Flatness vs Generalization Part1-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9b6dc9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e18b41eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.ToTensor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e4219f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_set = datasets.MNIST(root='C:\\\\Users\\\\varsh\\\\dataset\\\\mnist_train', train=True, download=True, transform=transform)\n",
    "test_data_set = datasets.MNIST(root='C:\\\\Users\\\\varsh\\\\dataset\\\\mnist_test', train=False, download=True, transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecb81b56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset MNIST\n",
       "    Number of datapoints: 60000\n",
       "    Root location: C:\\Users\\varsh\\dataset\\mnist_train\n",
       "    Split: Train\n",
       "    StandardTransform\n",
       "Transform: ToTensor()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b154d153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset MNIST\n",
       "    Number of datapoints: 10000\n",
       "    Root location: C:\\Users\\varsh\\dataset\\mnist_test\n",
       "    Split: Test\n",
       "    StandardTransform\n",
       "Transform: ToTensor()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a130ff17",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_sizes = [64,1024]\n",
    "train_loader1 = DataLoader(train_data_set, batch_size=batch_sizes[0], shuffle=True)\n",
    "test_loader1 = DataLoader(test_data_set, batch_size=batch_sizes[0], shuffle=True)\n",
    "\n",
    "train_loader2 = DataLoader(train_data_set, batch_size=batch_sizes[1], shuffle=True)\n",
    "test_loader2 = DataLoader(test_data_set, batch_size=batch_sizes[1], shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cea0fb62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CalcParams(model):\n",
    "    val = sum(params.numel() for params in model.parameters() if params.requires_grad)\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f022b39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7fbb632e",
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################################   MODEL 1   ####################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebf719fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "727ad4b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model1(nn.Module):\n",
    "    def __init__(self, i=784, z2=20, z3=40, o=10):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(i,z2)    \n",
    "        self.fc2 = nn.Linear(z2, z3)\n",
    "        self.out = nn.Linear(z3, o)\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.out(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a0efa2a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=Model1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0fed4b6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16950\n"
     ]
    }
   ],
   "source": [
    "model1params=CalcParams(model1)\n",
    "print(model1params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea55a682",
   "metadata": {},
   "outputs": [],
   "source": [
    "crit_loss1 = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b128ef94",
   "metadata": {},
   "outputs": [],
   "source": [
    "optim1 = torch.optim.Adam(model1.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b24d9185",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "train_correct_mod1 = []\n",
    "test_correct_mod1 = []\n",
    "train_losses_mod1=[]\n",
    "test_losses_mod1=[]\n",
    "for i in range(epochs):\n",
    "    train_corr = 0\n",
    "    test_corr = 0\n",
    "    for b, (X_train, y_train) in enumerate(train_loader1):\n",
    "       \n",
    "        y_pred = model1(X_train.view(-1,784))  \n",
    "        trn_loss = crit_loss1(y_pred, y_train)\n",
    "\n",
    "        predicted = torch.max(y_pred.data, 1)[1]\n",
    "        batch_corr = (predicted == y_train).sum()\n",
    "        train_corr += batch_corr\n",
    "        \n",
    "        optim1.zero_grad()\n",
    "        trn_loss.backward()\n",
    "        optim1.step()\n",
    "        \n",
    "    train_losses_mod1.append(trn_loss)\n",
    "    train_correct_mod1.append(train_corr)\n",
    "        \n",
    "    with torch.no_grad():\n",
    "        for b, (X_test, y_test) in enumerate(test_loader1):\n",
    "\n",
    "            y_val = model1(X_test.view(-1,784)) \n",
    "\n",
    "            predicted = torch.max(y_val.data, 1)[1] \n",
    "            test_corr += (predicted == y_test).sum()\n",
    "    \n",
    "    test_loss = crit_loss1(y_val, y_test)\n",
    "    test_losses_mod1.append(test_loss)\n",
    "    test_correct_mod1.append(test_corr)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d538ae21",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e97afc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_losses_mod1)):\n",
    "     train_loss_list.append(train_losses_mod1[i].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4599e3eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loss_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e3c95410",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_losses_mod1)):\n",
    "     test_loss_list.append(test_losses_mod1[i].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3c5eebb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_accuracy_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98eed812",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_correct_mod1)):\n",
    "     train_accuracy_list.append(train_correct_mod1[i].detach().numpy()/600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "023798a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_accuracy_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "40889da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_correct_mod1)):\n",
    "     test_accuracy_list.append(test_correct_mod1[i].detach().numpy()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8752859a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "547c18ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################################  MODEL 2 1024  #####################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a50a23ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9bbf91f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model2(nn.Module):\n",
    "    def __init__(self, in_features=784, h2=20, h3=40, out_features=10):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(in_features,h2)    \n",
    "        self.fc2 = nn.Linear(h2, h3)\n",
    "        self.out = nn.Linear(h3, out_features)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.out(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e7a1bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=Model2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "22b63d97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16950\n"
     ]
    }
   ],
   "source": [
    "model2params=CalcParams(model2)\n",
    "print(model2params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "100d0618",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model2(\n",
       "  (fc1): Linear(in_features=784, out_features=20, bias=True)\n",
       "  (fc2): Linear(in_features=20, out_features=40, bias=True)\n",
       "  (out): Linear(in_features=40, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f7e7099c",
   "metadata": {},
   "outputs": [],
   "source": [
    "crit_loss2 = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "95fc183c",
   "metadata": {},
   "outputs": [],
   "source": [
    "optim2 = torch.optim.Adam(model2.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e0359e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "train_losses_mod2=[]\n",
    "test_losses_mod2=[]\n",
    "train_correct_mod2 = []\n",
    "test_correct_mod2 = []\n",
    "for i in range(epochs):\n",
    "    train_corr = 0\n",
    "    test_corr = 0\n",
    "\n",
    "    for b, (X_train, y_train) in enumerate(train_loader2):\n",
    "       \n",
    "        y_pred = model2(X_train.view(-1,784))  \n",
    "        loss = crit_loss2(y_pred, y_train)\n",
    "\n",
    "        predicted = torch.max(y_pred.data, 1)[1]\n",
    "        batch_corr = (predicted == y_train).sum()\n",
    "        train_corr += batch_corr\n",
    "        \n",
    "        optim2.zero_grad()\n",
    "        loss.backward()\n",
    "        optim2.step()\n",
    "        \n",
    "    train_losses_mod2.append(loss)\n",
    "    train_correct_mod2.append(train_corr)\n",
    "        \n",
    "    with torch.no_grad():\n",
    "        for b, (X_test, y_test) in enumerate(test_loader2):\n",
    "\n",
    "            y_val = model2(X_test.view(-1,784)) \n",
    "\n",
    "            predicted = torch.max(y_val.data, 1)[1] \n",
    "            test_corr += (predicted == y_test).sum()\n",
    "    \n",
    "    loss = crit_loss2(y_val, y_test)\n",
    "    test_losses_mod2.append(loss)\n",
    "    test_correct_mod2.append(test_corr)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "66ef772b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_losses_mod2)):\n",
    "     train_loss_list.append(train_losses_mod2[i].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "efdaee1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_losses_mod2)):\n",
    "     test_loss_list.append(test_losses_mod2[i].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fc359aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_correct_mod2)):\n",
    "     train_accuracy_list.append(train_correct_mod2[i].detach().numpy()/600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "873ac6fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_correct_mod2)):\n",
    "     test_accuracy_list.append(test_correct_mod2[i].detach().numpy()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ae228804",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpa=np.linspace(-1.0, 2.0, num=20)\n",
    "thetaArr=[]\n",
    "for i in range(len(alpa)):\n",
    "    theta=(1-alpa[i])*model1params+alpa[i]*model2params\n",
    "    thetaArr.append(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b6fa6c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25d3d8e93a0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1= plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(alpa, train_loss_list, 'g', label='Train')\n",
    "ax1.plot(alpa, test_loss_list, 'g--', label='Test')\n",
    "ax2.plot(alpa, train_accuracy_list, 'b', label='accuracy')\n",
    "ax2.plot(alpa, test_accuracy_list, 'b--', label='accuracy')\n",
    "ax1.set_title('Loss and accuracy to the number of interpolation ratio lr=0.001. ')\n",
    "ax1.set_xlabel('alpha')\n",
    "ax1.set_ylabel('loss', color='b')\n",
    "ax2.set_ylabel('accuracy', color='r')\n",
    "ax1.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deb18dd4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "acab074d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################   Model lr=0.01   ################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d050dd8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cee7466d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelOne(nn.Module):\n",
    "    def __init__(self, in_features=784, h2=20, h3=40, out_features=10):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(in_features,h2)    \n",
    "        self.fc2 = nn.Linear(h2, h3)\n",
    "        self.out = nn.Linear(h3, out_features)\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.out(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8e2e052f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_one=ModelOne()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "009581d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "crit_loss_one = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4455d466",
   "metadata": {},
   "outputs": [],
   "source": [
    "optim_one = torch.optim.Adam(model_one.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "316e2c56",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "train_correct_mod1 = []\n",
    "test_correct_mod1 = []\n",
    "train_losses_mod1=[]\n",
    "test_losses_mod1=[]\n",
    "\n",
    "for i in range(epochs):\n",
    "    train_corr = 0\n",
    "    test_corr = 0\n",
    "\n",
    "    for b, (X_train, y_train) in enumerate(train_loader1):\n",
    "       \n",
    "        y_pred = model_one(X_train.view(-1,784))  \n",
    "        trn_loss = crit_loss_one(y_pred, y_train)\n",
    "\n",
    "        predicted = torch.max(y_pred.data, 1)[1]\n",
    "        batch_corr = (predicted == y_train).sum()\n",
    "        train_corr += batch_corr\n",
    "\n",
    "        optim_one.zero_grad()\n",
    "        trn_loss.backward()\n",
    "        optim_one.step()\n",
    "\n",
    "    train_losses_mod1.append(trn_loss)\n",
    "    train_correct_mod1.append(train_corr)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for b, (X_test, y_test) in enumerate(test_loader1):\n",
    "\n",
    "            y_val = model_one(X_test.view(-1,784)) \n",
    "\n",
    "            predicted = torch.max(y_val.data, 1)[1] \n",
    "            test_corr += (predicted == y_test).sum()\n",
    "\n",
    "    test_loss = crit_loss_one(y_val, y_test)\n",
    "    test_losses_mod1.append(test_loss)\n",
    "    test_correct_mod1.append(test_corr)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8a3540dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cda4d33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_losses_mod1)):\n",
    "     train_loss_list.append(train_losses_mod1[i].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b497003a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loss_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3a3bc851",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_losses_mod1)):\n",
    "     test_loss_list.append(test_losses_mod1[i].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "951c3346",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_accuracy_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ef65005a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_correct_mod1)):\n",
    "     train_accuracy_list.append(train_correct_mod1[i].detach().numpy()/600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9a7a9c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_accuracy_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "19b000ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_correct_mod1)):\n",
    "     test_accuracy_list.append(test_correct_mod1[i].detach().numpy()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f1ecec36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-Model 2lr=0.01-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a781ae0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelTwo(nn.Module):\n",
    "    def __init__(self, in_features=784, h2=20, h3=40, out_features=10):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(in_features,h2)    \n",
    "        self.fc2 = nn.Linear(h2, h3)\n",
    "        self.out = nn.Linear(h3, out_features) \n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.out(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d4e5fd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_two=ModelTwo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "172e8f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "crit_loss_two = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b2199155",
   "metadata": {},
   "outputs": [],
   "source": [
    "optim_two = torch.optim.Adam(model_two.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "83d9aabb",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "train_correct_mod2 = []\n",
    "test_correct_mod2 = []\n",
    "train_losses_mod2=[]\n",
    "test_losses_mod2=[]\n",
    "\n",
    "for i in range(epochs):\n",
    "    train_corr = 0\n",
    "    test_corr = 0\n",
    "\n",
    "    for b, (X_train, y_train) in enumerate(train_loader2):\n",
    "       \n",
    "        y_pred = model_two(X_train.view(-1,784))  \n",
    "        trn_loss = crit_loss_two(y_pred, y_train)\n",
    "\n",
    "        predicted = torch.max(y_pred.data, 1)[1]\n",
    "        batch_corr = (predicted == y_train).sum()\n",
    "        train_corr += batch_corr\n",
    "\n",
    "        optim_two .zero_grad()\n",
    "        trn_loss.backward()\n",
    "        optim_two .step()\n",
    "\n",
    "    train_losses_mod2.append(trn_loss)\n",
    "    train_correct_mod2.append(train_corr)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for b, (X_test, y_test) in enumerate(test_loader2):\n",
    "\n",
    "            y_val = model_two(X_test.view(-1,784)) \n",
    "\n",
    "            predicted = torch.max(y_val.data, 1)[1] \n",
    "            test_corr += (predicted == y_test).sum()\n",
    "\n",
    "    test_loss = crit_loss_two(y_val, y_test)\n",
    "    test_losses_mod2.append(test_loss)\n",
    "    test_correct_mod2.append(test_corr)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d1cd2d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_losses_mod2)):\n",
    "     train_loss_list.append(train_losses_mod2[i].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "32797119",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_losses_mod2)):\n",
    "     test_loss_list.append(test_losses_mod2[i].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "346fa80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_correct_mod2)):\n",
    "     train_accuracy_list.append(train_correct_mod2[i].detach().numpy()/600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0e7af287",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_correct_mod2)):\n",
    "     test_accuracy_list.append(test_correct_mod2[i].detach().numpy()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2f219909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25d3d7a4880>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(alpa, train_loss_list, 'g', label='Train')\n",
    "ax1.plot(alpa, test_loss_list, 'g--', label='Test')\n",
    "ax2.plot(alpa, train_accuracy_list, 'b', label='accuracy')\n",
    "ax2.plot(alpa, test_accuracy_list, 'b--', label='accuracy')\n",
    "ax1.set_title('Loss and accuracy to the number of interpolation ratio lr=0.01. ')\n",
    "ax1.set_xlabel('alpha')\n",
    "ax1.set_ylabel('loss', color='b')\n",
    "ax2.set_ylabel('accuracy', color='r')\n",
    "ax1.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8666c679",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}