SAiDL_CV_self.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lM9KsArYy-Yw"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision.transforms.functional as f\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision\n",
        "from torch.utils.data import DataLoader,Dataset,random_split\n",
        "import PIL\n",
        "import random\n",
        "if (torch.cuda.is_available()):\n",
        "  device = torch.device('cuda')\n",
        "else:\n",
        "  device = torch.device('cpu')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZWNFzvKiPmt"
      },
      "source": [
        "class Network(nn.Module):\n",
        "    def __init__(self):   \n",
        "        super().__init__()\n",
        "        self.l1 = nn.Conv2d(3,32,3,padding = 1)\n",
        "        self.n1 = nn.BatchNorm2d(32)\n",
        "        self.l2 = nn.Conv2d(32,32,3,padding = 1)\n",
        "        self.n2 = nn.BatchNorm2d(32)\n",
        "        self.l3 = nn.Conv2d(32,64,3,padding = 1)\n",
        "        self.n3 = nn.BatchNorm2d(64)\n",
        "        self.l4 = nn.Conv2d(64,64,3,padding = 1)\n",
        "        self.n4 = nn.BatchNorm2d(64)\n",
        "        self.l5 = nn.Conv2d(64,128,3,padding = 1)\n",
        "        self.n5 = nn.BatchNorm2d(128)\n",
        "        self.l6 = nn.Conv2d(128,128,3,padding = 1)\n",
        "        self.n6 = nn.BatchNorm2d(128)\n",
        "        self.l7 = nn.Conv2d(128,128,3,padding = 1)\n",
        "        self.n7 = nn.BatchNorm2d(128)\n",
        "        self.l8 = nn.Conv2d(128,256,3,padding = 1)\n",
        "        self.n8 = nn.BatchNorm2d(256)\n",
        "        self.l9 = nn.Conv2d(256,256,3,padding = 1)\n",
        "        self.n9 = nn.BatchNorm2d(256)\n",
        "        self.l10 = nn.Linear(9216,1024)\n",
        "        self.l11 = nn.Linear(1024,1024)\n",
        "        self.c = nn.Sequential(\n",
        "            nn.Linear(9216,10),\n",
        "            nn.Softmax(dim = 1)\n",
        "        )\n",
        "        self.relu = nn.ReLU()\n",
        "        self.drop = nn.Dropout(p = 0.3)\n",
        "        self.drop2d = nn.Dropout2d(p = 0.3)\n",
        "        self.softmax = nn.Softmax()\n",
        "        self.pool = nn.MaxPool2d(2,stride = 2)\n",
        "    def forward(self,xt,flag = False):\n",
        "      x = xt\n",
        "      convert = torch.zeros((x.shape[0],32-x.shape[1],x.shape[2],x.shape[3]))\n",
        "      convert = convert.to(device)\n",
        "      xt = torch.cat([xt,convert],dim = 1)\n",
        "      x = self.l1(x)\n",
        "      x = self.n1(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.drop2d(x)\n",
        "      x = self.l2(x)\n",
        "      x = x+xt\n",
        "      x = self.n2(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.pool(x)\n",
        "      xt = x\n",
        "      x = self.l3(x)\n",
        "      x = self.n3(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.drop2d(x)\n",
        "      x = self.l4(x)\n",
        "      convert = torch.zeros(xt.shape,device=device)\n",
        "      xt = torch.cat([xt,convert],dim = 1)\n",
        "      x = x + torch.reshape(xt,x.shape)\n",
        "      x = self.n4(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.pool(x)\n",
        "      xt = x\n",
        "      x = self.l5(x)\n",
        "      x = self.n5(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.drop2d(x)\n",
        "      x = self.l6(x)\n",
        "      convert = torch.zeros(xt.shape,device=device)\n",
        "      xt = torch.cat([xt,convert],dim = 1)\n",
        "      x = x + torch.reshape(xt,x.shape)\n",
        "      x = self.n6(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.drop2d(x)\n",
        "      x = self.pool(x)\n",
        "      xt = x\n",
        "      x = self.l7(x)\n",
        "      x = self.n7(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.drop2d(x)\n",
        "      x = self.l8(x)\n",
        "      convert = torch.zeros(xt.shape,device=device)\n",
        "      xt = torch.cat([xt,convert],dim = 1)\n",
        "      x = x + torch.reshape(xt,x.shape)\n",
        "      x = self.n8(x)\n",
        "      x = self.relu(x)\n",
        "      x = self.pool(x)\n",
        "      x = self.l9(x)\n",
        "      x = self.n9(x)\n",
        "      x = self.relu(x)\n",
        "      x = torch.reshape(x,(x.shape[0],-1,1,1))\n",
        "      x = torch.squeeze(x,dim = 2)\n",
        "      x = torch.squeeze(x,dim = 2)\n",
        "      if flag:\n",
        "        x = self.l10(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.l11(x)\n",
        "      else:\n",
        "        x = self.c(x)\n",
        "      return x\n",
        "model = Network()\n",
        "model = model.to(device)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQCGzTrmHZYB"
      },
      "source": [
        "class Unsoupdata(Dataset):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    transform = transforms.ToTensor()\n",
        "    self.t = transforms.Compose([\n",
        "                            transforms.RandomVerticalFlip(0.5),\n",
        "                            transforms.RandomHorizontalFlip(0.5),\n",
        "                            transforms.RandomApply([transforms.ColorJitter()],p=0.5)\n",
        "    ])\n",
        "    self.data = torchvision.datasets.STL10(root = '/',split = 'unlabeled',download = True,transform = transform)\n",
        "    self.n = len(self.data)\n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "  def __getitem__(self,i):\n",
        "    decide = random.randint(0,self.n-1)\n",
        "    while (decide==i):    \n",
        "      decide = random.randint(0,self.n-1)\n",
        "    return self.data[i][0],self.t(self.data[i][0]),self.data[decide][0],self.t(self.data[decide][0])\n",
        "\n",
        "class Soupdata(Dataset):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    transform = transforms.ToTensor()\n",
        "    self.data = torchvision.datasets.STL10(root = '/',split = 'train',download = True,transform = transform)\n",
        "    self.n = len(self.data)\n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "  \n",
        "  def __getitem__(self,i):\n",
        "    return self.data[i]"
      ],
      "execution_count": 3,
      "outputs": []
    },
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        "outputId": "b9002029-88ab-4ddf-e241-8fb3fc0c959d"
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      "source": [
        "batch_size = 128\n",
        "unsoup = Unsoupdata()\n",
        "unsouploader = DataLoader(dataset=unsoup,batch_size = 128,shuffle = True)\n",
        "soup =  Soupdata()\n",
        "train_data,test_val = random_split(soup,[len(soup)-500,500])\n",
        "test_data = torchvision.datasets.STL10(root = '/',split = 'test',download = True,transform = transforms.ToTensor())\n",
        "souptrain = DataLoader(dataset=train_data,batch_size = batch_size,shuffle = True)\n",
        "soupval = DataLoader(dataset=test_val,batch_size = len(test_val),shuffle = True)\n",
        "souptest = DataLoader(dataset=test_data,batch_size = len(test_data),shuffle = True)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz to /stl10_binary.tar.gz\n"
          ],
          "name": "stdout"
        },
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              "  0%|          | 0/2640397119 [00:00<?, ?it/s]"
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        {
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          "text": [
            "Extracting /stl10_binary.tar.gz to /\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
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        }
      ]
    },
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      "source": [
        "coste = nn.TripletMarginLoss()\n",
        "costc = nn.CrossEntropyLoss()\n",
        "sim = []\n",
        "def loss(y1,y2,y3,y4,t = 1,eps = 1e-8):\n",
        "  siml = nn.CosineSimilarity(dim = 1)\n",
        "  positive = siml(y1,y2)*siml(y3,y4)\n",
        "  negative = siml(y1,y3)+siml(y1,y4)+siml(y2,y3)+siml(y2,y4)\n",
        "  negative = negative**2\n",
        "  positive = torch.exp(positive)/t\n",
        "  negative = torch.exp(negative)/t\n",
        "  los = -1*torch.log(positive/negative)\n",
        "  los = los.mean()\n",
        "  return los"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIinezqgc6Ga"
      },
      "source": [
        "optimizerc = torch.optim.Adam(model.c.parameters(),lr = 0.001)\n",
        "optimizere = torch.optim.Adam(model.parameters(),lr = 0.001)\n",
        "def test(data):\n",
        "    c = 0\n",
        "    s = 0\n",
        "    for i,(x,y) in enumerate(data):\n",
        "        with torch.no_grad():\n",
        "            x =x.to(device)\n",
        "            y = y.to(device,dtype = torch.int64)\n",
        "            yt = model(x,False)\n",
        "            yt = torch.argmax(yt, dim= 1)\n",
        "            c = (y == yt).sum()\n",
        "            s = y.shape[0]\n",
        "        break\n",
        "    return (100*c/s).item()"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kCT1lYNNfD2w",
        "outputId": "b8f44ecf-d4d2-4dd0-889f-80854b636d2a"
      },
      "source": [
        "epochs = 10\n",
        "for j in range(epochs):\n",
        "  for i,(x1,x2,x3,x4) in enumerate(unsouploader) :\n",
        "    x1 = x1.to(device)\n",
        "    x2 = x2.to(device)\n",
        "    x3 = x3.to(device)\n",
        "    x4 = x4.to(device)\n",
        "    y1 = model(x1,True)\n",
        "    y2 = model(x2,True)\n",
        "    y3 = model(x3,True)\n",
        "    y4 = model(x4,True)\n",
        "    losse = loss(y1,y2,y3,y4)          \n",
        "    optimizere.zero_grad()\n",
        "    losse.backward()\n",
        "    optimizere.step()\n",
        "    print(f'epoch {j+1} step {i} loss {losse}')\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "epoch 1 step 389 loss 0.46476173400878906\n",
            "epoch 1 step 390 loss 0.612053394317627\n",
            "epoch 1 step 391 loss 0.6649968028068542\n",
            "epoch 1 step 392 loss 0.447976678609848\n",
            "epoch 1 step 393 loss 0.6037282943725586\n",
            "epoch 1 step 394 loss 0.9113625288009644\n",
            "epoch 1 step 395 loss 0.5033005475997925\n",
            "epoch 1 step 396 loss 0.5262120962142944\n",
            "epoch 1 step 397 loss 0.5894442796707153\n",
            "epoch 1 step 398 loss 0.5562207698822021\n",
            "epoch 1 step 399 loss 0.48075544834136963\n",
            "epoch 1 step 400 loss 0.5172708630561829\n",
            "epoch 1 step 401 loss 0.5605555772781372\n",
            "epoch 1 step 402 loss 0.4943723678588867\n",
            "epoch 1 step 403 loss 0.486528217792511\n",
            "epoch 1 step 404 loss 0.6501578092575073\n",
            "epoch 1 step 405 loss 0.3585273027420044\n",
            "epoch 1 step 406 loss 0.5527539849281311\n",
            "epoch 1 step 407 loss 0.5702358484268188\n",
            "epoch 1 step 408 loss 0.4440802037715912\n",
            "epoch 1 step 409 loss 0.5926536917686462\n",
            "epoch 1 step 410 loss 0.6106722950935364\n",
            "epoch 1 step 411 loss 0.5288556814193726\n",
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            "epoch 1 step 413 loss 0.5963495969772339\n",
            "epoch 1 step 414 loss 0.4989657998085022\n",
            "epoch 1 step 415 loss 0.5908526182174683\n",
            "epoch 1 step 416 loss 0.5660568475723267\n",
            "epoch 1 step 417 loss 0.5517035722732544\n",
            "epoch 1 step 418 loss 0.4445658326148987\n",
            "epoch 1 step 419 loss 0.3863821029663086\n",
            "epoch 1 step 420 loss 0.4972836971282959\n",
            "epoch 1 step 421 loss 0.5850299000740051\n",
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          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-a4e6d8402f4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mx4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0my2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0my3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0my4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-18-baaacc860196>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, xt, flag)\u001b[0m\n\u001b[1;32m     34\u001b[0m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m       \u001b[0mconvert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m       \u001b[0mconvert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m       \u001b[0mxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOQLMXUdM4zV",
        "outputId": "41d0ea6b-4408-442b-c0b0-4223cdc58a44"
      },
      "source": [
        "epochs = 40\n",
        "for j in range(epochs):\n",
        "   for i,(xt,yt) in enumerate(souptrain) :\n",
        "    xt = xt.to(device)\n",
        "    #xt = torch.reshape(xt,[xt.shape[0],-1,1,1])\n",
        "    yt = yt.to(device)\n",
        "    #xt = torch.squeeze(xt,dim = 2)\n",
        "    #xt = torch.squeeze(xt,dim = 2)\n",
        "    y_pred = model(xt,False)\n",
        "    y_pred = torch.squeeze(y_pred)\n",
        "    lossc = costc(y_pred,yt)          \n",
        "    optimizerc.zero_grad()\n",
        "    lossc.backward()\n",
        "    optimizerc.step()\n",
        "    if (i%20==0):\n",
        "      acc = test(soupval)\n",
        "      print(f'epoch {j+1} step {i} loss {lossc} test_accuracy {acc} train_accuracy {test(souptrain)}')\n",
        "    else:\n",
        "      print(f'epoch {j+1} step {i} loss {lossc}')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1 step 0 loss 2.308993101119995 test_accuracy 10.800000190734863 train_accuracy 8.59375\n",
            "epoch 1 step 1 loss 2.305510997772217\n",
            "epoch 1 step 2 loss 2.252635955810547\n",
            "epoch 1 step 3 loss 2.3051791191101074\n",
            "epoch 1 step 4 loss 2.3354108333587646\n",
            "epoch 1 step 5 loss 2.2735636234283447\n",
            "epoch 1 step 6 loss 2.3025429248809814\n",
            "epoch 1 step 7 loss 2.2513794898986816\n",
            "epoch 1 step 8 loss 2.227808952331543\n",
            "epoch 1 step 9 loss 2.310154676437378\n",
            "epoch 1 step 10 loss 2.2273454666137695\n",
            "epoch 1 step 11 loss 2.2776827812194824\n",
            "epoch 1 step 12 loss 2.2411158084869385\n",
            "epoch 1 step 13 loss 2.2005467414855957\n",
            "epoch 1 step 14 loss 2.2553844451904297\n",
            "epoch 1 step 15 loss 2.270887613296509\n",
            "epoch 1 step 16 loss 2.252403497695923\n",
            "epoch 1 step 17 loss 2.2104296684265137\n",
            "epoch 1 step 18 loss 2.2278342247009277\n",
            "epoch 1 step 19 loss 2.2626090049743652\n",
            "epoch 1 step 20 loss 2.2274460792541504 test_accuracy 20.0 train_accuracy 20.3125\n",
            "epoch 1 step 21 loss 2.211432695388794\n",
            "epoch 1 step 22 loss 2.296591281890869\n",
            "epoch 1 step 23 loss 2.197084903717041\n",
            "epoch 1 step 24 loss 2.2311453819274902\n",
            "epoch 1 step 25 loss 2.191375255584717\n",
            "epoch 1 step 26 loss 2.255065441131592\n",
            "epoch 1 step 27 loss 2.2101094722747803\n",
            "epoch 1 step 28 loss 2.234955310821533\n",
            "epoch 1 step 29 loss 2.2161877155303955\n",
            "epoch 1 step 30 loss 2.329285144805908\n",
            "epoch 1 step 31 loss 2.24908709526062\n",
            "epoch 1 step 32 loss 2.177198648452759\n",
            "epoch 1 step 33 loss 2.201646566390991\n",
            "epoch 1 step 34 loss 2.2383768558502197\n",
            "epoch 1 step 35 loss 2.3566346168518066\n",
            "epoch 2 step 0 loss 2.2339680194854736 test_accuracy 16.400001525878906 train_accuracy 22.65625\n",
            "epoch 2 step 1 loss 2.2191555500030518\n",
            "epoch 2 step 2 loss 2.288710117340088\n",
            "epoch 2 step 3 loss 2.1933441162109375\n",
            "epoch 2 step 4 loss 2.186598062515259\n",
            "epoch 2 step 5 loss 2.217607021331787\n",
            "epoch 2 step 6 loss 2.2158496379852295\n",
            "epoch 2 step 7 loss 2.207341194152832\n",
            "epoch 2 step 8 loss 2.1906180381774902\n",
            "epoch 2 step 9 loss 2.2441787719726562\n",
            "epoch 2 step 10 loss 2.205310583114624\n",
            "epoch 2 step 11 loss 2.2138521671295166\n",
            "epoch 2 step 12 loss 2.2317545413970947\n",
            "epoch 2 step 13 loss 2.204723596572876\n",
            "epoch 2 step 14 loss 2.1953208446502686\n",
            "epoch 2 step 15 loss 2.232746124267578\n",
            "epoch 2 step 16 loss 2.2392871379852295\n",
            "epoch 2 step 17 loss 2.243600606918335\n",
            "epoch 2 step 18 loss 2.2292351722717285\n",
            "epoch 2 step 19 loss 2.2422752380371094\n",
            "epoch 2 step 20 loss 2.188084363937378 test_accuracy 18.400001525878906 train_accuracy 23.4375\n",
            "epoch 2 step 21 loss 2.2024359703063965\n",
            "epoch 2 step 22 loss 2.229219913482666\n",
            "epoch 2 step 23 loss 2.223423480987549\n",
            "epoch 2 step 24 loss 2.233177661895752\n",
            "epoch 2 step 25 loss 2.225684881210327\n",
            "epoch 2 step 26 loss 2.2158422470092773\n",
            "epoch 2 step 27 loss 2.2018394470214844\n",
            "epoch 2 step 28 loss 2.179847240447998\n",
            "epoch 2 step 29 loss 2.31135892868042\n",
            "epoch 2 step 30 loss 2.1921348571777344\n",
            "epoch 2 step 31 loss 2.2924113273620605\n",
            "epoch 2 step 32 loss 2.2362873554229736\n",
            "epoch 2 step 33 loss 2.2560901641845703\n",
            "epoch 2 step 34 loss 2.2403151988983154\n",
            "epoch 2 step 35 loss 2.2810463905334473\n",
            "epoch 3 step 0 loss 2.2107582092285156 test_accuracy 19.200000762939453 train_accuracy 24.21875\n",
            "epoch 3 step 1 loss 2.263732433319092\n",
            "epoch 3 step 2 loss 2.250183582305908\n",
            "epoch 3 step 3 loss 2.221924066543579\n",
            "epoch 3 step 4 loss 2.2669012546539307\n",
            "epoch 3 step 5 loss 2.1976540088653564\n",
            "epoch 3 step 6 loss 2.2316908836364746\n",
            "epoch 3 step 7 loss 2.1619155406951904\n",
            "epoch 3 step 8 loss 2.2505416870117188\n",
            "epoch 3 step 9 loss 2.236656665802002\n",
            "epoch 3 step 10 loss 2.238571882247925\n",
            "epoch 3 step 11 loss 2.195157527923584\n",
            "epoch 3 step 12 loss 2.195765733718872\n",
            "epoch 3 step 13 loss 2.2258057594299316\n",
            "epoch 3 step 14 loss 2.2496488094329834\n",
            "epoch 3 step 15 loss 2.2308387756347656\n",
            "epoch 3 step 16 loss 2.2106144428253174\n",
            "epoch 3 step 17 loss 2.19541072845459\n",
            "epoch 3 step 18 loss 2.2230849266052246\n",
            "epoch 3 step 19 loss 2.209548234939575\n",
            "epoch 3 step 20 loss 2.224548578262329 test_accuracy 19.80000114440918 train_accuracy 19.53125\n",
            "epoch 3 step 21 loss 2.191005229949951\n",
            "epoch 3 step 22 loss 2.2277557849884033\n",
            "epoch 3 step 23 loss 2.290951728820801\n",
            "epoch 3 step 24 loss 2.225731134414673\n",
            "epoch 3 step 25 loss 2.2126245498657227\n",
            "epoch 3 step 26 loss 2.204853057861328\n",
            "epoch 3 step 27 loss 2.2178144454956055\n",
            "epoch 3 step 28 loss 2.1825883388519287\n",
            "epoch 3 step 29 loss 2.2313060760498047\n",
            "epoch 3 step 30 loss 2.208678722381592\n",
            "epoch 3 step 31 loss 2.2567813396453857\n",
            "epoch 3 step 32 loss 2.2124290466308594\n",
            "epoch 3 step 33 loss 2.244906187057495\n",
            "epoch 3 step 34 loss 2.2036666870117188\n",
            "epoch 3 step 35 loss 2.3321399688720703\n",
            "epoch 4 step 0 loss 2.24448561668396 test_accuracy 19.0 train_accuracy 24.21875\n",
            "epoch 4 step 1 loss 2.2918198108673096\n",
            "epoch 4 step 2 loss 2.2300171852111816\n",
            "epoch 4 step 3 loss 2.235455274581909\n",
            "epoch 4 step 4 loss 2.164525032043457\n",
            "epoch 4 step 5 loss 2.1899614334106445\n",
            "epoch 4 step 6 loss 2.2407708168029785\n",
            "epoch 4 step 7 loss 2.2534828186035156\n",
            "epoch 4 step 8 loss 2.2077364921569824\n",
            "epoch 4 step 9 loss 2.2687180042266846\n",
            "epoch 4 step 10 loss 2.2525510787963867\n",
            "epoch 4 step 11 loss 2.2066116333007812\n",
            "epoch 4 step 12 loss 2.228111982345581\n",
            "epoch 4 step 13 loss 2.2072596549987793\n",
            "epoch 4 step 14 loss 2.2048065662384033\n",
            "epoch 4 step 15 loss 2.1955623626708984\n",
            "epoch 4 step 16 loss 2.2262163162231445\n",
            "epoch 4 step 17 loss 2.203727960586548\n",
            "epoch 4 step 18 loss 2.2162137031555176\n",
            "epoch 4 step 19 loss 2.2672219276428223\n",
            "epoch 4 step 20 loss 2.200268268585205 test_accuracy 21.000001907348633 train_accuracy 25.78125\n",
            "epoch 4 step 21 loss 2.2726762294769287\n",
            "epoch 4 step 22 loss 2.136617660522461\n",
            "epoch 4 step 23 loss 2.2120742797851562\n",
            "epoch 4 step 24 loss 2.177035093307495\n",
            "epoch 4 step 25 loss 2.1949851512908936\n",
            "epoch 4 step 26 loss 2.2325828075408936\n",
            "epoch 4 step 27 loss 2.223130941390991\n",
            "epoch 4 step 28 loss 2.2063634395599365\n",
            "epoch 4 step 29 loss 2.249643087387085\n",
            "epoch 4 step 30 loss 2.201760768890381\n",
            "epoch 4 step 31 loss 2.133894920349121\n",
            "epoch 4 step 32 loss 2.271358013153076\n",
            "epoch 4 step 33 loss 2.2119522094726562\n",
            "epoch 4 step 34 loss 2.174870014190674\n",
            "epoch 4 step 35 loss 2.22733998298645\n",
            "epoch 5 step 0 loss 2.235511064529419 test_accuracy 21.400001525878906 train_accuracy 21.875\n",
            "epoch 5 step 1 loss 2.2497472763061523\n",
            "epoch 5 step 2 loss 2.1885251998901367\n",
            "epoch 5 step 3 loss 2.259967803955078\n",
            "epoch 5 step 4 loss 2.1587512493133545\n",
            "epoch 5 step 5 loss 2.2688241004943848\n",
            "epoch 5 step 6 loss 2.2368581295013428\n",
            "epoch 5 step 7 loss 2.2395551204681396\n",
            "epoch 5 step 8 loss 2.1734731197357178\n",
            "epoch 5 step 9 loss 2.167499303817749\n",
            "epoch 5 step 10 loss 2.2471601963043213\n",
            "epoch 5 step 11 loss 2.2657856941223145\n",
            "epoch 5 step 12 loss 2.194704294204712\n",
            "epoch 5 step 13 loss 2.2680530548095703\n",
            "epoch 5 step 14 loss 2.2371158599853516\n",
            "epoch 5 step 15 loss 2.1882829666137695\n",
            "epoch 5 step 16 loss 2.23665452003479\n",
            "epoch 5 step 17 loss 2.1611220836639404\n",
            "epoch 5 step 18 loss 2.1562414169311523\n",
            "epoch 5 step 19 loss 2.196096658706665\n",
            "epoch 5 step 20 loss 2.255654811859131 test_accuracy 21.000001907348633 train_accuracy 28.125\n",
            "epoch 5 step 21 loss 2.172517776489258\n",
            "epoch 5 step 22 loss 2.2219772338867188\n",
            "epoch 5 step 23 loss 2.197627544403076\n",
            "epoch 5 step 24 loss 2.186566114425659\n",
            "epoch 5 step 25 loss 2.230778694152832\n",
            "epoch 5 step 26 loss 2.1922214031219482\n",
            "epoch 5 step 27 loss 2.186277389526367\n",
            "epoch 5 step 28 loss 2.219911575317383\n",
            "epoch 5 step 29 loss 2.2017581462860107\n",
            "epoch 5 step 30 loss 2.2591161727905273\n",
            "epoch 5 step 31 loss 2.21770977973938\n",
            "epoch 5 step 32 loss 2.2600698471069336\n",
            "epoch 5 step 33 loss 2.226557731628418\n",
            "epoch 5 step 34 loss 2.2325446605682373\n",
            "epoch 5 step 35 loss 2.230109691619873\n",
            "epoch 6 step 0 loss 2.2140111923217773 test_accuracy 20.200000762939453 train_accuracy 26.5625\n",
            "epoch 6 step 1 loss 2.2226603031158447\n",
            "epoch 6 step 2 loss 2.145594596862793\n",
            "epoch 6 step 3 loss 2.226547956466675\n",
            "epoch 6 step 4 loss 2.221017837524414\n",
            "epoch 6 step 5 loss 2.1967318058013916\n",
            "epoch 6 step 6 loss 2.2102549076080322\n",
            "epoch 6 step 7 loss 2.1612637042999268\n",
            "epoch 6 step 8 loss 2.1820318698883057\n",
            "epoch 6 step 9 loss 2.222938060760498\n",
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            "epoch 16 step 20 loss 2.149474620819092 test_accuracy 23.400001525878906 train_accuracy 30.46875\n",
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            "epoch 17 step 0 loss 2.1469240188598633 test_accuracy 23.200000762939453 train_accuracy 28.125\n",
            "epoch 17 step 1 loss 2.2233736515045166\n",
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            "epoch 26 step 20 loss 2.136625051498413 test_accuracy 28.60000228881836 train_accuracy 33.59375\n",
            "epoch 26 step 21 loss 2.165893077850342\n",
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            "epoch 27 step 0 loss 2.1138288974761963 test_accuracy 25.80000114440918 train_accuracy 28.125\n",
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            "epoch 27 step 20 loss 2.146890878677368 test_accuracy 28.80000114440918 train_accuracy 38.28125\n",
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            "epoch 28 step 0 loss 2.205561637878418 test_accuracy 27.000001907348633 train_accuracy 28.90625\n",
            "epoch 28 step 1 loss 2.0910470485687256\n",
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            "epoch 28 step 20 loss 2.1583304405212402 test_accuracy 26.400001525878906 train_accuracy 32.8125\n",
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            "epoch 29 step 0 loss 2.1719441413879395 test_accuracy 28.80000114440918 train_accuracy 24.21875\n",
            "epoch 29 step 1 loss 2.153677463531494\n",
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            "epoch 29 step 20 loss 2.153334856033325 test_accuracy 29.000001907348633 train_accuracy 37.5\n",
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            "epoch 30 step 0 loss 2.174482822418213 test_accuracy 27.400001525878906 train_accuracy 28.90625\n",
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            "epoch 40 step 0 loss 2.2043514251708984 test_accuracy 26.80000114440918 train_accuracy 24.21875\n",
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            "epoch 40 step 19 loss 2.1542210578918457\n",
            "epoch 40 step 20 loss 2.157867193222046 test_accuracy 29.60000228881836 train_accuracy 36.71875\n",
            "epoch 40 step 21 loss 2.172908067703247\n",
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            "epoch 40 step 29 loss 2.136636734008789\n",
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            "epoch 40 step 31 loss 2.1505074501037598\n",
            "epoch 40 step 32 loss 2.154644727706909\n",
            "epoch 40 step 33 loss 2.1235241889953613\n",
            "epoch 40 step 34 loss 2.1446895599365234\n",
            "epoch 40 step 35 loss 2.1900391578674316\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbzN9Iu9rph5",
        "outputId": "20b47e94-30c0-4e81-cd3a-369e5fc833c5"
      },
      "source": [
        "souptest = DataLoader(dataset=test_data,batch_size = 64,shuffle = True)\n",
        "def teste(data):\n",
        "    c = 0\n",
        "    s = 0\n",
        "    for x,y in (data):\n",
        "        with torch.no_grad():\n",
        "            x =x.to(device)\n",
        "            y = y.to(device)\n",
        "            yt = model(x)\n",
        "            yt = torch.squeeze(yt)\n",
        "            yt = torch.argmax(yt, dim= 1)\n",
        "            c += (y == yt).sum()\n",
        "            s += y.shape[0]\n",
        "    return (100*c/s).item()\n",
        "\n",
        "teste(souptest)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.400001525878906"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    }
  ]
}