yYbRET/ryZfClVWOrd3tBjtmVj7UmeDwos5h5gr4VS0tsbhmjpuFX4MeFHEuonv6Xd7Z18R6C0bB9kN/QGk/z2TLf6AtiLOaC0BNbOacLP27oLuZ1tTcssYw50YdiVtlha0Til9lKqvruAbrq9ky8fm5btg1rltxLYbamkvcByILR27sEkqv05l5JdOA1Ca2db0zLDsittERP+vAlZ4dtK+LU3iAgr5pUYhV+Z5hvzJUv4YcXckzzn3bMJed9OL/9tq/caFX6kLvW7vWEoPzceflgxr8So3aen+cZ8CVnhQ/4Jf/Kdn9KyfVAbc3r576W9b9siJvx5U1/thtXtdaOCwoh5ZfJsw9aZrPDDiNlWwt+VPB8ChBYz2DnP5Knk+SDAf8y2iAl/3oTc7bWl8FOvOMSYbSd8k1zDitlWwn8oeT4CCC1msKPw05iPAvzHbIulFY1TQu722kr4d2MU0AogtJhtJXyleJbOw8BqoA0ILWawo/C3Y9KhOcj5jtkWTb4rsLgoA7cDVwNfwewc5k/huwvoZh7+bcBJpDohrJhtJfwBzOV4Q7R0DsS0hY2Ef9T4Ulgxgx2Fvw04HHOfAf8x2yIq/BkZA24G3oNRPa8A/g04C5P8q+8nG0q3tw1ze7o8VdAe4H7gZePvhBWzrYSfJtMQZ+k0YHpcNhL+0eNLYcUM9hT+M8eXfMdsiwVFIyLnisi9IlIRkXXTrHe6iGwVkW0icvFCynTDCHAd8BeYgavTgCuAU4DvYAZ4rgQOHf+G7y6gvZkMZeAe4LPJ8snjn4QVcysmEeSd8NMzWUO0dCD/E85GgZ1UK/zwYrZxVnVtwvcdsy0WauncA/wPjOSti4g0ApcBp2L2pI0iskFV71tg2TkzANyEsWuuw+xQBwBnAm/AJP32Kb/tWxHkM7BVwez4G4FNyfNmYDD5/HDgReNrhxezjZOvahN+CDGXSqWqd/JO+I9ixi0mWzrhtPMyjOgaBDpyKGE/5jdc+gp/QQlfVe+HGY+CLwa2qepDybpXAWcD1hL+d77zHcrl8ozrNTUNcuihv2bNmo2sXr2FpqZRhoe72LlzHTt2vIgnnjiOSqUJczD43rTb2rRpE+BXBT344IN8+9vfBuC1r62wb9+93H77t6f4htLR8RQ9PQ+zfPlD9PQ8xPLlD9PSYpJ7qdTC008fwd69f8KePUezd+/R9PYeAlwzvoXbbrttvGwfiAiPPvroeMynnSY0N/+MzZsvRqRCQ0MJkTKNjaVZvRYp09BQSh7mdVfXbnp64Prr72D//p1s2bLFe8xPPPHEeMyveMUwBxywjVtu+QwiZZqaRmhqGk6eqx/Ze42N5rm5eXj8dfpobh6ktRV+/OPtPPmkKeOhhx7yHvO+ffvGYz744B5e/er9PPbYiezff9ikNmtsHJv0XvUjbffsszGamuDWWx9j505TxpNPPuk1ZltI7ZFznhsRuQX4a1XdVOezNwCnq+q7kuW3Aieq6kVTbOtC4EKAww8//ITf//73c65PZ2cng4ODdT876CA4+2w45xw49VRobYVdu+B734Orr4Zbb4VZHCvq0tHRwZ49e2hra5vfBhbACSecwObNm8eXr7kGTj4ZjjwSBgfhGc+AF70I1q3Lng8+2Kw7Ogp33w0bN8KmTeb5vvtm9zs0NTWxa9cuVq5cOfPKOXPaaadx8803jy+/4x1w2WUw159/ZMT8BqOjMDZW+3pgADZvhve+F6qF9QMPPMAxxxyTUySz501vehNXXnnl+PJb3gJf/So0zUK6lUrQ329iqn5MfO/xx+FTn6qNF+DOO+/kxBNPzDmimXnf+97HF77whZr3PvIR8yiVsvZKH9XtOdvH/v3w2c/C0IQLzF5//fWsX7/eYbQLR0TuUtW6FvuMCV9Efkx6BkYtH1XV7yfr3MLUCf9c4DUTEv6LVfV9M1V83bp1mirnufDggw/WdAEbG5+iq+vHdHX9kI6OXyJSZmxsNf39p9HXdxrDw8eTx/h1T08PPT09M69ogf7+fnbt2jW+3Nb2aw4//DyGh59LY+NTNDc/AYBqA6Ojz2J4+DiGh5/H8PBxjI6uRbVlXuUeeOCBHJweORwzNDTEjh07at5rbHyKlpZHUG2uejSNv4ba983g9txUXHd3N6tWrcotjrkwOjrKI488UvNec/N22tvvplJpQ7WDSqWDSqWdSqU9WTbPJv75KdaOjg4OO+ywhQcwD0ql0ngvwyWtra0cccQRzstdKAtK+LMs4BamTvgvBS5R1dckyx8GUNVLZ9rufBO+YSfGfrga+DnGlzwGOCd5nMB8d/7Fw1swPvw6jPe+Djie7KbfkUhkqTFdwncxD38jcIyIHAX8ATgPeJO94gaAVwG/TJaPAz6OSfLHsfSTfDVX+K5AJBIJiAUlfBF5PfAvmOkR14vIb1T1NSKyGviKqq5X1ZKIXAT8ENN/vlxV711wzaekE6Pkz8Yk+T+yV1QkEoksInKxdGyxMEsnEolEisd0ls7SmmQaiUQikSmJCT8SiUQKQkz4kUgkUhBiwo9EIpGCEBN+JBKJFISY8CORSKQgxIQfiUQiBSEm/EgkEikIQZ94JSK7gblfLtOwguxW9CER6zU3Yr3mRqzX3FiK9TpCVetevjbohL8QRGTTVGeb+STWa27Ees2NWK+5UbR6RUsnEolECkJM+JFIJFIQlnLC/7LvCkxBrNfciPWaG7Fec6NQ9VqyHn4kEolEalnKCj8SiUQiVcSEH4lEIgVhUSd8ETlXRO4VkYqITDmFSUROF5GtIrJNRC6uen+5iNwsIg8mzwflVK8ZtysizxaR31Q9ekXkg8lnl4jIH6o+W++qXsl6j4jIb5OyN831+zbqJSJrROS/ReT+pM0/UPVZbr/XVPtK1eciIp9PPr9bRF442+8uhFnU681Jfe4WkTtE5AVVn9VtT4d1O0VE9le1z8dn+13L9fqbqjrdIyJlEVmefGblNxORy0XkSRG5Z4rP7e5fqrpoH8BzgGcDtwDrplinEdgOHA20AFuAY5PPPg1cnLy+GPiHnOo1p+0mdXwcc8IEwCWYm8Ln/XvNql7AI8CKhcaVZ72AVcALk9fdwANV7ZjL7zXdvlK1znrgRszNkV8C/HK237Vcr5OAg5LXZ6T1mq49HdbtFOC6+XzXZr0mrH8m8FPbvxnwcuCFwD1TfG51/1rUCl9V71fVrTOs9mJgm6o+pKqjwFWYG96SPH89ef114M9yqtpct/sqYLuqzves4tmy0Hi9/V6q+piqbk5e9wH3A4fmVH7KdPtKdV2/oYY7gQNFZNUsv2utXqp6h6o+nSzeCRyWU9kLrpul7+a97fOBK3Mqe0pU9VZg7zSrWN2/FnXCnyWHAjuqlneSJYpnqOpjYBIKcHBOZc51u+cxeWe7KOnSXZ6XdTKHeinwIxG5S0QunMf3bdULABE5Ejge+GXV23n8XtPtKzOtM5vvzpe5bvudGJWYMlV7uqzbS0Vki4jcKCLPneN3bdYLEekATgeurnrb5m82HVb3r6YFVc0BIvJj4JA6H31UVb8/m03UeW/Bc1Gnq9cct9MCnAV8uOrtLwJ/h6nn3wH/CLzDYb1epqq7RORg4GYR+V2iTOZNjr9XF+aP+UFV7U3envfvNXHzdd6buK9MtY6V/WyGMievKPJKTMI/uert3NtzjnXbjLEr+5PxlWuBY2b5XZv1SjkTuF1Vq5W3zd9sOqzuX8EnfFV99QI3sRNYU7V8GLAref2EiKxS1ceSbtOTedRLROay3TOAzar6RNW2x1+LyL8D17msl6ruSp6fFJHvYbqTt+L59xKRZkyy/5aqXlO17Xn/XhOYbl+ZaZ2WWXx3vsymXojI84GvAGeo6p70/Wna00ndqg7MqOoNIvKvIrJiNt+1Wa8qJvWwLf9m02F1/yqCpbMROEZEjkrU9HnAhuSzDcDbktdvA2bTY5gNc9nuJO8wSXoprwfqjujbqJeIdIpId/oaOK2qfG+/l4gI8B/A/ar6TxM+y+v3mm5fqa7rBclsipcA+xMbajbfnS8zbltEDgeuAd6qqg9UvT9de7qq2yFJ+yEiL8bknT2z+a7NeiX1OQB4BVX7nIPfbDrs7l95j0K7fGD+3DuBEeAJ4IfJ+6uBG6rWW4+Z1bEdYwWl7/cAPwEeTJ6X51SvututU68OzI5/wITvfxP4LXB30qirXNULMwtgS/K4N5TfC2NRaPKb/CZ5rM/796q3rwDvAd6TvBbgsuTz31I1O2yq/Syn32imen0FeLrqt9k0U3s6rNtFSdlbMAPKJ4XwmyXLbweumvA9a78ZRtw9Boxhctc7Xe5f8dIKkUgkUhCKYOlEIpFIhJjwI5FIpDDEhB+JRCIFISb8SCQSKQgx4UcikUhBiAk/EolECkJM+JFIJFIQ/j8R0RFWKop1XgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.plot(x2.numpy(),y2.numpy(),color=\"black\")\n",
    "plt.plot(x2,F2M3prediction.data.numpy(),color=\"yellow\")\n",
    "plt.title('Function-2 Model Prediction 3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "bced3823-8279-4c48-a6a9-c1761cf36543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/P3PnzuXMM8/klltuCbgfN998M9OmTeO8886jffv2zqARoF+/frz33nt069aNF198kaFDhzJixAjuvvtuZ/qgQYOc+ceNG0dCQgLLly8nOzubiy66iB49etjeLLyopCbMy+ETB+HCEaTkb4t2UYQQQohSoVq1aqxcuZKxY8dSt25dbrnlFiZOnMimTZtYt24dV155JYmJibz66qskJSWRlpbG3r176d27NwCVKlWiSpUqLFy4kL59+xIbG0u9evXo1q0by5cvB6BTp040atSImJgYEhMT2blzJxs3bqR58+a0aNECpRR33HFHwHL26dOHadOmMXnyZI8bgKempnLs2DHnfSX79+/PggULfNLvvPNO5zKzZ8/m008/JTExkQsuuIDDhw+zZcuWsL6vUhPm5UDmPrjqSfblNQPOiHZxhBBCCA+BaqwiKTY2lu7du9O9e3fOPfdcJk2aRIcOHWjdujVLlizxyHv8+HHbdQS6X7V7rVVsbCx5eXkAhap5ql+/PnFxccyZM4eRI0eyePHigPm11n7Xr7Xmvffeo2fPnh7pO3fuDLk8wUhNmJdYR3tkgZb2SCGEEAJg06ZNHrVAq1atomnTppx99tmkpKQ4g7Dc3FzWr19PjRo1aNSoEd9++y0A2dnZZGZm0rVrV6ZOnUp+fj4pKSksWLCATp06+d3uOeecw44dO9i2zbROTZ48OWhZX375ZYYPH05sbKwzLSEhgVq1avHHH38A8Nlnn9GtWzdq1qxJQkICCxcuBOCLL75wLtOzZ08+/PBDcnNzAdi8eTMZGRmhvF0hk5owLxViTRCWL0GYEEIIAUB6ejoDBw7k2LFjVKhQgTPPPJOxY8cSHx/P119/zWOPPUZqaip5eXk88cQTtG7dms8++4wHH3yQF198kbi4OKZNm0bv3r1ZsmQJ7dq1QynFm2++Sf369dm4caPtditVqsTYsWO55pprqFOnDhdffDHr1q0LWNYuXbrYpk+aNIkBAwaQmZnJ6aefzoQJEwCYMGEC99xzD1WqVPGo9brvvvvYuXMn7du3R2tN3bp1nUFluKhAVYOlUceOHfWKFSsitv5V+9Zz3sdtuCVmKlP+2ydi2xFCCCFCtWHDBlq2bBntYogg7I6TUmql1rqjXX5pjvTiao4sW8GpEEIIIcoWCcK8VHAEYVqaI4UQQggRQRKEeYndtxeA/MOHolwSIYQQQpRnEQvClFLjlVIHlVK2PeiUMUoptVUptUYp1T5SZSmM2MxMAAqyMqNcEiGEEEKUZ5GsCZsIXBVgfi+ghePxAPBhBMsSMlUhDoC8fGmOFEIIIUTkRCwI01ovAI4EyHI98Kk2lgI1lVKnRao8oYpxBGE//h31ogghhBCiHItmn7CGwB6310mOtKiKiYs3E0pqwoQQQgiAw4cPk5iYSGJiIvXr16dhw4YkJiZSs2ZNWrVqFdI6xowZw6effuqTvnPnTtq0aWObrpTiv//9rzPt0KFDxMXF8eijjxaq/NWqVQtLnnCLZhBmd58A23EhlFIPKKVWKKVWpKSkRLRQMU2bARAfcyKi2xFCCCHKitq1a7Nq1SpWrVrFgAEDePLJJ52vY2KChxJ5eXkMGDCAfv36FWq7p59+Oj/88IPz9bRp02jdunWhy19aRXPE/CSgsdvrRsA+u4xa67HAWDCDtUayUDHKfJhaVdkeyc0IIYQQ5UJ+fj73338/ixcvpmHDhsycOZPKlSvTvXt3unTpwqJFi7juuutIS0ujWrVqPP3006xcudI5Sv3FF1/sd92VK1emZcuWrFixgo4dOzJ16lT69OnDvn0mXNi1axf33HMPKSkp1K1blwkTJtCkSRN27NjBbbfdRl5eHldd5dk9/a233uKrr74iOzub3r17M3To0Ii+P4FEMwj7DnhUKTUFuABI1VonR7E8AChHBd2qrDOjXBIhhBDC1xOznmDV/lVhXWdi/URGXDWiSMtu2bKFyZMn8/HHH9OnTx+mT5/OHXfcAcCxY8eYP38+AEOGDHEuc/fdd/Pee+/RrVs3Bg0aFHD9t956K1OmTKF+/frExsbSoEEDZxD26KOP0q9fP/r378/48eN57LHH+Pbbb3n88cd56KGH6NevH6NHj3aua/bs2WzZsoVly5ahtea6665jwYIFdO3atUj7XlyRHKJiMrAEOFsplaSUulcpNUApNcCR5SdgO7AV+Bh4OFJlKQyrJsxPy6gQQggh3DRv3pzExEQAOnTowM6dO53zbrnlFp/8qampHDt2jG7dugFw5513Blz/VVddxZw5c5g8ebLP+pYsWcJtt93mXI91I+5FixbRt29fn/XPnj2b2bNnc95559G+fXs2btzocWPykhaxmjCtdd8g8zXwSKS2X1TaCr7O/YLs7IeoWDG65RFCCCHcFbXGKlIquv1RxsbGkpWV5XxdtWpVn/xaa5Sy6xZuLz4+ng4dOvD222+zfv16vv/+e7953ddrtw2tNc8//zwPPvhgyNuPJBkx30t2XraZaLqIzZujWxYhhBCivKlZsyYJCQnOWqsvvvgi6DJPPfUUw4cPp3bt2h7pXbp0YcqUKc71WP3LLrroIo90S8+ePRk/fjzp6ekA7N27l4MHDxZ/p4pIgjAvCZUSnNP/rMlzzdi2DZSCWbOiUCohhBCi/JgwYQKPPPIInTt3pnLlykHzt27dmv79+/ukjxo1igkTJtC2bVs+++wzRo4cCcDIkSMZPXo0559/Pqmpqc78PXr04LbbbqNz586ce+653HzzzaSlpYVvxwpJmVbBsqNjx456xYoVEd2GGqpg6WOM7P4Gj121GVq3hsmToV8/uP12+PzziG5fCCGEcLdhwwZatmwZ7WKIIOyOk1Jqpda6o11+qQmzUS+mBlQ4waH1ByAxEZ5/3tSCAZSxoFUIIYQQpZMEYTaqxFSkYtxRUnY7Ohf++acEYUIIIYQIKwnCbFSJrUR83DFSUs19JNFagjAhhBBRVda6D51sinJ8JAizUaVCZWLj0jmeEetKlCBMCCFElFSqVInDhw9LIFZKaa05fPgwlSpVKtRy0Rwxv9SqElcF4jJIz3IEYVqDdW8s+QIIIYQoYY0aNSIpKYlI3z9ZFF2lSpVo1KhRoZaRIMxG1fiq6Phk0k/YNEcWFESvYEIIIU5KcXFxNG/ePNrFEGEmzZE2EirVpKBiBuk5NkGYEEIIIUQYSBBmI6FKLXIrZZCeE28S3JsgpTlSCCGEEGEgQZiNhCqnkFMpi7RctyBMOuYLIYQQIowkCLNRs3odCmLzOaE0+cRIECaEEEKIsJMgzEZCVccNQiulkoHjDvAShAkhhBAijCQIs5FQqaaZqJhKOtWkJkwIIYQQYSdBmI2aVhBW6ZgJwvLyZJwwIYQQQoSVBGE2EiommIlKjpqwv/+GrVtNmgRhQgghhAgDCcJsJFRyBGFWcyTAlCnmWYIwIYQQQoSBBGE2XM2RbkHYihXmWYIwIYQQQoSBBGE2Tql8ipmokuIKwiwShAkhhBAiDCQIs1Elrgo1shTU2CtBmBBCCCEiQoIwPxqkxUB1myBMCCGEECIMJAjzo9EpTe1rwoQQQgghwkCCMD+ann8Z1NwpQZgQQgghIkKCMD9a1jkHqqaQUll5zpA+YUIIIYQIg4gGYUqpq5RSm5RSW5VSz9nMT1BKfa+UWq2UWq+UujuS5SmMc+qcA8C+Osc9Z8yaFYXSCCGEEKK8iVgQppSKBUYDvYBWQF+lVCuvbI8A/2it2wHdgbeVUvGRKlNhtKzbEoADdY9EuSRCCCGEKI8iWRPWCdiqtd6utc4BpgDXe+XRQHWllAKqAUeAvAiWKWRNE5qi8itxuE5KtIsihBBCiHIokkFYQ2CP2+skR5q794GWwD5gLfC41rrAe0VKqQeUUiuUUitSUkomKIqNiaVK1lmk1kkuke0JIYQQ4uQSySBM2aR592rvCawCGgCJwPtKqRo+C2k9VmvdUWvdsW7duuEup18JOS3JaHigxLYnhBBCiJNHJIOwJKCx2+tGmBovd3cD32hjK7ADOCeCZSqUU/JbklNlJ1kVol0SIYQQQpQ3kQzClgMtlFLNHZ3tbwW+88qzG7gcQClVDzgb2B7BMhVK3ZhzQGk21452SYQQQghR3kQsCNNa5wGPAr8AG4CvtNbrlVIDlFIDHNleAboopdYCvwHPaq0PRapMhdUgzlwhuXFg3yiXRAghhBDlTUQb2rTWPwE/eaWNcZveB/SIZBmKo2HlFqAVG1qVXD80IYQQQpwcZMT8AKrGV4b0+uzWx4NnFkIIIYQoBAnCAoiPB9IasDdDrpAUQgghRHhJEBZAXByQ1oDkNO+LOiPgyiuhr/Q9E0IIIU4WEoQF4AzC0ksgCPv1V5gyJfLbEUIIIUSpIEFYAKY58jQOZaWQK++UEEIIIcJIQosArJowgP3VolsWIYQQQpQvEoQF4B6E7asd75rRsiUkJUWnUEIIIYQoFyQICyA+Hkg/DYD9113qmrFxI4wfH51CCSGEEKJckCAsgLg4IL0+APtrxYW+4OrVsGNHZAolhBBCiHJBbk0dQFwckFEXhWI/6Z4zlfK/YGKiedY6UkUTQgghRBknNWEBxMcDBXEkxNXxDcKKQ2v44APIyAjfOoUQQghRpkgQFkCcowXylPj6wYOwefPgggsgNzf4in/6CR55BAYNKnYZhRBCCFE2SXNkAFYQVjOuHvur7A6c+d57Yft22B0kH7hqwFJSildAIYQQQpRZUhMWQLxjVIqE2Prsj8/xzTBjhukblu6nlkwpmDDBPh2kz5gQQghxEpMgLACrJiwhtj770/eja1T3zPDSS+b59ddNLRhAfr5nnuef911xoE79QohS7+OPYcOGaJdCCFHWSXNkAFYQViOmPifyTnC8UkUSjrtlsIKp115zpf34o+dKDhzwfB0TAxUcb7vUhAlRJj3wgPkah9IFVAgh/JGasACs5shqOMYKi892zczMhIIC34X+/e/AK9Xa9cstQZgQZVZeXrRLIIQo6yQIC8CqCXMGYZXcfnXfeAP27i3eBmbMgJ07i7cOIYQQQpRJEoQFYAVhVbUjCKvodep79GjxN9Krl/QRE0IIIU5CEoQFYDVHVs53BGHVIrCRjRsjsFIhhBBClHYShAVg1YTF5dUiLiau6EHYCy8Uv+lSCCGEEOVKSEGYUqqqUirGMX2WUuo6pVQh7mhdNllBWH5eDPWq1St6EDZsGFxzTdjKJYQQQoiyL9SasAVAJaVUQ+A34G5gYqQKVVpYzZE5OVC/Wv3iNUeuXg379oWlXEIIIYQo+0INwpTWOhO4EXhPa90baBW5YpUOVk1Ybm4YgjCAhg2LXSYhhBBClA8hB2FKqc7A7YA1GmnQgV6VUlcppTYppbYqpZ7zk6e7UmqVUmq9Ump+iOUpEbGxZmzV3FyoX7U+++tUjHaRhBBCCFFOhDpi/hPA88AMrfV6pdTpwO+BFlBKxQKjgSuBJGC5Uuo7rfU/bnlqAh8AV2mtdyulTi38LkRWXJyrOfJgfC75CmJljFUhhBBCFFNIQZjWej4wH8DRQf+Q1vqxIIt1ArZqrbc7lpsCXA/845bnNuAbrfVux3YOFq74kRcXZ2rCmlSrT4Eu4FAVqJcR7VIJIYQQoqwL9erIL5VSNZRSVTFB1Cal1KAgizUE9ri9TnKkuTsLqKWUmqeUWqmU6hdqwUtKfLyrTxhEaKwwIYQQQpx0Qu0T1kprfRy4AfgJaALcGWQZu2HgvRvyKgAdgGuAnsB/lVJn+axIqQeUUiuUUitSUlJCLHJ4WM2RDao3AGBvjRLdvBBCCCHKqVCDsDjHuGA3ADO11rn4BlTekoDGbq8bAd5jNCQBs7TWGVrrQ5ihMNp5r0hrPVZr3VFr3bFu3bohFjk8rObIetXqAXCwaoluXgghhBDlVKhB2EfATqAqsEAp1RQ4HmSZ5UALpVRzpVQ8cCvwnVeemcAlSqkKSqkqwAXAhlALXxKs5sjalWsDcPj9t2Du3CiXSgghhBBlXUhBmNZ6lNa6odb6am3sAi4Nskwe8CjwCyaw+spxZeUApdQAR54NwCxgDbAM+ERrva4Y+xN2VnNkjYo1qBBTgcMnjsCll8IPP0S7aEIIIYQow0K6OlIplQC8BHR1JM0HXgZSAy2ntf4J04fMPW2M1+u3gLdCLG+Js5ojlVLUqVKHA+kHzIxrroEtW+Cjj+B//4tuIYUQQghR5oTaHDkeSAP6OB7HgQmRKlRpYjVHAjSu0ZiktCTXzDPPhMeCjdQhhBBCCOEr1CDsDK31S1rr7Y7HUOD0SBastDh61FR4gemcfzDDayizxo1h0iTo3RsGDDBpFSvC8OGuPA8/DImJoW1QKdi8udjlFkIIIUTpFuqI+VlKqYu11gsBlFIXAVmRK1bpsWOHa7pulbqs2r/KN1O/fuYB8OGH5rmgwARU990HtWqZNOU1akedOnDokO/6pk+H558vdtmFEEIIUXqFGoQNAD519A0DOAr0j0yRSq+6VepyMOMgWmuUd0DlLSYGBnmNZ6sdo3rMnw9t2zrGvqgXmcIKIYQQolQL9erI1VrrdkBboK3W+jzgsoiWrJS48UZo0sRMN6jegJz8HA5nHS7eSrt1M7Vjp54KI0cWv5BCCCGEKHNC7RMGgNb6uGPkfIB/R6A8pU7lylDBUV94WvXTAFxXSIaDXcd+7TUO7v798NRTkJ8fvu0KIYQQIqoKFYR5CdIeVz7ExrpiH+f9I9P3l2whHngA3nkH5swp2e0KIYQQImKKE4QFu21RuRCVIMy7v5k1RkZBQWS3K4QQQogSE7BjvlIqDftgSwGVI1KiUmbxYkhyDA1mBWH70rxvgRlhwS4CEEIIIUSZE7AmTGtdXWtdw+ZRXWsd6pWVZdqmTa7p6vHVqRZfjb1pe8O7kYce8ny9ejUMHeqbz7uvmBBCCCHKrOI0R55UzLBfikY1GpF0PCn4AoVx4YWer6dOhSFDXM2PVk2YBGFCCFFkSUnQvr251kmI0kCCsCCsixetfmGnVTst/H3C3vJz60xphhRCiLAZPRr+/hsmnBQ33RNlgQRhQZxmRqVwBWHVT2PLkS3h3UhSmGvWhBBCCFHqSRAWRGysec7LM88NqjXgYMZBjmcf979QYcXF2adbzY/SHCmEEGEjP6WitJAgLAgrCLNqwlrUbgEQ3n5h//lP4PnSLCmEEMUmP6WitJEgLAjvIOyMWmcAkJKREr6NdOkSWj45fRNCiCKTn1BR2kgQFoR1yyKrObJu1boApGSGMQiT5kghhBDipCNBWBBW8OV+dSSEuTnSXxBmOXLEPKenh2+bQghxkpHmSFHaSBAWxOLF5vnQIfNcp0odAJ785cnwbSRYELZokXm+447wbVMIUSRSIS2ECBcJwoK49lrzXLGieVaROJWqVMk+fcqU8G9LCCFOchJIi9JCgrAgrODLuoc2QKyKDe9GYvwcBqn5EkIIIcotCcKC8O6YD3Bf+/sAKNAF4dlI48b+58kpmxBChJX0DROlhQRhQVjdtdxrwj5a+REAX6z5IvIF8FdLJsIrLw8uuQTmzo12SYQQESbntqK0kH/4IOxqwvq16wdAVl6WT/5Ro+C770qiZCKskpNh4ULo1y/aJRGlnPyBl11SAyZKGwnCgrCrCXv10lcB0Da/xo8/DtdfX4QNDRtWhIXKMa3hww8hM7NktidjsQkhhChhEQ3ClFJXKaU2KaW2KqWeC5DvfKVUvlLq5kiWpyjsasIaVG9AfGw8249uD9+Ghg4t3vI7dphA4vffw1OeaPvuO3j4YXj++ZLZXkaGeT52rGS2J4QQ4qQXsSBMKRULjAZ6Aa2AvkqpVn7yDQd+iVRZisOuJiw2JpbmNZuz5ciW8G0oJ6d4yy9YYJ4nTix2UUqF444bpB8+XDLbGz3aPJdUzZsQQoiTXiRrwjoBW7XW27XWOcAUwK6hbiAwHTgYwbIUmV1NGEDLui3ZcGhDyRfIn/LWjOZ9y6aS2p4QQghRQiIZhDUE9ri9TnKkOSmlGgK9gTGBVqSUekAptUIptSIlJYz3bAyBXU0YQL2q9dh4aCNbDoepNqxdu/Csp7z1PC1v+yPKPInXyz45hqK0iGQQZvfv6f3RHwE8q7XOD7QirfVYrXVHrXXHunXrhqt8IfFXE9aoRiMARi8fHZ4NvfJK8ZYvb78qJV0TFs1gzzvCF0JEhJzTidImkkFYEuA+CmkjYJ9Xno7AFKXUTuBm4AOl1A0RLFOh+asJe+T8RwBomtA0PBuqWrV4y5d00BJp5W1//Fm2DOLjYfbsaJdEiHKvvJ2rirIvkkHYcqCFUqq5UioeuBXwGEFLa91ca91Ma90M+Bp4WGv9bQTLVGhWTdj8+Z7pNSvVpFKFSuw5vsd3oaJYsiQ86ykvQcvJEoT98Yd5/qVUXpcihBAigiIWhGmt84BHMVc9bgC+0lqvV0oNUEoNiNR2w82KBcZ49VpTSlE9vjrvLn2Xn7f8XPwNWUMkFFV5O8U7mZojofwdPyFKoWh/zYXwFtFxwrTWP2mtz9Jan6G1HuZIG6O19umIr7W+S2v9dSTLUxT5AXqrZednA3D9lOuZtGpS8TYUrpHay8uvzMlSE1be968cknhZCBEuMmJ+EAkJ/uddcfoVAOQW5HLXzLuKt6GKFUPLN2qU/Zhi5e2foaT3J9rvX7S3L8RJRL5uorSQICyIBg3M82OP+c4bddWo8G0oPj60fI8/HjhgK281K2Vhf44dg+zsoi1bFvZPCCFEREgQFoIaNSDG5p1qUL1B+DZSv3541pOWFp71nGyKc2pcqxZcemn0ti+EKBQ59xGlhQRhIYiJse8bpsL5TY6NLd7y1p/4V18Vvywno4KC4i1f1Ktb5d9AiBIn5zyitJAgLASxsf476PdrF6YO9QBvvhm+dQkhIkL+wMsuOecRpY0EYSEIFITd1e4uz4RLhkGdjUXb0KBB8NdfRVu2vP4zlNR+ldf3TwjhJF9zUdpIEBaC2Fj/rVVdGndxTienJcPlL8Bd3Yu+sfPOK9py5e3XpaRPWYvbHFlc5e34CSGECEqCsBD46xMGULGC60rFTYc3mYkKJ4q3we+/L97yovCiFQRJ+4gQJUa+bqK0kSAsBLm5cCKEuOrBHx40E7pw3/T77oNPP3VLuPbawtfMSE1K2SbHTwghTjoShIXg4EH48ssA858+CEC7eu1MQlxmodY/bhz07++VqBTMm1eo9ZRL5b1PmJyalzkSL5d9cgxFaSFBWBjUrVqX3uf05psN35iECjYj2hdFt24wfXpoecvbr4r0CRNCCFHOSRAWJncn3k2+DnCjyaK6/vrQ8pWlP/F33oFTT412KYI7cQKWLo3sNqQmTAghTloShIWoevXA83u16BWZDcfGws6dsHt36MtoDe++CykpkSlTUezZA336QFYWPPVU5MqmNbz0EmzYUPjlvD30EHTubN7/wixXFGUpiBaijJJzHlHaSBAWomB3A6oQU4G+bfpGZuNNm0LjxoH/qN3nrV0L//433HtvZMpTFE8+CdOmwQ8/FG65wgYnR4/Cyy8X/jZCds2R1phtqan+l5PgSQghRBFJEBaCBg3M/SODefPKKI547x5EpKeb59JUE2YFK+6nooECGCtfZiasXl347RX2htqhBriFmVcY06aFZz0i4iTuLvvkGIrSQoKwEHTsCKefHjxfoxqNnNNL9hTxXoKhOn7c83Vp/1WxC8K2bQu+3FdfQWKiqeEKRVHbG+zev1DWVdz33Vr+4MHirUcIEZT1kzNlSnTLIYRFgrAQxMdDTqgXPI5bBMAjPz2CGqpYvb8ItTgOx4/DqFF+/udvugkOHHC9Lu1B2Jo15nnFCldaYQKmzBCH/bDWWdj3I1o1YaX9uAlRjliV6huLeGc5IcJNgrAQWEGY1ib2mT07QOY9XeCP5/l7/98AfLr6U4/Zaw+sRQ1VzN4WaCXG44+bh+32fv0V+rndPDzaQywEY52Cut8NIFAQ5l0zFBPiR9VaZzgGuw0lSCzu+y5BmBAlJtSfESFKinwkQ2AFYQUF8M030CvYhZB//Mc5+c7Sd5i8drJr1u4/APh247dBt3vkiHn2O1q/e+1QWfkzr1IltHzPPef5OiMjtOWKWhNWVFITJkSZIVdHitJGgrAQTJzoOUJE0P/NnGoMv2K48+Vt39zmtmwx/3Rbt7ZPd19vafyluegi89ysmSvNXznt3qMxY0LbTllrjvz3v4u3vBAiZKXxp1Gc3CQIC0Hjxua5MP+3z1z0jMfrtxa9RW5+LhqzEkURfw0CDZdQWh05AotMXzmPqwD9vaHhqB0KteYs0DatJtSSuDpSlBlyyIUQ4SJBWAgefdQ8h9o33JIyyDVExDO/PsNzvz7H/vT9AMQo+7e+oAAOH/ZM8/jRT0qy31hp/mfYutU+/b33zHNaGkya5Eq362cV7v3LyYGqVWHyZP/rDzY4XLjLdcUVnhcuCCHCSmrCRGkjQVgIqlY1z6GOkmCpU6UOU2+e6nz9ztJ3GPbHMMB/EPb661Cnjv9Yy8PKla7p0t4x387mzeZ5wAC46y7480/zuiSCsAMHTFQ9aJD/bYay7XCW67ff4P77w7c+IYQH6ZgvShv5SIbAatmyLkYszP9un9Z9mHj9RJ90q4P+4j2LoaprqInvvjPPe/eGsPKsLNd0aa4JC9bsmJxsnq03ujhBWKj5rG1Yv8pFff9K8/suhPAgQVhoUk+UwW4vZVREP5JKqauUUpuUUluVUs/ZzL9dKbXG8VislGoXyfIUlfU/u2BB0Zbvn9ifR89/1CPNGsLiovEXwb1d4MnG3PL1LT7b9HH++b5pBQVw7JhvelEDhKws0wZrt85wOnAApk/37UxfEjVh1jZiY/1vMxTu5crLK16ZhBARJc2Rwf2w+QdqDq/Jwt0Lo12Uk0LEgjClVCwwGugFtAL6KqVaeWXbAXTTWrcFXgHGRqo8xRHKLYuCee/q9xjUxbOzfl6B40/7lO2QkMRX678KvqIRI3zTXnoJ3nij+IW0TJgAo0fDkCHhWZ+/AOqvv+Dmm13BnpUvP983b6ij5Ra1JsxumxbrooJg25s+3Xd+RgYMHx54/aJMkcrPskuCsOB+3/E7AH8m/RnlkpwcIlkT1gnYqrXerrXOAaYA17tn0Fov1lpbPa2WAo0ohWrVsk9fuNB8qf/+O7T13JwwHD5y9eN6evbThS9Mw4a+aV95BW/WeBr79hV+/eAKGEoqcDh0yDwHqgn78MPwbtM7CAtUE2bdyNuO+z+yNaKvuxdfNGOeTZ5MSORfQoiIka9XcNYV/KJkRDIIawjscXud5Ejz517g5wiWp8iuu848/9//eaZ/+615/vXX0NaTkwMkt6f9H9sBGPnnSJ88SgH11vDG+gfQhNgs593RwQpY9uzxzRsNwZr6rKAxUBAWLnl5MHasq2YtlJqwQNyPR79+vsfCGlLEvf+eECIqpE9Y+B1IP8D8nfOjXYwyK5IfSbtzDtsQWyl1KSYIe9bP/AeUUiuUUitSUlLsskRUlSrmCsn69Yu3Huv/uvKJ5jx+VMOX3/vkyY9Ng4faMTPpY/5qdgf0uckzQ0KC74q9T+/c7ylZGoQaVIUjCAvWVvTJJ/Dgg/D22+a1dYWm97gg7pfCnnFG0bdn3azOGo4jGGnrEqJIcvJzeOznx0jJ8P8fITVhwRV2DMsu47vQfVL3iJTlZBDJICwJaOz2uhHg0z6mlGoLfAJcr7U+7D0fQGs9VmvdUWvdsW7duhEpbDAZGbBqVXjWpZS5/RGbr2V136PwjyvQWtHD1QFt7ymTodU39F6tGLZgGGnZacw8sIDV9WByGzhc2W2F7op7d9pIdYIPJj/fjM3VokV4t+8uPd0879jhmb5kiefrV191TXfo4H99wd4ra8DXtWtDK1+4PmRCnGRmbJjBe8ve48lfnvSbR2rCwm/70e3RLkKZFsmP5HKghVKquVIqHrgV+M49g1KqCfANcKfWenMEyxIWf/xhnx7q2ZXd/3XNSjXhq69h+CHOqOW/xuWF31+gxhs1uGHqDSQ+BLfdDH3+D56Z8wwpFYvWlLYndQ9/JZv+Tr9s/YW1B7wCBa8dm7V1Frn5uYXfUKhB2P79sGZN4Qdkc+cvKMrLg5EjXfsUbET9d95xTf/rX9CzZ+G2Fwqp9SqTTqbD9k/KP8S+HFsm/mjzdb7HsyUjJ4NjJ44BgX+rF+5eyNKkpZEqXtSl56Sz69iuoPkC9Qk7kXeC7LzscBbrpBexIExrnQc8CvwCbAC+0lqvV0oNUEoNcGR7EagNfKCUWqWUKtfDhVs/3rY/BFm12frYVi6YlQdzXw5pfXNPh7cWv8WtFybx756QZ/UxVzDqAkiLd8v8wQc+w1s0GdGEDmNNLc9VX1xF2zFt/W5rwa4F9PqiF//9/b8hlc1DqP9aeXlQoULh1+9u/3779GuugSeeMFcqAmQX4odEa5g92/88b9nZrruuB/rVP5n+zUVYXfX5VTz0w0MR386EvydQoAuY/o/ryt/8gnzeWvQWmbmFvIVIISzZs4QD6a5uFVprZmyYEfAk0Lovr3dz2hmjzqDWcHN1lfPrePZ3jPtrnEe+SyZcQudxncNQehO8WoFfNAz+bTDxr8R7pHWb2I1mI5t5pG08tNHnfQik+uvVqf1m7XAUUThEtHJWa/2T1vosrfUZWuthjrQxWusxjun7tNa1tNaJjkfHSJYn3Nz/Q3/8EY4f982TnQ3jxoV+f21FLCz4L4t7aHqs2w25lf1ndphbL4N3O0Pci3DNbfBTC3i8FzzdA5btXWYyPfIIO7au4N+//JvsvOxC/0AczDgIwNYjnrcgOuu9s7jq86sCL1yYmrBQLzW1U6sWtPIeBQVTs2YFUVafwjVrCr/+n37yTbMLpJo0gcqO43bkiOe8OXNcFwH4e19ycgp/j6yTWFrayRfP/rLtF8asDPGm9sVgVysyZd0Unvn1Gf47twgnZDYycjJYvne5R1qX8V2cJ4hg9vfGr25kyLwhQdenvH5gD2S4gjlnc2Tf67nv+/uCrmv2ttk+QwetPbCWAu367q45sIZvNnzjkaf1B63pNrFb0PVHymsLXyO3wDNgtVo93LX9sK3f98H7fQQzrFJGbiHvyysCkhbyEN1xB8TH28/bvRuuvdbk8fbyy3Dffea+1YX9o6iS1xiGZfJNW03mfzLpeYafJjE3P50F/7rNTI/tCBd8cgHPzHmGec3gur7w7tJ3Sfwo0XlmaGd13l7yYuC4ymH0stFUfLWi80dHKUWBLuC1P17jcOZhthzZwi/bfqFAF9D2w7YeZ8xOoe74Sy/BQ+bsvukT8HJhf8O8B5e1tjtlSiFX5Mc11wS5safDwYP2y//4I/ToAW+9ZV77C8Lat3fdK0sElJRkxvGzrrMQkeH+h2z9CaflhHBv1RD0nd6XTp908jkx3Jvmum2IdRK457j/K75DGVqhsB3ze37e02MQ7VX7V9F2TFuGLRjmTGs3ph03fXWTz7JrDhThRK8IDmceRg1V/Lb9t0Iv6x2oudMn25lNlEgQFqJatfyPF2r19ba7T7V1oWJqqmdzZLDPt/f8ynGVmXXHLPRLmryh8PVUuCCU+0timiwvvQvW1TOvNx7y7LifkeM6s9m4+y8S096k3tOQcMpHPPrzo+Tk5zibHuav2M/C3QsZPHcwD/7woMc61h5cy50z7mT+zvkM/m0wOfk5pubMT7Cxoyak+wtsa8JLl5rpzbUhqwJ89cMb3DPzntB2Glzbffjh0JexkVUBfj7T8cJ7VHyvA/VhR5hxjp8VWeO2OTrrp2WlssmuZn/9+iKX9WSzc6d5tm4BKsoeqx9WoL5G7ieBlsV7FnPXt3c5gwV/zZHuint15J5UEwT+uTd6A5lOWTcFNVQ5g9YV+0wvnuGLhhd5nZEIuP5M+pN9aUUcq7IEJB1PIr8g+oNoSxAWojp1/M8L1NcrYD+wAOmBxGq4aQMs/QRuH/IZDNG8/9bNTJkGZxwJvrw392BqzyETIByp4pnneLZpa02pspCs3CyPNDAdNsHcmLz7pO68tvA1Bv40kBbvtWDBsdUMvgyevQISB7jWefoTcOWdgcuWVQHOHgj9e8MtK59nwqoJtvmsM2UPYRpv7Imr4Oo7YHU9+CN5KZd/ejm5+bkcyTrCAa/tPnwt3Hirmf5+3XSmOVpH3+sES7I9o/Srpl3HOQPDUsSI2HhoI5WHVS7VnbKjcacou/+rQ5mHOJR5qOQLE6LsvGwavN2A7zeZYXFST6TyxsI3PJrV7Nj9OYf7D9uqwbJr/vLepnuA1eOzHkxaPYn0nHSPvIHWU9wgLNC6S8qbi94EXFclWmUqziCr7p8D53tdyH31/lxcOO5CWo226R5SCJ+v+ZxNhzYVax12ktOSafxuY/7z23/Cvu7CkiAsRO1s7mrp/VsUsK+X17zC/I6FkjchoyK3rIeto0APgQFDPoAhBQw5ZQuz75jNE0ugmp8TzS/WfuGc7vF9H9s8j8963Dlt1YrN2T7HmWY1Ubj3F5j897cAdNvwDK91hTcvhtX14daboa2jT/HSxtDjTnjwWph5tgm68tw+lVZN2ZzTXWn5BfmoocrZP2T6P9Op9796LGwCG+tAZhx83B601wCsT/eA78+ClCpwvKIZ5mOp2z0aFjWG9W4joIy6AMZ2gE2OAPxoZbj9t0eYu2Mue9P2UufNOtT/3HwwljbyHQTvuuk308fxdj52NXQ5/Ca5MfB49YUcSD/A4r2mBqA4f2laa2cAHExeQZ7rVlle9qXtY9iCYR4/pJNWTeJE3gmmrPNszp2/cz7P//q8R1pOfg73zLyH3am7C7kHxrp1rjF7LSOWjkANVc6g305hhxwY/Ntgn/2xk5OfgxqqnH94wdR9qy513zIfnuS0ZIbMG1KoYOXVBa8ycdXEkPN7y8rN4q5v72J/uv2FKbtTd5OcnswTvzwBwJO/PMnzvz3PT1tMP8cJf08gZmgMOfme1f3OAMmmdqmw40n5E0oNllWOGBXjk+b9OtB6wjVERaCAJxK1Sgt3L3SeaDprBR37aT0XZ7t2+1PY42u3jtTs4t0I/M4Zd3Luh+cWax12UjJN3+Cfttr08y1hEoSF6Npr/c+zmikD1YS5T7vn814mXCdaGgUoTlXNufL0K3j3F0h7HXTsK+ghUJD7H7JfgS+/hs97f8712020c0PzXkHXfeNXN/qkXf7p5T5padq+b9TUNrC2nuv1nDNM/7Ub+kKVF8wFBpbMOPN8zO36hCNZprrv1QWvUv316tw87WbABEwtH4Vaz8ID18GLC4Zw8fiL+eJceL8TvN0FrrsNTn0Gzn7UDPPR+T7oMq4LvW6Hi++FNo/AV+u/4qMO5uKGB/9lrjYFiNGuPhRxMXHOH51fzjDreb9T0LeOWWfCqISNPPqz64bu+TEmEPP5CQuhJu+txW9ReVhlDmce5pThp/gERwBfrv2SjJwM6v+vPs1HNgdM3xatNWNWjGFp0lJum34bL/z+AqsPrCYnP4cTeSecZ8LetSXdJ3XnjUXmXqU7j+1k/cH1/Lr9VyasmsCAHwZwIu8E7yx5h7yCPJLTktl7fC/BnHtBCk0v8RwDxmpeOXrCfsiSnPwc8nSI9xR1eG3ha/Sd3te5X/5qgqxaXqsMdd6sQ9sP/V897O7OGXcydP5Q10UxfmxI2cC2I6bm+b+//5e7Z94d0vrtTF0/lUmrJ/Hsr7bjXTv30wpirKYsqwlw0JxBaDSpJzz/NK0/9kDBT3H5BFMBat/cAwPvfHZNlt7SwtONLaBI3PbnkgmXcNH4izzWbx2TUGoSgwlWIxqKcASfWmtW7V/lkRao35q75LRkxqwI7WKVcASu4SJBWIhiYz1fFxS4Lrj78kvzHGpNWCjfFa1d+YrzOVEDH/E8/XOsVBVo4vOh7zq4ve3tfPtpDnoIzKg7EL3tTvQQU6OWPxSWxD3ErNtnwfbL4MjpDL5ksM92ItVkNbe5b9pHKz8CzHhA7k0RVl+sHMcoF68uGc6iPYu44yYYeLXnOvZXd00vSVrCLLfxYW/5+hYG/Mv12hrq42BVyHFcIu9eY7DtFPO87lT7fXA/fLmOz5H7pfZ5MfDQtRAzBP6pi7MJ0+5Kj+S0ZD5b/RmP//w4aqhi0upJgKnJOnriqDM4svyZ9Ce3f3M7A38eyOGswyQdT+K7Td9x3kfn8dmaz3jox4foPK6z84y1QBfQ4r0WVB5W2fkDFatModVQxb0z73Wuu0AX0Hxkc9p82Mb1o4Zm+MLhPDX7KSaumkiDdxrQ6N1G5BfkM3ntZP8/9nd3g3u6orVm4qqJ/J38t7PWLlbFkpWb5ew7lJOfQ4EuoMHbDbhmgecFJlrrkH9YTxl+Cq0/aE16Tjo3TLmBPal7mLdzHuP/Hu+xPoDDWYdZe9BrHL2urxD7stcPA57N9HasfiitPmjFme+dGTCvVYZg++QdZPmsw6YmCXz/uL1f2wU2oTZXZeVm+Vw1aFs2x/qsbdl9RuwCG+/8odSo+evXG6pQthGufkZD5g1hadJS5zatK9O9j3UoZQomLAFUMYLP9QfXo7Xm8zWfc95H5zFz48xCl+mGqTfw0I8POfvtefv6n69RQ1Wp66cmQVgRdezo23861JqwQNxry/wFYdtpTi4V+B9PkYNnz/aD1OVL+jpqwmxMnWqeR/retxKgz9VpTP/M1aQYo+FC1ZieZ/aET3+DUdt49bJX0S9pZ6CmX9LOR9rzaaQ+lwqz3oGpX9OuXjv+W8+0yV0SfJxAH/fc4Jvmb6yy9IqFX38oVp1mnm+5GY5km1qZEUtHOOfnOP6H//FzM4dct/9pK6/7xRFrT4WPHIOztH4E+vQxzbU36MnM2zmPvII89qXtQw1VNHinAf2+7ceoZaMA+z+sE3kneH/Z+xzMOMiWI1sAz6vK/kn5BzA/fBbrjyNGxTibFK0ax9HLRzvzjV/lClDcmzbd/xismqu0bFe1w9iVY7ntm9v4eOXH9m9S3Q3mvSrI5e6Zd9N+bHvn+gt0gTNY3JO6h4qvVqT/t/05nHWYEwWeQ3nEvBzjU1ObnZfNXd/e5TNQZWp2KhsPbWT6P9OZuWkmg+cO5tJJl3Lvd/c6g+TYGN8gq/kH9eDaAXDZi7a1aXYB0f70/Uz/ZzqLdi+iwisV+GOXn5GfbTR6txEt3jNnCd7NhRb3P2KtNS/MfYF/Uv7h3SXv8tQvT/n8UftryivQBaRlp9FydEuW713urIWwauzc8+5KDfyFfmr2U9z01U0s2bOEv5P/5petvzjnpeekc/9393PsxDHn+qxnuyZza7+/XPelbZndXwcKDs8MHvMGZG0jUBeAwtQq5RXk8eaiN23HXBs6fyidx3X2GXzWOzAOR02Y+za8j0eo/AU/7rYe2cqOozs4kH4ANVQxbf00ftj8A20+bMOXa790XlG66fAmn/12l5ufS35BPrn5uUz/Zzpaa+e4ct41Z9uObCMnP4exK8cCsO7gOr8nJdFQzJExTy5PPeW6FN5uOKtgNWFWMLVuHcTFBd6WvyBs7ly4nO20ZyV/4Xs7neuZyVI68y/HzQmU9xfJun2O+zhUffs6J6fRh2n08R/EBVEtvpqZWGpuHbJqwE0wYwYvP/RVgKVctUW5sbA7AbbVggVNodNe+PEs01/srlWmX1nfNn2ZvG5ykcpXHAVu31crCAJ40jFM2sKmrvlj3Ea8e8dt/MeVjoBu02FXZ9N3bcaHXFvPPGZOupTE+ol+fyysYM69f947S95h8NzBDPzZ1evf/XJ5K8A4nOUabsNaj/tdE3am7gTMUAF2g2RaQRq4/jh/2/4bp9c0Hfjcf8StINDqiwHw1qK3qBxXmcFzXTWr7tuxmsxSs1OdV6NZtUyfr/ncpzyWbzd+y7Yj2zh24hgdP+5I73N6M2PjDL8d5+3+xKw/fbtlUjIPQsePfPbdYv15xMaYGrxft//K4LmDWXtwLU93fhqA33b4H05Aa41GU/ONmvRp3cd55v75ms+5c8adbB3oexm29cccq2I5nHWYYX8MY9LqSSQdN5dQ33Oeuap4x7Edzm3YLZ9fkM+SpCVsPLSRwXMHO4/x+8vf572rzf1PrbRZW2fZln/JniX8ufdPZ+34sRPHuPpLUxWtX9Lk5ucyetloPvn7E06teqrzOC/bu4zrzr7ONgg7nGk+q+7Bj3WMrPzOWstM27vfobUmJiY8/T3sjt/hzMPUrlI7YPDg7fM1n/Psr89yNOsor1/xujPdvTbN+7vnE1A7Xs/e5jugtFUm7+W9A7b5O+fTq4XpimK1Lryy4BX+3fnfIe/L7tTdNK9l03ThxjqZ+K2fef9GLx/NNS2uATzHMYtRMT6fg8zcTF7/43Ve6PoClYZVonuz7lzU+CKG/TGMH2/70XZ7x04c48z3zuSexHucxyVGxYTUdF1SJAgrhNatA8/Psuk/7P5bV93RBJaSAg0amGnvz4BVXe6vOXLdOvPsHYBZwVYSpqe5VUPmE4TZCTaOlndV3PDh8MwzwddrqVYtaBbrbYjPhzOPmEdPx8n39ZtgrONe58N/BfSXfHnTl14r8HwjNRATmw1oDlasxL7qkBVnOt83Owbfnw11M0w/sduqdeaLjCW0OAxbasPpR2D7Ka51tTngGt4jVA+59SF8/grX9P8u8s07OUi/U+8+Enaemv2Uc9o9qLG4Xz06fYMZy23c366Rsq2zx37f9nOmuTer2XWw/XKt6xhYF2Rk52eTlJbkUyarc/1fyX+xeM9isnKzeOZX38+QT5MfMHLpSOcfrr/+YcSnobXrc+bezDdj4wzA/w+u9WPvPkK7vxonO95/ktbx+nbjt3yZ+yVvL3ENYmaNrRUX4/8srPO4zmw+vJm0nDSPY2SdeFg1mXb7MGvbLF7q/pJPuaxA09qvRXsWAaZG98aWNzoDoQMZB1zNwDGxtgGF3VASqSdSWZ+ynv/+/l/m7pgLYDuu4YwNM7jxqxu5o+0dHuUC03R+3dnX2W4zUO3SjI0zeKDDA87P8Pebv7fNV6ALiLGp2SyMQOXIyssKmseitWb2ttnOgDY9J51tR7axP30/HRp08Oyu4BWMbDhkao0/X/M5r1/xesAaq61HtvoEYesOruPcep4/Ou6fd+tEqbADets1WfvjPGmIifWolbKOoV0Q9tait3j1j1ed+zNv5zwaVm8ImJMl9xOR+Tvn031SdxbctQCAn7f+zNl1znau28pbUmO5BSJBWCH06QP3BBimanOQu19WqmSe27aFQ36uZv/LcTIwZYp9EBbqqAtWTVZIQVhhPfccXHxx6PlPOSV4njBTAPkmEK2baR4AFzrGVrvJ/I7x1hzg43v4fNAS71X453ZA9M6dFJzeHAXsSoDqOeYKy8p5kD3lC959/3b6roU19eDNrjF8Pq3AecXkf9o/zmt/jaTJMei2Cz5rBw2Ow74atlsNm9UHVoeUz/0+etYZrDv3IOv2b253Tn+78VufvCP+HAGYP0wrKLJjd9sY95HhL5lwie9CVz8CnT4gJsjdvn7Y/INtutUU9Ms2V3OZv6sM7QIQ6wpD8Pwze2/Ze3Q4zfNkaeexnQB89Y+rZnjaNM/1+RuDyqodcQ9STuSdoFKFSs59SDqe5Jx2HyneO1C0avj+2O3ZLDr+7/Fccbo5a9h2ZBsVK/i28Wfne74Hx7OPU3N4TZ98VjmS05OdadZ+WxctuNdsBWqODFS7ZB2rYIFzvs73CcKs9y9Uodw30S4Iyy/I59iJY8zcNJMTeSdYd3AdH674kLpVTB+GuNg4jxOHzo1c3wN/HdO3HTVnqYXtO2X3/rqXOdDVyKEKVhvo3v3BfdoqR1p2mk85rRM9u6ZghXLOz8rLcp68WCcEsTGxHtspDR3yLRKEFUJxBjH3Pgm3BnHN9XPhx5EjnkFYaiq0aQMX2dSkWF5iCHtp6LndEIKwM9jKg3zEM7xln8HufkxWlRyYm2EHenOK8YHPoApjGMCTvEuM+778/rsZ9PS+4LceCcrfQQiBwozbBtD8mHnuZnWVqXw2lzi64P3fP/BK/Vvgn8noIZj3ZP9+hl3n6pv3qV1sEuS9s35MMnIziFWxZORmUD2+OsnpycSqWLYd3UZadhoHMg7Q4pQWDJ47mEc7PcrHf31MnSp1qFyhMpNWT+Ljf33M/d/fT7t67TjjlDP4ZsM3PNzxYT5Y8QEAvc7s5ayJaX9ae59boCRUTHDWltWsVLNk75vX6YNCL6KGur6Q7sOvWLpO7Oqc7j6xu3O60jDfP+w+X7uGdanxuiuCPnbimE+zlRXouZ+B9/n6ZnBcjGE38rr3sr2n9namDZo9iHvb3+vRHO0eFFp+3vqzczo5LdljnvvFFu8te4+LGpsfmS1HttCsZjOfdXkHIv46OlsB3r3f3euTd/Nhc8b67tJ3nfPeXvI2r13+mm2Q4G9oFYCX5r3Ei91eDBogLdi1gJiYKzzSVu9fzQWNLvBIS05L5rTqp/lsv0JMBWdtl52jWUdpVKORbTB45WdX8vvO333SrVqnT/76xCN9SZLrpNDf/TKn/WOi90BXD3659kuf/Vt7cC3nnXaeR5r73QlCqQXOL8j36S/54YoP6drUfG+8j9cPm3/gX5NdVztZQdqv2391Bpwf//UxbeuZK5CHzB/CI50e8ViHd7Oze9rBjIPOWsWRS0c6063npONJzqb5cX+P4/ELfL/z0SJBWBjVqmVuU+iP+/+pNYTV9/Y154Bn4LZkiblFi9Wv3ttKOvAurvb7wvTp2s4ZPMub/oOw4cPhDc+r7vjd7Qflzz/hssv8b6AYQdhghjGSJ2hEErfg1q/M2t7dd/teulpY1k29iyLQvi3zGqLA+9KsMAwmazUBWH3xKseZsTysP8/GCY098i+8ZyEAt7a51Zk28YaJANzX3jegHX2Nq1O+TxNwISQkmFg+v6DA2ZclryCP7PxsM3xG0zSouZN5v8VRu0pt5u+cz3mnncfSpKXUqVKHJglN+GjlR3Rp1IXU7FR+2PwD/9fq/3h6ztOw+N+Q0opFM1vyn9/+w/xd87n3vHsZ9/c4qsRVse30fP3Z1zNz00wAepzRw6c/TccGHZ0jkQe72tGddy1RSFq5bvUVytWE7t5f/j7vL3/fI+3JX570yefeRN3gnQYe89wvtgC4dbrrs2HV3AHU/199CnSBR7++00ee7uxnFooFuxb4nZeTn8O/Jv+LDSkbnGlnv382XRp38RhD7b7v7vO5EnDQ7EH8b8n/nK8f//lxalSs4RGgXPnZlbSv+QZ0cf1eXDjuQkZeNdKj9qfBOw0Yd53nTa3jXolj8k2TnVdmgxmf0L0Gqe2Ytvx8+88eV4pPXjuZ2lVq2wZg7gLdAmrdQdcJr/c4fHuP7/V4v45mHfUYq3HUslG8ccUbHjVT/b/tT792/TzWM/DngTxy/iPk5OeENOjwRys/4uHzPe9EMmXdFCbfZE7U3L9zWmuGzh/qkdc9SDuaZf40j544ajsAuMXqUvGfua4BVq1a46fnPO1MG7NyjLM2d+LqiT5l/3zN5wzoMMAnPVpUaaqWC0XHjh31ihUrorb9P/6Arl3t5zVtCrsctSDW23rXXTBpEkyYAImJcN55ZuDX1Y4WoREj4IknXMtYgdftt5vRwKdOhcmTTYB3VYD7ZD/GSEbhiu4v51d+4wo+4V7uYxwPMoYxPGS7rFVbps21Vc5pD1q7auZQ0KULLF5sEubMgSs8zzA9mlKXLYMLPM/GQnUXE5jEXYznbu5momul1gZycsxVDjb9ffzuS3FpDb17m9q/l1+GM86wzzdnDlx5pev1gw/CRx+51rF3LzRqZL+s+7bKgWDDrRR1OJYlS8xHsSjLFlVamrlfZSjbdP991WgycjKoGl+VE3knqFyhMjE1DoDK59jBauxK3UVCxQQ0muS0ZKrFV2PtwbW0OKUFv+/8na5Nu7Ji3wqqxFWhQkwF+n/bn8k3TaZibEXiY+N5d+m7ZOVl8cqlr/Dzlp+ZtW0W9553L0/+8iQfXP0BX677koW7F/L4BY9z5ilnMvDngdStUpeUzBQe6/QYo5aNomvTrlzS5BKG/WHujfj4BY8z8k9TW3t/+/uJVbFUrFDRmda3TV8qVqhoO9BsrUq1fPrwNaze0KPWBaBqXFVn4HBe/fOoUbEG83fNByA+Np761eo7rxAGU9NauUJlj2bOyhUqo/EcuDhGxVAhpkKh+vedLBTKNAUGaTasFl+N+Nh48gvyfW7e3aB6A5+grXnN5uQV5JGSmRLyINL+NK/ZvFABfmG0rtua9Snm6vDRV4/2CSjDTSm1Umvd0W6e1IQV0iU2XVIsVgBmyc933cvZ/erI7SEMqZWTY5ogAdasge7dC11Us11HIPIRA6hINi8xlFM4Sg9+YR1t2OfWfGn3f7KKduyjAVd7z7ACMICffvIJwjzY/FOlUIcU6tKKDTYLFEKYbk1UKHl58O23ZnroUP/5qph7P43jHm5iOjU/+shzfjkJsArj8GGobXe/zCKy62u/bZtpzj///PBtp6jcOysrFNUrmqtzqsQ57guWXh+AhErQtpJrMFirJtPqQH1+Q7MzFza60JnHuzbjmrPMVWZ79sCsXy9j9RtvERMDT1z4BAAPne95EvZop0c9Xo/s5Woaf/WyV53TI64a4bNf3mkTrp/gk6e0yc7L5v4H8/ns8wLQivzsyhzPPk5+QT6V4ypTqUIljmcfJy07zVnDVbNSTY5kHSEzN5MYFUPluMqkZaexYNcCujTuQlxsHJUqVOLT1Z/S4bQOnFr1VCrEVKBAFzBq2SjuO+8+KlaoSNW4qmw4tIFdx3bRqm4rth3dRp0qdcytz9IPsDJ5Jbedextzts+hc6POrN6/muoVq/P2krcZe+1Y5u2ax5drv+TDaz6kenx17phxB21ObcPATgOJVbE88csTpOek83aPt6keX528gjwe/ulhKleozIvdXiRWxbIrdRejl4+m/WntuabFNeQX5FMhpgIvLzCdKV/q9hLxsfFUjK3orFm6s+2dxKpYYmNiqRBTgRX7VjB/13zqV6tPrzN7ER8bT0ZuBp+u/hSAi5pcRIWYCsTFxPHxX2Y4mu7NupNQMcFZ8wzmc2z1Oe11Zi9nc3mf1n34ar1p8Ti9lquWtVp8NfIK8nwCO3+B2tm1z/a4Ah3wqBV3b1b21/+zxFgDAZaVR4cOHXS0mX/PwA+ttX7iCdfrsWO1njTJN9+IEZ7LuM+LiXFNz54deHuPMcLj9eXM0aD1J9zjkf4AY7QG1zbdpvNRzuklXKB/pqdt2fzusM17pLXWeskSn/yncMj/utwe/ZmgQesJ9Hel//KLa/qLL/welIDlLc7jr79c01u3+s/XsKFeQXsNWt/ENN/3a/fu0D5I5YC1Ozt2BJ5fWO4freKuK1THj4dvG3brycrSOjOz6Ovs3t2sc9Gi4pWtPLr77nL31RLFUFBQoHPyciK+HWCF1vYxjdSEFcHRo6Z5MJivv3ZNP/CAfR67YS0shRnCxLvJzd/Vkfn47z9V4DZ2b2eW+s0XtCw6WAIcIYTqkJYt0Rts3oSebpe+33473HZb4PJAeBsk27d3W7nvvjnt3UsmZtyc/dS3KViAZcupcN27rzyrXdv8LhS1ktd9mBvhqRQMCyVKEaUUcbFBBu2MMPlJLIKaNYPn0Tq0H9E3A9wfOD9wc73n9kIMwv7kApZj2zQdtr5TPvtd1H8Dtw73Aa/yDLL+Iu/Xja6R1zUwnrvJwesLG2TbxSk3APujXFUeZuEOwkrrn2p6uml6LYrMzOIFUBJ8CVF2SBBWRMF+6NLSQgvC0tOD5wlle/5quHK9goZ1nEsnlrvW6zavoAgfh100YSdNPdK8y5qXU8BxHCPVVgp9TB5atAgtgPrJ95J8j/IUNQhr6OovN43/417G8zIveuYJ8R/PtgyhLHu5743RS5tXXrG/g4SdcAdN0QjC3A+bv0N41llQp07JlMefYO/NtGmwe3fgPO5uu81/jb4QomgkCCuGlBT/8zIyQgvCQh2iKlitmHcQZo2c/yTvBlxuHW2c04UJwgpQLKYzzdhFc3Y608eMgdNP98x728tnk8BxUqnBDT0yWcKFhOT66+EqcyuNgIGUo7boO/5FDnFcylxexNVh3t+yGgLVU8G1rmHvj1ETgIN43aXb5l84lwpkYoaKCFgTFqgt2rJzZ/A8UaQ1vPgidPC9g5at0lpzFYobboDPve6WdOCAbVaSk+3TS0KoNWF9+sCFF5qvz0MPBb+59eTJ8LGfW3+WFd6/TUJEmwRhxVCnDqxcaT/vhRfMRXSFEShoCxaEfcsNHq+tICyTwCPMtsV1m5hAgY73D/s7/JuLWOyT76GHzNVZ7qb9bkaFrkkqM79T3ILNYGe33uqbduaZ6Dpm2RgCvDlxcfxOd67nO/7LK8zjUl5xq7Gy26+VtOc6vvMcANZbc9/7oPms65xzfPJ0Zx5V8Rybyva9tW5EGkiQ9jutIbsIQ1MVV0GBGYHD+sy6fz62bPEfCJSWIOyee+Dfod8WD4CZM+HOOz3TTvMc05MNG2D+fN9l09LsxzwOl6efhqVe3TiVgo0boVcv33jfOm7JyfD44+bk6bvvIle+wsj0HdYtbGy+rkJElQRhxdS+vbkc3tv48fbpgQT68QkWhB3E8+aGWVQp3MYJXBPmXmP3Dk8yiP/5zRuMe+3QIWqj0Ly34xqYPp01lTsx8f9+5Eiv2+Gii5x/5rEEeAP693fWUO2kmc9s7wAonxg6spIf+JdPXg8xMeyhER8yIPidB1q2dE4uxve2BrbNxd7VJe3a2ZYhkMGDTQuv95/s2WdDkyYBF/Xr2DEzzEMg770HPXrA9Ome6b/+aprivGuMLO7BWV6eKf+xY0UrJxQ9qJswAd4NXElcJK1a2Q8nU7euGbDW8uuvMGNG4dffs6dpJT90CJ580nwvCwpMPN/Z645PWsNjj8GsWbBggRmP0LpYyP33xDpZLIkAOTsbTgQYPmrFCjP83syZ/vO4mz/ftEhMmgQ33xyeMkbDihWwz/7GA6KckyAsDGrVMj94w4aZWwsVVaB7Txamk35RVcd/BzX3H8WneMdnftOmZvD6UKjTXFcKLu5tgrl3Nl4DN95Iu6w/uXva1bT++3MWL3btdxrVeYcn2U1jfqEHCs12XDVVVgCpzvO8HQf4NiFaNzd3V4Dig8pPcQK3e+XFxNCTX3iYDznsuJrTX23hsttHspkWZOHZ580KvpZhM1it+0F94w1nx6oPeIjefMNY7qdNuuc9Lfv2NWPTWqyhxzIyPLKxebNvjWSozj8fzjwzcJ6tW82z99h4ax0Vq/5qiH9zu4vPjBnw2mswaFDRygklV7MWqInvyBFzAvVlgBsKeNdWXnmlx3UfAeXmmqbQVatg9mzzZ/3UU2ag5+nTYeDA4OVWCkaOhP/7P/Pa/aNn1YrFxJhh73r3ptBWrvRtqty8GUY7briQmWmarU85BapVg7lz4R/f+5A7bzLxyy++8+x0727GbrzrLt8TAjAnE+5NxtEYVnDBAjMucyDnny+1dCWpoMAM4zhmTPC8Eedv7IrS+igN44QFk5en9ZdfFm7oKdC6QQPP161auaY//7zw6ysND61Dz5uYWLh1f8mteg6Xa9D6fj7ym+9HenkkrKW1x/yb+UpXIMf5eggv6l+5TB9ats2Z9jrPatD6ClwDtuUSq/dR32N8NY99B/073TxeOx8FBbrgssv1cappDbrgYIqeOFHrLCravodz5mj9zz+utFtu0To1Vev4ePP68GHPz6D7soXlb9mnn9b6jz/M9COPmDxvvOGZ/513zPRjj9mvc8gQV5r1me7bt+jlXbnSd1m3tzikfSwoCJw3N1frI0dcy6Smmufrrgv+Gf3tN8/fgldftc9XUKD1woVat2jhW35rWLp27TyPP2j9yiue6+nRwzW9eLHWl1/uu60VKzxfW/vxzTeutPffN8/Vq2v96KO+75m/9/PXX7VesEDrjz7SOi7OtR8vvOD/98HdBx+Y9D59tM7O9p2/ZYvWl12m9emna/3kk4HXd+CASVNK69WrtR43TuvJk115s7L8H/PieO01s/4JE8wwhqB1zZrm+ZFH7JcJ5fOfmqr1oUPFK9u2bVrXrm0+z5GwbZvWe/d6ph04oHVKim/e++83x7u48vPN7056eujLZGaa9zs2tvjbDwUBxgmzTSzNj7IQhLk7cMD/D29hHj17Fn8d0XiMGxf9MrzCYL2QLvoAdcOyPmsA2WCPvZymP2CA8/U0btK7aaTvZpxHvln0cE5fwnyf9RQU2K//lFNc0++/r3VysvkBTE93pVvS082PlbvkZK1//NFM5+WZYCMlxXMb1uCqy5d7rvOuu8x0nz6e6S++6Ln8+PFaL1vmep2Q4Nr+p5+aNCugAFMOO6++av50tdZ67VpXgPL33777ar3es8d3PQUFWmdkeC4DWl90kZkeP17rkSPNdHa21vPmaX377Z77dOyYeX77bVfQWdzH9u2+adYx++0389r7JA1MUOtvnf5OBPv3t0/v2tU17b2tXbtc0336aH3ffZ7va6B9y8nR+vHH7ed5s4Iw0Lp3b5N24oRrvvvnze6Rl2ce334b/D1v08b+s1YU2dlmP7X2HGQ7lH12f/8saWlar1rlmadaNf/LW/LzzWfi4EH7+eedZ9Zx6qmutCVLzPH1J9hJiju7fXRPy893DUTsndf9OHtbulTrzZvN8p984hlAf/21Wc/AgaGVUWutjx41y0gQVoRHWQvC7KSna/3nn66z1BtuCP6DIY+T9zFxYtGX1dr8KYHWN95oak5yc7XeuFHrKlVMelycq9bC+zFypNb/+Y9nmr8/Qq21vvnm0Mqktfkx9Z6XkWHmFRSYYGfYMFfNE7huljB8uAkYA42Yv2aNCZTA9Sfpvb38fNe0e6BwwQWm5s+u/Fat2IgRZnvuNUjhfFxxRWTW26xZ+NbVpo3WTZsGz+cv8ANTAz5unDnO55/vOc/9GDRpEnw7desWrvx2CgrMd8SSnW2+B1lZpka6c2dTI7xli/ke7d2rdY0aWteq5XsSEmybx46Zz6k1b9Eiz7xz5pjPufuJ2KZNpizWScbIkSZQX73aVRN63XVajxljTnAKCsyJUEGB57HPzdX65ZfNtFLmu6W1uQnI2rXmxEtrE1Sed545aTt61FQsZGZqPXeu1o0amdq5tDTzsNadkqL1vn2e38cGDVy/M2vXer4ff/xhpufONa+XLjXvTX6+1q+/7so7ZYp5fukl8z/6wANa/+9/Ju2OO+yPZUaG1tOmmW3+/rtJ37/fLFPugzDgKmATsBV4zma+AkY55q8B2gdbZ3kIwgLJyzMfvL17zdn/7t3mjygSP8byKP+Pli1Lblt2QY7dw9Khg++8wYPNvFGjCr/93bs9/6wi9b05dMg8WzVmlvR082Mf7WMuj9Af339v/pCtmp78fK07djTzRowwxzjaZQz0CFbrBlo/9ZR9er9+vmlKeb62grRIPsaMCe/6zjwz8Pzq1U3AbL0OtZavOAIFYcrMDz+lVCywGbgSSAKWA3211v+45bkaGAhcDVwAjNRa2/RgdunYsaNesWJFRMpcHlgdT3fuhIoVzSXz//xjBmU8dAhOPdWkNWgAY8dGtahClGkffggDBgTPl5EBixbB77/DGWfA/fdHvmyicOLizAVWBw9GuySipI0fH/pFZUWllFqptba9VU0kg7DOwBCtdU/H6+cBtNavu+X5CJintZ7seL0J6K619jvUoQRhUbR0qbkM8rTTzFD/cXEm0gM4fpyCOb+hViyHN17nODWoRjrbOINm7CSPCuTu3s+xgho0aWKunMrJMYFhQYEJGitUMJf4b95s7s954gT88Qc0a2YuQb/8cnMFV+vWZnSHihVh0iTNqce30oXFfEr/gMWfNw+mTAl+RUzlyq4hHxo2DH5lkzg5TZwI/QN/5IpFa3NVY2amuZpxzx4z1ti+feZ54UJYvdrMt7vSUPiXn2+C4zVr4M8/Yf16SE21v8JSlG+dO8Ni3yEvwypaQdjNwFVa6/scr+8ELtBaP+qW5wfgDa31Qsfr34BntdYrvNb1APAAQJMmTTrs8r4uXpReeXmwY4cJ2Jo1i3ZpSofkZFMVCfDww67r+O3s3w8//mj+cS+6CO67r1CbsirdraEcvKdzc01ga8XUmZnmOS7ODDBavboJljMyTL6sLDPEQHa2ed6wAerVM4e5SRPz5/b332Y9FSuafImJZluVKkFSkgmwW7Y092BdsQLi4005OnQwQ6Fs22Z2NS3NrDcnx5TnxAkz1tapp5pBSGvVMutMSDCB+ddfm8vOExLMn+unn5ogSSmzvV27zPNrr8Ett5iyfvmlORTr10OLFiZIP/10s5/Nm5v3ZetWs96LLjIjzO/aZc5FBg40Y1oJ//LzzbGPjTXHOC/PHLPcXHPSlZdn3usqVcwxtmryjxwxy1Svbj4zp5xi8mzcaD5v+fnmZCkmxtRe5eRAo0bmhOnUU13HRSlzbBs2NF+lBg3M57Zy5eLtV0GBWbdSZtsxMa59TUszn+n0dPP5z8w0eayTuxMnzHAU2dkmvWJF13vw99+uceAOH4b69c17Va+eyX/0qPneWGO71a9vTlrbtTPrX7PGfF47dDDj7zVtaoL36tWhRg0zNEhCgvlJ6dvX5LeGVdqyxbzP+/ZBt27mGDRvbrY3caKpxc3KMicAWpv3OyfHlCktzTynpMAVV5jz9cREWL7cLL9jhxmGIzPTvDdKmbHzfvwROnY0x6tqVbONZctcn42rrzbb+usvc8Kxd6/ZlyZNzPubkWHem7PPNts/dMhsb+dOz/f23HPNvs2d6/r9q10bXnrJnOh7j7EXbtEKwv4P6OkVhHXSWg90y/Mj8LpXEPaM1trPKENSEyaEEEKIsiNQEBbJwVqTgMZurxsB3mMCh5JHCCGEEKLciWQQthxooZRqrpSKB24FvO9O9h3QTxkXAqmB+oMJIYQQQpQXFSK1Yq11nlLqUeAXIBYYr7Ver5Qa4Jg/BvgJc2XkViATiPA1CkIIIYQQpUPEgjAArfVPmEDLPW2M27QGHolkGYQQQgghSiO5gbcQQgghRBRIECaEEEIIEQUShAkhhBBCRIEEYUIIIYQQURCxwVojRSmVApTEkPl1gEMlsB0ROjkmpY8ck9JJjkvpI8ekdCqJ49JUa13XbkaZC8JKilJqhb8RbkV0yDEpfeSYlE5yXEofOSalU7SPizRHCiGEEEJEgQRhQgghhBBRIEGYf2OjXQDhQ45J6SPHpHSS41L6yDEpnaJ6XKRPmBBCCCFEFEhNmBBCCCFEFEgQJoQQQggRBRKEeVFKXaWU2qSU2qqUei7a5SnvlFI7lVJrlVKrlFIrHGmnKKXmKKW2OJ5rueV/3nFsNimlerqld3CsZ6tSapRSSkVjf8oqpdR4pdRBpdQ6t7SwHQelVEWl1FRH+p9KqWYluoNlkJ9jMkQptdfxfVmllLrabZ4ckwhTSjVWSv2ulNqglFqvlHrckS7flSgKcFxK//dFay0PxwOIBbYBpwPxwGqgVbTLVZ4fwE6gjlfam8BzjunngOGO6VaOY1IRaO44VrGOecuAzoACfgZ6RXvfytID6Aq0B9ZF4jgADwNjHNO3AlOjvc+l/eHnmAwBnrbJK8ekZI7JaUB7x3R1YLPjvZfvSuk8LqX++yI1YZ46AVu11tu11jnAFOD6KJfpZHQ9MMkxPQm4wS19itY6W2u9A9gKdFJKnQbU0Fov0eYb8qnbMiIEWusFwBGv5HAeB/d1fQ1cLrWVgfk5Jv7IMSkBWutkrfVfjuk0YAPQEPmuRFWA4+JPqTkuEoR5agjscXudROADKYpPA7OVUiuVUg840upprZPBfLmAUx3p/o5PQ8e0d7oonnAeB+cyWus8IBWoHbGSl2+PKqXWOJorrWYvOSYlzNEcdR7wJ/JdKTW8jguU8u+LBGGe7KJaGcMjsi7SWrcHegGPKKW6Bsjr7/jIcStZRTkOcozC40PgDCARSAbedqTLMSlBSqlqwHTgCa318UBZbdLkuESIzXEp9d8XCcI8JQGN3V43AvZFqSwnBa31PsfzQWAGpkn4gKNaGMfzQUd2f8cnyTHtnS6KJ5zHwbmMUqoCkEDoTW3CQWt9QGudr7UuAD7GfF9AjkmJUUrFYf7ov9Baf+NIlu9KlNkdl7LwfZEgzNNyoIVSqrlSKh7T+e67KJep3FJKVVVKVbemgR7AOsx73t+RrT8w0zH9HXCr4yqV5kALYJmj+j9NKXWho42+n9syoujCeRzc13UzMNfR50IUgvVH79Ab830BOSYlwvEejgM2aK3fcZsl35Uo8ndcysT3JVpXM5TWB3A15sqKbcDgaJenPD8wV6GudjzWW+83pp39N2CL4/kUt2UGO47NJtyugAQ6Or5g24D3cdwNQh4hH4vJmOr6XMwZ373hPA5AJWAapgPsMuD0aO9zaX/4OSafAWuBNZg/hdPkmJToMbkY0wS1BljleFwt35VSe1xK/fdFblskhBBCCBEF0hwphBBCCBEFEoQJIYQQQkSBBGFCCCGEEFEgQZgQQgghRBRIECaEEEIIEQUShAkhyhWlVL5SapXb47kwrruZUmpd8JxCCBFchWgXQAghwixLa50Y7UIIIUQwUhMmhDgpKKV2KqWGK6WWOR5nOtKbKqV+c9zk9zelVBNHej2l1Ayl1GrHo4tjVbFKqY+VUuuVUrOVUpWjtlNCiDJNgjAhRHlT2as58ha3ece11p0wI2GPcKS9D3yqtW4LfAGMcqSPAuZrrdsB7TF3dQBzi5PRWuvWwDHgpojujRCi3JIR84UQ5YpSKl1rXc0mfSdwmdZ6u+Nmv/u11rWVUocwtzPJdaQna63rKKVSgEZa62y3dTQD5mitWzhePwvEaa1fLYFdE0KUM1ITJoQ4mWg/0/7y2Ml2m85H+tYKIYpIgjAhxMnkFrfnJY7pxcCtjunbgYWO6d+AhwCUUrFKqRolVUghxMlBzuCEEOVNZaXUKrfXs7TW1jAVFZVSf2JOQPs60h4DxiulBgEpwN2O9MeBsUqpezE1Xg8ByZEuvBDi5CF9woQQJwVHn7COWutD0S6LEEKANEcKIYQQQkSF1IQJIYQQQkSB1IQJIYQQQkSBBGFCCCGEEFEgQZgQQgghRBRIECaEEEIIEQUShAkhhBBCRMH/AwGtFUMOtYMiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(F2M1epoch_arr,F2M1loss_arr, color = \"red\")\n",
    "plt.plot(F2M2epoch_arr,F2M2loss_arr, color = \"blue\")\n",
    "plt.plot(F2M3epoch_arr,F2M3loss_arr, color = \"green\")\n",
    "plt.title('Model Loss for Function 2')\n",
    "plt.legend(['First Model', 'Second Model','Thrid Model'])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.savefig('EpochVSLoss.png')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(x2,F2M1prediction.data.numpy(),color=\"red\")\n",
    "plt.plot(x2,F2M2prediction.data.numpy(),color=\"blue\")\n",
    "plt.plot(x2,F2M3prediction.data.numpy(),color=\"green\")\n",
    "plt.plot(x2,y2.numpy(),color=\"black\")\n",
    "plt.title('Model Prediction for function 2')\n",
    "plt.legend(['First Model', 'Second Model','Thrid Model','Orginal Value for function 2'])\n",
    "plt.xlabel('Function')\n",
    "plt.ylabel('Target')\n",
    "plt.savefig('Model_Prediction.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8706ad4d-a89e-47f3-a3f6-190932f5ca1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}