Lab 34-35- Neural Network for Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f61242a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd508053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x22760e0f870>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef63039e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fc66075",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, \n",
    "                                                    cancer.target,\n",
    "                                                   test_size=0.2,\n",
    "                                                   random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1decae85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(455, 30)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60b6907a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 30)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1149076a",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a1d7e66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5b15802",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = torch.tensor(X_train, dtype=torch.float32, device=device)\n",
    "X_test = torch.tensor(X_test, dtype=torch.float32, device=device)\n",
    "y_train = torch.tensor(y_train, dtype=torch.float32, device=device)\n",
    "y_test = torch.tensor(y_test, dtype=torch.float32, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7cc88e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[9.0290e+00, 1.7330e+01, 5.8790e+01,  ..., 1.7500e-01, 4.2280e-01,\n",
       "         1.1750e-01],\n",
       "        [2.1090e+01, 2.6570e+01, 1.4270e+02,  ..., 2.9030e-01, 4.0980e-01,\n",
       "         1.2840e-01],\n",
       "        [9.1730e+00, 1.3860e+01, 5.9200e+01,  ..., 5.0870e-02, 3.2820e-01,\n",
       "         8.4900e-02],\n",
       "        ...,\n",
       "        [1.4290e+01, 1.6820e+01, 9.0300e+01,  ..., 3.3330e-02, 2.4580e-01,\n",
       "         6.1200e-02],\n",
       "        [1.3980e+01, 1.9620e+01, 9.1120e+01,  ..., 1.8270e-01, 3.1790e-01,\n",
       "         1.0550e-01],\n",
       "        [1.2180e+01, 2.0520e+01, 7.7220e+01,  ..., 7.4310e-02, 2.6940e-01,\n",
       "         6.8780e-02]], device='cuda:0')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "560cc7a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([455])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "694423fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.view(-1,1)\n",
    "y_test = y_test.view(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7a8520da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([455, 1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f27d3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_features = X_train.shape[1]\n",
    "outputs = y_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a1fee23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_features, outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40bbea77",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4ec0a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(nn.Module):\n",
    "    \n",
    "    def __init__(self, in_dims, out_dims):\n",
    "        super(Model, self).__init__()\n",
    "        self.fc1 = nn.Linear(in_dims, 32)\n",
    "        self.fc2 = nn.Linear(32, out_dims)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.fc1(x)\n",
    "        out = torch.relu(out)\n",
    "        out = self.fc2(out)\n",
    "        return torch.sigmoid(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb944504",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(input_features, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "12192cdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(\n",
       "  (fc1): Linear(in_features=30, out_features=32, bias=True)\n",
       "  (fc2): Linear(in_features=32, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "63998bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.0001\n",
    "iters = 1000\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "optimizer = optim.SGD(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "06cbc274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Loss: 31.460100173950195\n",
      "Epoch: 100, Loss: 1.1117026805877686\n",
      "Epoch: 200, Loss: 0.8827389478683472\n",
      "Epoch: 300, Loss: 0.6078259348869324\n",
      "Epoch: 400, Loss: 0.33753105998039246\n",
      "Epoch: 500, Loss: 0.71336430311203\n",
      "Epoch: 600, Loss: 0.24982386827468872\n",
      "Epoch: 700, Loss: 0.4410471022129059\n",
      "Epoch: 800, Loss: 0.22094951570034027\n",
      "Epoch: 900, Loss: 0.21264803409576416\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(iters):\n",
    "    y_pred = model(X_train)\n",
    "    loss = criterion(y_pred, y_train)\n",
    "    \n",
    "    loss.backward()\n",
    "    \n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    if epoch % 100 == 0:\n",
    "        print(\"Epoch: {}, Loss: {}\".format(epoch, loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "806532b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f303e3a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9473684210526315\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    y_predicted = model(X_test)\n",
    "    y_predicted = y_predicted.round()\n",
    "    \n",
    "    if torch.cuda.is_available():\n",
    "        y_predicted = y_predicted.detach().cpu().numpy()\n",
    "        \n",
    "        \n",
    "    print(f'Accuracy: {accuracy_score(y_test, y_predicted)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3887c44d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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