notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "float_32=torch.tensor([3.0,6.0,9.0],\n",
    "                      dtype=torch.float32,\n",
    "                      device='cpu',\n",
    "                      requires_grad=False)\n",
    "float_16=float_32.type(torch.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_mix=float_16*float_32\n",
    "float_mix.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor=torch.rand(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.4668, 0.9583],\n",
       "        [0.9396, 0.8561],\n",
       "        [0.0922, 0.2942]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.0382, 0.5356, 0.7205],\n",
       "        [1.1714, 0.9793, 1.1347],\n",
       "        [0.2949, 0.1155, 0.1732]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.matmul(tensor,tensor.reshape(2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=torch.arange(0,100,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x14fe2213b50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pytorch Workflow Fundamentals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cpu'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device='cuda' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([100, 1]), torch.Size([100, 1]))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W=0.7\n",
    "b=0.9\n",
    "X=torch.arange(0,1,0.01).unsqueeze(1)\n",
    "Y=(X*W)+b\n",
    "X.shape,Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotPredictions(train_data=X_train,\n",
    "                    train_labels=y_train,\n",
    "                    test_data=X_test,\n",
    "                    test_labels=y_test,\n",
    "                    y_pred=None\n",
    "                    ):\n",
    "    plt.figure(figsize=(10,7))\n",
    "    plt.scatter(x=X_train.squeeze(),y=y_train.squeeze(),c='black',s=10,label=\"Training Data\")\n",
    "    plt.scatter(x=X_test.squeeze(),y=y_test.squeeze(),c='orange',s=10,label=\"Training Data\")\n",
    "    if y_pred is not None:\n",
    "        plt.scatter(x=X_test.squeeze(),y=y_pred.squeeze(),c='blue',s=10,label=\"Predictions\")\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPredictions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegressionModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.weight=nn.Parameter(torch.randn(1,\n",
    "                                            dtype=float,\n",
    "                                            requires_grad=True))\n",
    "        self.bais=nn.Parameter(torch.randn(1,\n",
    "                                           dtype=float,\n",
    "                                           requires_grad=True))\n",
    "    def forward(self,X:torch.Tensor) -> torch.Tensor:\n",
    "        return (self.weight*X)+self.bais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict({'weight': tensor([0.3367], dtype=torch.float64), 'bais': tensor([0.1288], dtype=torch.float64)})\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "model_0=LinearRegressionModel()\n",
    "list(model_0.parameters())\n",
    "print(model_0.state_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.3342],\n",
       "        [0.4083],\n",
       "        [0.4588],\n",
       "        [0.3544],\n",
       "        [0.4116],\n",
       "        [0.2062],\n",
       "        [0.3847],\n",
       "        [0.3611],\n",
       "        [0.4487],\n",
       "        [0.4318]], dtype=torch.float64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with torch.inference_mode():\n",
    "    y_pred=model_0(X_test)\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPredictions(y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Train MAE: 0.9404928649565172, Test MAE : 0.9803254945542491\n",
      "20: Train MAE: 0.6970616651620575, Test MAE : 0.7233802949549498\n",
      "40: Train MAE: 0.4536304653675979, Test MAE : 0.4664350953556508\n",
      "60: Train MAE: 0.21019926557313826, Test MAE : 0.20948989575635163\n",
      "80: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "100: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "120: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "140: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "160: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "180: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "200: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "220: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "240: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "260: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n",
      "280: Train MAE: 0.008888814241875397, Test MAE : 0.003933736076912314\n"
     ]
    }
   ],
   "source": [
    "epoch_count=[]\n",
    "train_loss_list=[]\n",
    "test_loss_list=[]\n",
    "\n",
    "\n",
    "loss_fn=nn.L1Loss()\n",
    "optim=torch.optim.SGD(model_0.parameters(),lr=0.01)\n",
    "epochs=300\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model_0.train()\n",
    "\n",
    "    optim.zero_grad()\n",
    "\n",
    "    y_train_pred=model_0(X_train)\n",
    "\n",
    "    train_loss=loss_fn(y_train_pred,y_train)\n",
    "\n",
    "    train_loss.backward()\n",
    "\n",
    "    optim.step()\n",
    "\n",
    "    \n",
    "\n",
    "    if epoch%20==0:\n",
    "        model_0.eval()\n",
    "\n",
    "        with torch.inference_mode():\n",
    "            test_pred=model_0(X_test)\n",
    "\n",
    "        test_loss=loss_fn(test_pred,y_test)\n",
    "        \n",
    "        epoch_count.append(epoch)\n",
    "        train_loss_list.append(train_loss.detach().numpy())\n",
    "        test_loss_list.append(test_loss.detach().numpy())\n",
    "        print(f\"{epoch}: Train MAE: {train_loss}, Test MAE : {test_loss}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_loss(train_loss_list,test_loss_list):\n",
    "    plt.figure(figsize=(10,7))\n",
    "    plt.plot(epoch_count,train_loss_list,c='orange',label='Training Loss')\n",
    "    plt.plot(epoch_count,test_loss_list,c='black',label='Testing Loss')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_loss(train_loss_list,test_loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('weight', tensor([0.7002], dtype=torch.float64)),\n",
       "             ('bais', tensor([0.9088], dtype=torch.float64))])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## learned Params\n",
    "model_0.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with torch.inference_mode():\n",
    "    y_pred=model_0(X_test)\n",
    "plotPredictions(y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "MODEL_PATH=Path('models')\n",
    "MODEL_PATH.mkdir(parents=True,exist_ok=True)\n",
    "\n",
    "MODEL_NAME=\"01_pytorch_workflow_model_0.pth\"\n",
    "MODEL_SAVE_PATH=MODEL_PATH/MODEL_NAME\n",
    "\n",
    "torch.save(obj=model_0.state_dict(),f=MODEL_SAVE_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cpu'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device='cuda' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1\n",
    "\n",
    "Make a binary classification dataset with Scikit-Learn's make_moons() function.\n",
    "\n",
    "    For consistency, the dataset should have 1000 samples and a random_state=42.\n",
    "    Turn the data into PyTorch tensors. Split the data into training and test sets using train_test_split with 80% training and 20% testing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE=42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=make_moons(n_samples=1000,random_state=RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14ffeb7af30>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of X torch.float32 | Type of y torch.float32\n"
     ]
    }
   ],
   "source": [
    "X=torch.from_numpy(X).type(torch.float)\n",
    "y=torch.from_numpy(y).type(torch.float)\n",
    "print(f\"Type of X {X.dtype} | Type of y {y.dtype}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=RANDOM_STATE,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT=X.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MoonClassV1(\n",
       "  (seq_layers): Sequential(\n",
       "    (0): Linear(in_features=2, out_features=10, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=10, out_features=10, bias=True)\n",
       "    (3): ReLU()\n",
       "    (4): Linear(in_features=10, out_features=10, bias=True)\n",
       "    (5): ReLU()\n",
       "    (6): Linear(in_features=10, out_features=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch import nn\n",
    "class MoonClassV1(nn.Module):\n",
    "    \n",
    "    def __init__(self,input_feature,hidden_features,output_features):\n",
    "        super().__init__()\n",
    "        self.seq_layers=nn.Sequential(\n",
    "                        nn.Linear(in_features=input_feature,out_features=hidden_features),\n",
    "                        nn.ReLU(),\n",
    "                        nn.Linear(in_features=hidden_features,out_features=hidden_features),\n",
    "                        nn.ReLU(),\n",
    "                        nn.Linear(in_features=hidden_features,out_features=hidden_features),\n",
    "                        nn.ReLU(),\n",
    "                        nn.Linear(in_features=hidden_features,out_features=output_features)\n",
    "                        )\n",
    "\n",
    "    def forward(self,x):\n",
    "        return self.seq_layers(x)\n",
    "model_4=MoonClassV1(input_feature=2,hidden_features=10,output_features=1).to(device)\n",
    "model_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn=nn.BCEWithLogitsLoss()\n",
    "optim=torch.optim.SGD(model_4.parameters(),\n",
    "                      lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def accuracy_fn(y_test,y_pred):\n",
    "#     y_test=y_test\n",
    "#     y_pred=y_pred\n",
    "#     return sum(y_test==y_pred)/(y_test.shape[0]+y_pred.shape[0])\n",
    "def accuracy_fn(y_true, y_pred):\n",
    "    correct = torch.eq(y_true, y_pred).sum().item() # torch.eq() calculates where two tensors are equal\n",
    "    acc = (correct / len(y_pred)) * 100 \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Train_loss: 0.7028446197509766 | Train_acc: 50.0 | Test_loss: 0.7017712593078613 | Test_acc: 50.0\n",
      "10: Train_loss: 0.6923766136169434 | Train_acc: 50.0 | Test_loss: 0.6923398375511169 | Test_acc: 50.0\n",
      "20: Train_loss: 0.6834291815757751 | Train_acc: 66.875 | Test_loss: 0.6841877102851868 | Test_acc: 61.5\n",
      "30: Train_loss: 0.6733468770980835 | Train_acc: 72.5 | Test_loss: 0.6749029755592346 | Test_acc: 68.0\n",
      "40: Train_loss: 0.6599600315093994 | Train_acc: 78.25 | Test_loss: 0.6625420451164246 | Test_acc: 75.5\n",
      "50: Train_loss: 0.641018271446228 | Train_acc: 78.875 | Test_loss: 0.6449383497238159 | Test_acc: 76.0\n",
      "60: Train_loss: 0.6135632991790771 | Train_acc: 79.375 | Test_loss: 0.6193956732749939 | Test_acc: 76.0\n",
      "70: Train_loss: 0.5750464797019958 | Train_acc: 79.25 | Test_loss: 0.5836650133132935 | Test_acc: 77.0\n",
      "80: Train_loss: 0.5273573398590088 | Train_acc: 79.375 | Test_loss: 0.5398254990577698 | Test_acc: 79.0\n",
      "90: Train_loss: 0.4768719971179962 | Train_acc: 80.125 | Test_loss: 0.49350491166114807 | Test_acc: 80.0\n",
      "100: Train_loss: 0.43049511313438416 | Train_acc: 80.75 | Test_loss: 0.44910433888435364 | Test_acc: 82.5\n",
      "110: Train_loss: 0.38823482394218445 | Train_acc: 81.75 | Test_loss: 0.4063606262207031 | Test_acc: 83.0\n",
      "120: Train_loss: 0.3510705232620239 | Train_acc: 83.25 | Test_loss: 0.3675994575023651 | Test_acc: 83.5\n",
      "130: Train_loss: 0.32075345516204834 | Train_acc: 84.25 | Test_loss: 0.33563128113746643 | Test_acc: 84.5\n",
      "140: Train_loss: 0.29638373851776123 | Train_acc: 85.25 | Test_loss: 0.3093257248401642 | Test_acc: 86.5\n",
      "150: Train_loss: 0.2767871618270874 | Train_acc: 86.25 | Test_loss: 0.28763657808303833 | Test_acc: 87.0\n",
      "160: Train_loss: 0.2614314556121826 | Train_acc: 87.0 | Test_loss: 0.270045667886734 | Test_acc: 88.0\n",
      "170: Train_loss: 0.2494461089372635 | Train_acc: 87.75 | Test_loss: 0.2560252547264099 | Test_acc: 89.0\n",
      "180: Train_loss: 0.24018555879592896 | Train_acc: 88.25 | Test_loss: 0.2448883205652237 | Test_acc: 89.5\n",
      "190: Train_loss: 0.23303541541099548 | Train_acc: 88.75 | Test_loss: 0.23628249764442444 | Test_acc: 90.0\n",
      "200: Train_loss: 0.22739671170711517 | Train_acc: 89.25 | Test_loss: 0.2294890582561493 | Test_acc: 90.0\n",
      "210: Train_loss: 0.22272834181785583 | Train_acc: 89.375 | Test_loss: 0.22388212382793427 | Test_acc: 90.5\n",
      "220: Train_loss: 0.2185860276222229 | Train_acc: 89.625 | Test_loss: 0.2190183699131012 | Test_acc: 91.0\n",
      "230: Train_loss: 0.21466343104839325 | Train_acc: 89.875 | Test_loss: 0.2145586609840393 | Test_acc: 91.0\n",
      "240: Train_loss: 0.2107550948858261 | Train_acc: 90.0 | Test_loss: 0.21038465201854706 | Test_acc: 91.5\n",
      "250: Train_loss: 0.20680536329746246 | Train_acc: 90.125 | Test_loss: 0.20625929534435272 | Test_acc: 92.0\n",
      "260: Train_loss: 0.20272037386894226 | Train_acc: 90.375 | Test_loss: 0.20201070606708527 | Test_acc: 92.0\n",
      "270: Train_loss: 0.19841547310352325 | Train_acc: 90.625 | Test_loss: 0.19757263362407684 | Test_acc: 92.0\n",
      "280: Train_loss: 0.19383254647254944 | Train_acc: 90.75 | Test_loss: 0.1928989291191101 | Test_acc: 92.0\n",
      "290: Train_loss: 0.18893250823020935 | Train_acc: 91.125 | Test_loss: 0.18793337047100067 | Test_acc: 92.0\n",
      "300: Train_loss: 0.18368586897850037 | Train_acc: 91.375 | Test_loss: 0.18262235820293427 | Test_acc: 92.0\n",
      "310: Train_loss: 0.17807313799858093 | Train_acc: 91.5 | Test_loss: 0.176947683095932 | Test_acc: 93.0\n",
      "320: Train_loss: 0.17208927869796753 | Train_acc: 91.75 | Test_loss: 0.17091187834739685 | Test_acc: 93.0\n",
      "330: Train_loss: 0.1657375693321228 | Train_acc: 92.125 | Test_loss: 0.16452307999134064 | Test_acc: 93.0\n",
      "340: Train_loss: 0.15902602672576904 | Train_acc: 92.625 | Test_loss: 0.157782644033432 | Test_acc: 93.0\n",
      "350: Train_loss: 0.15198086202144623 | Train_acc: 93.0 | Test_loss: 0.1507340669631958 | Test_acc: 93.0\n",
      "360: Train_loss: 0.14463460445404053 | Train_acc: 93.5 | Test_loss: 0.1432485580444336 | Test_acc: 93.0\n",
      "370: Train_loss: 0.13696947693824768 | Train_acc: 94.0 | Test_loss: 0.13550756871700287 | Test_acc: 93.0\n",
      "380: Train_loss: 0.12902840971946716 | Train_acc: 94.125 | Test_loss: 0.1274811178445816 | Test_acc: 94.5\n",
      "390: Train_loss: 0.12083129584789276 | Train_acc: 94.625 | Test_loss: 0.11929517984390259 | Test_acc: 95.0\n",
      "400: Train_loss: 0.11248418688774109 | Train_acc: 95.0 | Test_loss: 0.11096517741680145 | Test_acc: 96.0\n",
      "410: Train_loss: 0.10424312949180603 | Train_acc: 95.5 | Test_loss: 0.10290255397558212 | Test_acc: 96.5\n",
      "420: Train_loss: 0.09589525312185287 | Train_acc: 95.875 | Test_loss: 0.09447398781776428 | Test_acc: 97.5\n",
      "430: Train_loss: 0.0877581238746643 | Train_acc: 96.5 | Test_loss: 0.08611449599266052 | Test_acc: 97.5\n",
      "440: Train_loss: 0.07997362315654755 | Train_acc: 97.125 | Test_loss: 0.07825881242752075 | Test_acc: 97.5\n",
      "450: Train_loss: 0.07257198542356491 | Train_acc: 97.625 | Test_loss: 0.0707935318350792 | Test_acc: 98.0\n",
      "460: Train_loss: 0.06564057618379593 | Train_acc: 98.25 | Test_loss: 0.06379535794258118 | Test_acc: 98.0\n",
      "470: Train_loss: 0.059226371347904205 | Train_acc: 98.75 | Test_loss: 0.057327184826135635 | Test_acc: 98.0\n",
      "480: Train_loss: 0.053373198956251144 | Train_acc: 99.125 | Test_loss: 0.0514836423099041 | Test_acc: 98.5\n",
      "490: Train_loss: 0.04808340221643448 | Train_acc: 99.375 | Test_loss: 0.04617702588438988 | Test_acc: 99.5\n",
      "500: Train_loss: 0.043332409113645554 | Train_acc: 99.5 | Test_loss: 0.04144797474145889 | Test_acc: 100.0\n",
      "510: Train_loss: 0.03910214826464653 | Train_acc: 99.75 | Test_loss: 0.03722884878516197 | Test_acc: 100.0\n",
      "520: Train_loss: 0.03534887731075287 | Train_acc: 99.875 | Test_loss: 0.03350489214062691 | Test_acc: 100.0\n",
      "530: Train_loss: 0.032034918665885925 | Train_acc: 100.0 | Test_loss: 0.030206715688109398 | Test_acc: 100.0\n",
      "540: Train_loss: 0.029110439121723175 | Train_acc: 100.0 | Test_loss: 0.027325674891471863 | Test_acc: 100.0\n",
      "550: Train_loss: 0.02653822861611843 | Train_acc: 100.0 | Test_loss: 0.02481675148010254 | Test_acc: 100.0\n",
      "560: Train_loss: 0.024272534996271133 | Train_acc: 100.0 | Test_loss: 0.022602763026952744 | Test_acc: 100.0\n",
      "570: Train_loss: 0.022277966141700745 | Train_acc: 100.0 | Test_loss: 0.020644783973693848 | Test_acc: 100.0\n",
      "580: Train_loss: 0.020508794113993645 | Train_acc: 100.0 | Test_loss: 0.018923379480838776 | Test_acc: 100.0\n",
      "590: Train_loss: 0.018938176333904266 | Train_acc: 100.0 | Test_loss: 0.017392421141266823 | Test_acc: 100.0\n",
      "600: Train_loss: 0.017539571970701218 | Train_acc: 100.0 | Test_loss: 0.016041109338402748 | Test_acc: 100.0\n",
      "610: Train_loss: 0.01629117876291275 | Train_acc: 100.0 | Test_loss: 0.014841174706816673 | Test_acc: 100.0\n",
      "620: Train_loss: 0.015174069441854954 | Train_acc: 100.0 | Test_loss: 0.01377073209732771 | Test_acc: 100.0\n",
      "630: Train_loss: 0.014168467372655869 | Train_acc: 100.0 | Test_loss: 0.012808794155716896 | Test_acc: 100.0\n",
      "640: Train_loss: 0.01326358038932085 | Train_acc: 100.0 | Test_loss: 0.011943671852350235 | Test_acc: 100.0\n",
      "650: Train_loss: 0.012445168569684029 | Train_acc: 100.0 | Test_loss: 0.011167844757437706 | Test_acc: 100.0\n",
      "660: Train_loss: 0.011703252792358398 | Train_acc: 100.0 | Test_loss: 0.01046816911548376 | Test_acc: 100.0\n",
      "670: Train_loss: 0.011028790846467018 | Train_acc: 100.0 | Test_loss: 0.009832803159952164 | Test_acc: 100.0\n",
      "680: Train_loss: 0.010412690229713917 | Train_acc: 100.0 | Test_loss: 0.00925664883106947 | Test_acc: 100.0\n",
      "690: Train_loss: 0.009850378148257732 | Train_acc: 100.0 | Test_loss: 0.00873236171901226 | Test_acc: 100.0\n",
      "700: Train_loss: 0.00933336652815342 | Train_acc: 100.0 | Test_loss: 0.008250357583165169 | Test_acc: 100.0\n",
      "710: Train_loss: 0.008852296508848667 | Train_acc: 100.0 | Test_loss: 0.007797963451594114 | Test_acc: 100.0\n",
      "720: Train_loss: 0.0084035973995924 | Train_acc: 100.0 | Test_loss: 0.007365444675087929 | Test_acc: 100.0\n",
      "730: Train_loss: 0.007982317358255386 | Train_acc: 100.0 | Test_loss: 0.006954792886972427 | Test_acc: 100.0\n",
      "740: Train_loss: 0.0075890058651566505 | Train_acc: 100.0 | Test_loss: 0.00655990419909358 | Test_acc: 100.0\n",
      "750: Train_loss: 0.007221142761409283 | Train_acc: 100.0 | Test_loss: 0.006187498569488525 | Test_acc: 100.0\n",
      "760: Train_loss: 0.006871219724416733 | Train_acc: 100.0 | Test_loss: 0.005842078942805529 | Test_acc: 100.0\n",
      "770: Train_loss: 0.006539623718708754 | Train_acc: 100.0 | Test_loss: 0.005524498876184225 | Test_acc: 100.0\n",
      "780: Train_loss: 0.006226483732461929 | Train_acc: 100.0 | Test_loss: 0.005231200251728296 | Test_acc: 100.0\n",
      "790: Train_loss: 0.005938909016549587 | Train_acc: 100.0 | Test_loss: 0.00495527870953083 | Test_acc: 100.0\n",
      "800: Train_loss: 0.005670079030096531 | Train_acc: 100.0 | Test_loss: 0.004700979683548212 | Test_acc: 100.0\n",
      "810: Train_loss: 0.005418611224740744 | Train_acc: 100.0 | Test_loss: 0.004468874540179968 | Test_acc: 100.0\n",
      "820: Train_loss: 0.005182832013815641 | Train_acc: 100.0 | Test_loss: 0.00425568874925375 | Test_acc: 100.0\n",
      "830: Train_loss: 0.00496240658685565 | Train_acc: 100.0 | Test_loss: 0.004059865605086088 | Test_acc: 100.0\n",
      "840: Train_loss: 0.004756553564220667 | Train_acc: 100.0 | Test_loss: 0.0038791028782725334 | Test_acc: 100.0\n",
      "850: Train_loss: 0.004564171191304922 | Train_acc: 100.0 | Test_loss: 0.0037131914868950844 | Test_acc: 100.0\n",
      "860: Train_loss: 0.004384710919111967 | Train_acc: 100.0 | Test_loss: 0.003559031756594777 | Test_acc: 100.0\n",
      "870: Train_loss: 0.004217359237372875 | Train_acc: 100.0 | Test_loss: 0.003414856269955635 | Test_acc: 100.0\n",
      "880: Train_loss: 0.004060868639498949 | Train_acc: 100.0 | Test_loss: 0.0032800890039652586 | Test_acc: 100.0\n",
      "890: Train_loss: 0.003913721535354853 | Train_acc: 100.0 | Test_loss: 0.0031539795454591513 | Test_acc: 100.0\n",
      "900: Train_loss: 0.0037760192062705755 | Train_acc: 100.0 | Test_loss: 0.00303611159324646 | Test_acc: 100.0\n",
      "910: Train_loss: 0.003646459663286805 | Train_acc: 100.0 | Test_loss: 0.002926209010183811 | Test_acc: 100.0\n",
      "920: Train_loss: 0.003524408908560872 | Train_acc: 100.0 | Test_loss: 0.0028232482727617025 | Test_acc: 100.0\n",
      "930: Train_loss: 0.0034092485439032316 | Train_acc: 100.0 | Test_loss: 0.0027263846714049578 | Test_acc: 100.0\n",
      "940: Train_loss: 0.003300063544884324 | Train_acc: 100.0 | Test_loss: 0.002635431243106723 | Test_acc: 100.0\n",
      "950: Train_loss: 0.003196673933416605 | Train_acc: 100.0 | Test_loss: 0.0025492694694548845 | Test_acc: 100.0\n",
      "960: Train_loss: 0.0030987781938165426 | Train_acc: 100.0 | Test_loss: 0.0024681929498910904 | Test_acc: 100.0\n",
      "970: Train_loss: 0.003006136743351817 | Train_acc: 100.0 | Test_loss: 0.0023912168107926846 | Test_acc: 100.0\n",
      "980: Train_loss: 0.0029182008001953363 | Train_acc: 100.0 | Test_loss: 0.002318401588127017 | Test_acc: 100.0\n",
      "990: Train_loss: 0.0028346613980829716 | Train_acc: 100.0 | Test_loss: 0.0022499701008200645 | Test_acc: 100.0\n"
     ]
    }
   ],
   "source": [
    "epoch_count=[]\n",
    "train_loss_list=[]\n",
    "test_loss_list=[]\n",
    "\n",
    "epochs=1000\n",
    "for epoch in range(epochs):\n",
    "    model_4.train()\n",
    "\n",
    "    y_logits=model_4(X_train).squeeze(dim=1)\n",
    "    y_train_pred=torch.round(torch.sigmoid(y_logits))\n",
    "    \n",
    "    loss=loss_fn(y_logits,y_train)\n",
    "\n",
    "    accuracy_train=accuracy_fn(y_train,y_train_pred)\n",
    "\n",
    "    optim.zero_grad()\n",
    "\n",
    "    loss.backward()\n",
    "\n",
    "    optim.step()\n",
    "\n",
    "    # testing\n",
    "    model_4.eval()\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        y_test_logits=model_4(X_test).squeeze(dim=1)\n",
    "        y_test_pred=torch.round(torch.sigmoid(y_test_logits))\n",
    "\n",
    "        loss_test=loss_fn(y_test_logits,y_test)\n",
    "\n",
    "        accuracy_test=accuracy_fn(y_test,y_test_pred)\n",
    "\n",
    "    if epoch%10==0:\n",
    "        epoch_count.append(epoch)\n",
    "        train_loss_list.append(loss.detach().numpy())\n",
    "        test_loss_list.append(loss_test.detach().numpy())\n",
    "        print(f\"{epoch}: Train_loss: {loss} | Train_acc: {accuracy_train} | Test_loss: {loss_test} | Test_acc: {accuracy_test}\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14f83ed1f10>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,7))\n",
    "plt.plot(epoch_count,train_loss_list,c='black',label='Training Loss')\n",
    "plt.plot(epoch_count,test_loss_list,c='yellow',label='Testing Loss')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_boundary(model: torch.nn.Module,X:torch.Tensor,y:torch.Tensor):\n",
    "    \n",
    "    model.to('cpu')\n",
    "    X, y=X.to('cpu'),y.to('cpu')\n",
    "\n",
    "    x_min,x_max=X[:,0].min()-0.1,X[:,0].max()+0.1\n",
    "    y_min,y_max=X[:,1].min()-0.1,X[:,1].max()+0.1\n",
    "\n",
    "    xx,yy=np.meshgrid(np.linspace(x_min,x_max,101),np.linspace(y_min,y_max,101))\n",
    "\n",
    "    X_to_y_pred=torch.from_numpy(np.column_stack((xx.ravel(),yy.ravel()))).type(torch.float)\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        y_logits=model(X_to_y_pred)\n",
    "\n",
    "    if len(torch.unique(y))>2:\n",
    "        y_pred=torch.softmax(y_logits,dim=1).argmax(dim=1)\n",
    "    else:\n",
    "        y_pred=torch.round(torch.sigmoid(y_logits))\n",
    "\n",
    "\n",
    "    y_pred=y_pred.reshape(xx.shape).detach().numpy()\n",
    "    plt.contourf(xx,yy,y_pred,cmap=plt.cm.RdYlBu,alpha=0.7)\n",
    "    plt.scatter(X[:,0],X[:,1],c=y,s=40,cmap=plt.cm.RdYlBu)\n",
    "    plt.xlim(x_min,x_max)\n",
    "    plt.ylim(y_min,y_max)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boundary(model_4,X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tan_h(logits):\n",
    "    return (torch.exp(logits)-torch.exp(-logits))/(torch.exp(logits)+torch.exp(-logits))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code for creating a spiral dataset from CS231n\n",
    "import numpy as np\n",
    "N = 100 # number of points per class\n",
    "D = 2 # dimensionality\n",
    "K = 3 # number of classes\n",
    "X = np.zeros((N*K,D)) # data matrix (each row = single example)\n",
    "y = np.zeros(N*K, dtype='uint8') # class labels\n",
    "for j in range(K):\n",
    "  ix = range(N*j,N*(j+1))\n",
    "  r = np.linspace(0.0,1,N) # radius\n",
    "  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta\n",
    "  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n",
    "  y[ix] = j\n",
    "# lets visualize the data\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2], dtype=uint8)"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_CLASSES=len(np.unique(y))\n",
    "NUM_FEATURES=X.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=RANDOM_STATE)\n",
    "X_train,X_test=torch.from_numpy(X_train).type(torch.float).to(device),torch.from_numpy(X_test).type(torch.float).to(device)\n",
    "y_train,y_test=torch.from_numpy(y_train).type(torch.LongTensor).to(device),torch.from_numpy(y_test).type(torch.LongTensor).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiClass(nn.Module):\n",
    "    def __init__(self, input_features,hidden_neurons,output_feature):\n",
    "        super().__init__()\n",
    "        self.seq_layer=nn.Sequential(\n",
    "            nn.Linear(in_features=input_features,out_features=hidden_neurons),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=hidden_neurons,out_features=hidden_neurons),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=hidden_neurons,out_features=hidden_neurons),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=hidden_neurons,out_features=output_feature),\n",
    "        )\n",
    "\n",
    "    def forward(self,x):\n",
    "        return self.seq_layer(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiClass(\n",
       "  (seq_layer): Sequential(\n",
       "    (0): Linear(in_features=2, out_features=10, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=10, out_features=10, bias=True)\n",
       "    (3): ReLU()\n",
       "    (4): Linear(in_features=10, out_features=10, bias=True)\n",
       "    (5): ReLU()\n",
       "    (6): Linear(in_features=10, out_features=3, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_5=MultiClass(input_features=NUM_FEATURES,hidden_neurons=10,output_feature=NUM_CLASSES).to(device)\n",
    "model_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn=nn.CrossEntropyLoss()\n",
    "optim=torch.optim.Adam(model_5.parameters(),lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Train_loss: 1.0999644994735718 | Train_acc: 32.5 | Test_loss: 1.164074182510376 | Test_acc: 26.666666666666668\n",
      "10: Train_loss: 0.707125723361969 | Train_acc: 61.25000000000001 | Test_loss: 0.7514070272445679 | Test_acc: 60.0\n",
      "20: Train_loss: 0.35417014360427856 | Train_acc: 80.41666666666667 | Test_loss: 0.4236952066421509 | Test_acc: 83.33333333333334\n",
      "30: Train_loss: 0.1073695495724678 | Train_acc: 97.5 | Test_loss: 0.076918825507164 | Test_acc: 98.33333333333333\n",
      "40: Train_loss: 0.05329030379652977 | Train_acc: 97.5 | Test_loss: 0.015403428114950657 | Test_acc: 100.0\n",
      "50: Train_loss: 0.04449461027979851 | Train_acc: 97.91666666666666 | Test_loss: 0.010325643233954906 | Test_acc: 100.0\n",
      "60: Train_loss: 0.036408018320798874 | Train_acc: 98.75 | Test_loss: 0.03147875890135765 | Test_acc: 98.33333333333333\n",
      "70: Train_loss: 0.025289708748459816 | Train_acc: 98.75 | Test_loss: 0.006066794041544199 | Test_acc: 100.0\n",
      "80: Train_loss: 0.02285327948629856 | Train_acc: 99.16666666666667 | Test_loss: 0.0015899337595328689 | Test_acc: 100.0\n",
      "90: Train_loss: 0.019989188760519028 | Train_acc: 99.16666666666667 | Test_loss: 0.0004917517071589828 | Test_acc: 100.0\n",
      "100: Train_loss: 0.019552651792764664 | Train_acc: 99.16666666666667 | Test_loss: 0.0005374083411879838 | Test_acc: 100.0\n",
      "110: Train_loss: 0.018555274233222008 | Train_acc: 99.16666666666667 | Test_loss: 0.00048755519674159586 | Test_acc: 100.0\n",
      "120: Train_loss: 0.01800089329481125 | Train_acc: 99.16666666666667 | Test_loss: 0.0001912249281303957 | Test_acc: 100.0\n",
      "130: Train_loss: 0.017227819189429283 | Train_acc: 99.16666666666667 | Test_loss: 0.000147704835399054 | Test_acc: 100.0\n",
      "140: Train_loss: 0.019357018172740936 | Train_acc: 99.16666666666667 | Test_loss: 0.00023981096455827355 | Test_acc: 100.0\n",
      "150: Train_loss: 0.35414600372314453 | Train_acc: 93.33333333333333 | Test_loss: 0.9803314805030823 | Test_acc: 85.0\n",
      "160: Train_loss: 0.23480960726737976 | Train_acc: 94.58333333333333 | Test_loss: 0.1732284426689148 | Test_acc: 93.33333333333333\n",
      "170: Train_loss: 0.06199650838971138 | Train_acc: 97.5 | Test_loss: 0.02293076179921627 | Test_acc: 98.33333333333333\n",
      "180: Train_loss: 0.045082446187734604 | Train_acc: 98.33333333333333 | Test_loss: 0.009644676931202412 | Test_acc: 100.0\n",
      "190: Train_loss: 0.03207528963685036 | Train_acc: 99.16666666666667 | Test_loss: 0.008911040611565113 | Test_acc: 100.0\n"
     ]
    }
   ],
   "source": [
    "epoch_count=[]\n",
    "train_loss_list=[]\n",
    "test_loss_list=[]\n",
    "\n",
    "epochs=200\n",
    "\n",
    "for epoch in range(epochs):\n",
    "\n",
    "    model_5.train()\n",
    "\n",
    "    y_logits=model_5(X_train)\n",
    "    y_train_pred=torch.softmax(y_logits,dim=1).argmax(dim=1)\n",
    "\n",
    "    loss=loss_fn(y_logits,y_train)\n",
    "    accuracy_train=accuracy_fn(y_train,y_train_pred)\n",
    "\n",
    "    optim.zero_grad()\n",
    "\n",
    "    loss.backward()\n",
    "\n",
    "    optim.step()\n",
    "\n",
    "    # testing\n",
    "\n",
    "    model_5.eval()\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        y_test_logits=model_5(X_test)\n",
    "        y_test_pred=torch.softmax(y_test_logits,dim=1).argmax(dim=1)\n",
    "\n",
    "        loss_test=loss_fn(y_test_logits,y_test)\n",
    "        accuracy_test=accuracy_fn(y_test,y_test_pred)\n",
    "\n",
    "        \n",
    "    if epoch%10==0:\n",
    "        epoch_count.append(epoch)\n",
    "        train_loss_list.append(loss.detach().numpy())\n",
    "        test_loss_list.append(loss_test.detach().numpy())\n",
    "        print(f\"{epoch}: Train_loss: {loss} | Train_acc: {accuracy_train} | Test_loss: {loss_test} | Test_acc: {accuracy_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14f84d97830>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_count,train_loss_list,c='black',label=\"Training Loss\")\n",
    "plt.plot(epoch_count,test_loss_list,c='yellow',label=\"Testing Loss\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boundary(model_5,X_test,y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_opencv",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}