Arda Karaman CENG501 - Spring2022 - PA2 - Task 2 - CNN with Pytorch.ipynb

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Task 2: CNN with PyTorch\n","# CENG501 - Spring 2022 - PA2\n","\n","In this task, you will implement a convolutional neural network (CNN) with PyTorch. \n"]},{"cell_type":"markdown","metadata":{"id":"oRaKrjO5JSu-"},"source":["## 1 Import the Modules\n","\n","Let us start with importing some libraries that we will use throughout the task."]},{"cell_type":"code","metadata":{"id":"v4L5nogMKyNx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654380253081,"user_tz":-180,"elapsed":10351,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"825a10f2-aca7-41e6-a0d6-cacfb529f423"},"source":["import matplotlib.pyplot as plt # For plotting\n","import numpy as np              # NumPy, for working with arrays/tensors \n","import time                     # For measuring time\n","import random                   # Python's random library\n","\n","# PyTorch libraries:\n","import torch\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","# install and import the torchinfo library\n","!pip install torchinfo\n","from torchinfo import summary\n","\n","\n","%matplotlib inline\n","plt.rcParams['figure.figsize'] = [12, 8]\n","plt.rcParams['figure.dpi'] = 100 # 200 e.g. is really fine, but slower"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting torchinfo\n","  Downloading torchinfo-1.7.0-py3-none-any.whl (22 kB)\n","Installing collected packages: torchinfo\n","Successfully installed torchinfo-1.7.0\n"]}]},{"cell_type":"markdown","metadata":{"id":"4BXVQOGRI4Sc"},"source":["### 1.1 Enable GPU\n","\n","From \"Edit -> Notebook Settings -> Hardware accelerator\" select GPU. With the following we will specify to PyTorch that we want to use the GPU."]},{"cell_type":"code","metadata":{"id":"97DJEyArJLcR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654380261472,"user_tz":-180,"elapsed":371,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"b9d6f4a1-a010-4047-ae0c-cbc36cb28025"},"source":["if torch.cuda.is_available():\n","  print(\"Cuda (GPU support) is available and enabled!\")\n","  device = torch.device(\"cuda\")\n","else:\n","  print(\"Cuda (GPU support) is not available :(\")\n","  device = torch.device(\"cpu\")"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Cuda (GPU support) is available and enabled!\n"]}]},{"cell_type":"markdown","metadata":{"id":"UMmi17e-JX7o"},"source":["## 2 The Dataset\n","\n","We will use torchvision.datasets to download the CIFAR10 dataset. "]},{"cell_type":"code","metadata":{"id":"MlXFbhR8Aq0m","colab":{"base_uri":"https://localhost:8080/","height":100,"referenced_widgets":["1b498fcdd6fc4fd39de89e57596eca41","cd9c580ea59447ea858c5188e69bd65b","c34ce8c200e34a35b53f05ed6b5c3e48","e42b5d1457db4c48a4307afc8754abf0","314282e0757245c6a2596713599ba1ef","6a0b7f6b1f084ae58adb0f9c0a8aafdc","81c7344dfddc4a029c4d0dbd666035b9","64030338d66c4571bd55adeb1517857b","c3c6f9f9d8fc4b8c93a170aa294237bc","8749f4558c8347188b3d842b7be3cf01","2c0d2abc93814733a0c39fb0c31a6290"]},"executionInfo":{"status":"ok","timestamp":1654380269258,"user_tz":-180,"elapsed":5999,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"c867f5ef-535b-4ad3-e985-3d512ced76ea"},"source":["transform = transforms.Compose(\n","    [transforms.ToTensor(),\n","     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","\n","batch_size = 128\n","\n","trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n","                                        download=True, transform=transform)\n","trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                          shuffle=True, num_workers=2)\n","\n","testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n","                                       download=True, transform=transform)\n","testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                         shuffle=False, num_workers=2)\n","\n","classes = ('plane', 'car', 'bird', 'cat',\n","           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"],"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/170498071 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1b498fcdd6fc4fd39de89e57596eca41"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Extracting ./data/cifar-10-python.tar.gz to ./data\n","Files already downloaded and verified\n"]}]},{"cell_type":"markdown","metadata":{"id":"0i9tug4jGGg4"},"source":["### 2.1 Visualize Samples"]},{"cell_type":"code","metadata":{"id":"aiLTjQoKBIpQ","colab":{"base_uri":"https://localhost:8080/","height":264},"executionInfo":{"status":"ok","timestamp":1654380276525,"user_tz":-180,"elapsed":1520,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"3dad6ea0-c84b-4022-86df-1a963fae7ee0"},"source":["dataiter = iter(trainloader)\n","classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n","S = 4\n","\n","for i in range(S):\n","  for j in range(S):\n","    images, labels = dataiter.next()\n","    X = np.transpose(images[0].numpy()/2+0.5, (1, 2, 0))\n","    y = labels[0]\n","\n","    plt.subplot(S, S, i*S+j+1)\n","    plt.imshow(X)\n","    plt.axis('off')\n","    plt.title(classes[y])\n","    plt.subplots_adjust(hspace = 0.5)\n","\n","plt.show()"],"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 16 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"kUmdAA1QJvRx"},"source":["## 4 Define and Train a Small CNN Model\n","\n","Now, all the pieces are ready and we can define a model."]},{"cell_type":"markdown","metadata":{"id":"fJq7aT_wh9SK"},"source":["### 4.1 Model Definition\n","\n","Create a three-layer CNN with the following layers:\n","\n","| Layer Name | Input HxW | Filter size | Stride | Pad | # of in channels | Out HxW | # of out channels | \n","| ----| -----| ----| ---| ---| ----| -----|---------- |\n","| Conv1   | 32x32 | ? | ? | ? | 3  | 28x28 | 16 |\n","| Conv2   | 28x28 | ? | ? | ? | 16 | 26x26 | 32 |\n","| Maxpool | 26x26 | 4 | 2 | 0 | 32 | 12x12 | 32 |\n","| Conv3   | 12x12 | ? | ? | ? | 32 | 10x10 | 32 |\n","\n","and the fully-connected layers:\n","\n","| Layer Name | Input Size | Output size | \n","| ----| -----| ----| \n","| FC1 | 3200 | 1500 |\n","| FC2 | 1500 | 10 | \n","\n","You should choose suitable values for variables marked with `?' in the table and make sure that receptive fields can be properly placed in all layers. \n","\n","While creating your model, pay attention to the following aspects:\n","* Each Conv layer and FC layer will be followed by ReLU, except for the last one.\n","* You should keep all other parameters (dilation, bias, group-mode, ..) as their default values in Pytorch.\n","\n","You will need to read the following pages from Pytorch regarding the layers that you will use:\n","* [Conv2d](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)\n","* [MaxPool2d](https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d)"]},{"cell_type":"code","metadata":{"id":"n13wrSZTiDEp","executionInfo":{"status":"ok","timestamp":1654383081798,"user_tz":-180,"elapsed":422,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}}},"source":["import torch.nn as nn\n","import torch.nn.functional as F\n","\n","class SmallCNN(nn.Module):\n","    def __init__(self):\n","        super(SmallCNN, self).__init__()\n","        torch.manual_seed(501)\n","        random.seed(501)\n","        np.random.seed(501)       \n","        self.conv1 = None\n","        self.conv2 = None\n","        self.conv3 = None\n","        self.maxpool = None\n","        self.fc1 = None\n","        self.fc2 = None\n","        ###########################################################\n","        # @TODO: Create the convolutional and FC layers as        #\n","        #  described above.                                       #\n","        ###########################################################\n","        self.conv1 = nn.Conv2d(3, 16, (5,5))\n","        self.conv2 = nn.Conv2d(16, 32, (3,3))\n","        self.conv3 = nn.Conv2d(32, 32, (3,3))\n","        self.maxpool = nn.MaxPool2d(4, 2)\n","        self.fc1 = nn.Linear(3200, 1500)\n","        self.fc2 = nn.Linear(1500, 10)\n","        ###########################################################\n","        #                         END OF YOUR CODE                #\n","        ###########################################################\n","\n","    def forward(self, x):\n","      ###########################################################\n","      # @TODO: Feedforward x through the layers. Note that x    #\n","      # needs to be reshaped to (batchsize, 3200) before        #\n","      # the FC layers.                                          #\n","      ###########################################################\n","\n","      x = F.relu(self.conv1(x))\n","\n","      x = F.relu(self.conv2(x))\n","\n","      x = self.maxpool(x)\n","\n","      x = self.conv3(x)\n","\n","      x = x.view(x.size(0), -1)\n","\n","      x = self.fc1(x)\n","  \n","      x = self.fc2(x)\n","\n","      ###########################################################\n","      #                         END OF YOUR CODE                #\n","      ###########################################################\n","      return x"],"execution_count":40,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CvpiVF5uiBE9"},"source":["### 4.2 Trainer for the Model\n","\n","Let us define our training function, which will use the cuda device for training the model."]},{"cell_type":"code","metadata":{"id":"5Flgr2C6Xb2s","executionInfo":{"status":"ok","timestamp":1654382912960,"user_tz":-180,"elapsed":470,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}}},"source":["def train(model, criterion, optimizer, epochs, dataloader, verbose=True):\n","  \"\"\"\n","    Define the trainer function. We can use this for training any model.\n","    The parameter names are self-explanatory.\n","\n","    Returns: the loss history.\n","  \"\"\"\n","  loss_history = [] \n","  for epoch in range(epochs):\n","    for i, data in enumerate(dataloader, 0):    \n","      \n","      # Our batch:\n","      inputs, labels = data\n","      inputs = inputs.to(device)\n","      labels = labels.to(device)\n","\n","      # zero the gradients as PyTorch accumulates them\n","      optimizer.zero_grad()\n","\n","      # Obtain the scores\n","      outputs = model(inputs)\n","\n","      # Calculate loss\n","      loss = criterion(outputs.to(device), labels)\n","\n","      # Backpropagate\n","      loss.backward()\n","\n","      # Update the weights\n","      optimizer.step()\n","\n","      loss_history.append(loss.item())\n","    \n","    if verbose: print(f'Epoch {epoch} / {epochs}: avg. loss of last 5 iterations {np.sum(loss_history[:-6:-1])/5}')\n","\n","  return loss_history"],"execution_count":38,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"L_QLjjxUIkKs"},"source":["### 4.3 Create and visualize the model"]},{"cell_type":"code","metadata":{"id":"jIHLBpztIeWa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654383084924,"user_tz":-180,"elapsed":371,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"46fcdc5c-a68d-42b8-e7a5-379423bc8f13"},"source":["model = SmallCNN()\n","\n","summary(model, input_size=(batch_size, 3, 32, 32))"],"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["==========================================================================================\n","Layer (type:depth-idx)                   Output Shape              Param #\n","==========================================================================================\n","SmallCNN                                 [128, 10]                 --\n","├─Conv2d: 1-1                            [128, 16, 28, 28]         1,216\n","├─Conv2d: 1-2                            [128, 32, 26, 26]         4,640\n","├─MaxPool2d: 1-3                         [128, 32, 12, 12]         --\n","├─Conv2d: 1-4                            [128, 32, 10, 10]         9,248\n","├─Linear: 1-5                            [128, 1500]               4,801,500\n","├─Linear: 1-6                            [128, 10]                 15,010\n","==========================================================================================\n","Total params: 4,831,614\n","Trainable params: 4,831,614\n","Non-trainable params: 0\n","Total mult-adds (G): 1.26\n","==========================================================================================\n","Input size (MB): 1.57\n","Forward/backward pass size (MB): 39.82\n","Params size (MB): 19.33\n","Estimated Total Size (MB): 60.72\n","=========================================================================================="]},"metadata":{},"execution_count":41}]},{"cell_type":"markdown","metadata":{"id":"0r8pT7Y9FmDH"},"source":["### 4.4 Train the Small Model\n","\n","We will create an instance of our model and \"define\" which loss function we want to use. We will also state our choice for the optimizer here.\n","\n","For more information, check the PyTorch docs: [CrossEntropyLoss](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) and [SGD](https://pytorch.org/docs/stable/optim.html#torch.optim.SGD)."]},{"cell_type":"code","metadata":{"id":"8Gg_Xp3vrg42","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654383192961,"user_tz":-180,"elapsed":103540,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"bac06f61-2ca8-412f-c12d-11bf36cb98a5"},"source":["\n","import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n","\n","model = model.to(device)\n","epochs = 10\n","loss_history = train(model, criterion, optimizer, epochs, trainloader)"],"execution_count":42,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 0 / 10: avg. loss of last 5 iterations 2.0268855810165407\n","Epoch 1 / 10: avg. loss of last 5 iterations 1.821582555770874\n","Epoch 2 / 10: avg. loss of last 5 iterations 1.6378780841827392\n","Epoch 3 / 10: avg. loss of last 5 iterations 1.4911758661270142\n","Epoch 4 / 10: avg. loss of last 5 iterations 1.4284263134002686\n","Epoch 5 / 10: avg. loss of last 5 iterations 1.326482343673706\n","Epoch 6 / 10: avg. loss of last 5 iterations 1.265813374519348\n","Epoch 7 / 10: avg. loss of last 5 iterations 1.2452037572860717\n","Epoch 8 / 10: avg. loss of last 5 iterations 1.2477300643920899\n","Epoch 9 / 10: avg. loss of last 5 iterations 1.2341881036758422\n"]}]},{"cell_type":"markdown","metadata":{"id":"DILBn6y92Ceg"},"source":["### 4.5 The Loss Curve\n","\n","Let us visualize the loss curve."]},{"cell_type":"code","metadata":{"id":"bNZBSrzXiVVe","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1654383198431,"user_tz":-180,"elapsed":357,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"8f3867b9-c5c4-43c2-c03c-7626e21f69a1"},"source":["plt.plot(loss_history)\n","plt.xlabel('Iteration number')\n","plt.ylabel('Loss value')\n","plt.show()"],"execution_count":43,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"KPMf0jfF2zo6"},"source":["### 4.6 Quantitative Analysis\n","\n","We can analyze the accuracy of the predictions as follows. You should see around 54\\% accuracies. We can finetune the hyperparameters to obtain better results. But we will skip that and go for a bigger model.\n","\n","*Disclaimer: This code piece is taken from PyTorch examples.*"]},{"cell_type":"code","metadata":{"id":"5PAV1QQw218J","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654383212315,"user_tz":-180,"elapsed":10095,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"cafc271f-7f0d-44ac-e368-cedaffc96555"},"source":["correct = 0\n","total = 0\n","model = model.to(\"cpu\")\n","with torch.no_grad():\n","    for data in testloader:\n","        images, labels = data\n","        outputs = model(images)\n","        _, predicted = torch.max(outputs.data, 1)\n","        total += labels.size(0)\n","        correct += (predicted == labels).sum().item()\n","\n","print('Accuracy of the network on the 10000 test images: %d %%' % (\n","    100 * correct / total))"],"execution_count":44,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy of the network on the 10000 test images: 55 %\n"]}]},{"cell_type":"markdown","metadata":{"id":"M4AJXtea2427"},"source":["## 5 Your CNN\n","\n","Now, create your own CNN. It should have at least 5 convolutional layers. Other than that, there is no restriction on what you can use in your CNN or how you can structure it. \n"]},{"cell_type":"markdown","metadata":{"id":"Dr7NvUrWW9ax"},"source":["### 5.1 Model Definition"]},{"cell_type":"code","metadata":{"id":"qmBmxHGoVE_F","executionInfo":{"status":"ok","timestamp":1654384386266,"user_tz":-180,"elapsed":456,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}}},"source":["import torch.nn as nn\n","import torch.nn.functional as F\n","\n","class YourCNN(nn.Module):\n","    def __init__(self):\n","        super(YourCNN, self).__init__()\n","        torch.manual_seed(501)\n","        random.seed(501)\n","        np.random.seed(501)     \n","        self.conv1 = None\n","        self.conv2 = None\n","        self.conv3 = None\n","        self.conv4 = None\n","        self.conv5 = None\n","        self.maxpool = None\n","        self.fc1 = None\n","        self.fc2 = None  \n","        ###########################################################\n","        # @TODO: Create your layers here.                         #\n","        ###########################################################\n","        self.conv1 = nn.Conv2d(3, 16, (5,5))\n","        self.conv2 = nn.Conv2d(16, 32, (3,3))\n","        self.conv3 = nn.Conv2d(32, 32, (3,3))\n","        self.conv4 = nn.Conv2d(32, 32, (1,1))\n","        self.conv5 = nn.Conv2d(32, 32, (1,1))\n","        self.maxpool = nn.MaxPool2d(4, 2)\n","        self.fc1 = nn.Linear(3200, 1500)\n","        self.fc2 = nn.Linear(1500, 10)\n","        ###########################################################\n","        #                         END OF YOUR CODE                #\n","        ###########################################################\n","\n","    def forward(self, x):\n","      ###########################################################\n","      # @TODO: Feedforward x through the layers.                #\n","      ###########################################################\n","      x = F.relu(self.conv1(x))\n","\n","      x = F.relu(self.conv2(x))\n","\n","      x = self.maxpool(x)\n","\n","      x = F.relu(self.conv3(x))\n","\n","      x = F.relu(self.conv4(x))\n","\n","      x = self.conv5(x)\n","\n","      x = x.view(x.size(0), -1)\n","\n","      x = self.fc1(x)\n","  \n","      x = self.fc2(x)\n","      ###########################################################\n","      #                         END OF YOUR CODE                #\n","      ###########################################################\n","      return x"],"execution_count":61,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OjayBqpCXBJG"},"source":["### 5.2 Create and visuale your model"]},{"cell_type":"code","metadata":{"id":"MfE6ztl-Vsxr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654384388543,"user_tz":-180,"elapsed":290,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"b19699ac-93da-4f4f-e350-994a938dc6c6"},"source":["model = YourCNN()\n","\n","summary(model, input_size=(batch_size, 3, 32, 32))"],"execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":["==========================================================================================\n","Layer (type:depth-idx)                   Output Shape              Param #\n","==========================================================================================\n","YourCNN                                  [128, 10]                 --\n","├─Conv2d: 1-1                            [128, 16, 28, 28]         1,216\n","├─Conv2d: 1-2                            [128, 32, 26, 26]         4,640\n","├─MaxPool2d: 1-3                         [128, 32, 12, 12]         --\n","├─Conv2d: 1-4                            [128, 32, 10, 10]         9,248\n","├─Conv2d: 1-5                            [128, 32, 10, 10]         1,056\n","├─Conv2d: 1-6                            [128, 32, 10, 10]         1,056\n","├─Linear: 1-7                            [128, 1500]               4,801,500\n","├─Linear: 1-8                            [128, 10]                 15,010\n","==========================================================================================\n","Total params: 4,833,726\n","Trainable params: 4,833,726\n","Non-trainable params: 0\n","Total mult-adds (G): 1.29\n","==========================================================================================\n","Input size (MB): 1.57\n","Forward/backward pass size (MB): 46.37\n","Params size (MB): 19.33\n","Estimated Total Size (MB): 67.28\n","=========================================================================================="]},"metadata":{},"execution_count":62}]},{"cell_type":"markdown","metadata":{"id":"AQw2uB8kXFu-"},"source":["### 5.3 Train the Model"]},{"cell_type":"code","metadata":{"id":"M_fNyLKWV5kg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654384497511,"user_tz":-180,"elapsed":105880,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"f7dfab17-9ee3-4099-fc0a-4b3d26796ac8"},"source":["import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n","\n","model = model.to(device)\n","epochs = 10\n","loss_history = train(model, criterion, optimizer, epochs, trainloader)"],"execution_count":63,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 0 / 10: avg. loss of last 5 iterations 2.3028661727905275\n","Epoch 1 / 10: avg. loss of last 5 iterations 2.290854740142822\n","Epoch 2 / 10: avg. loss of last 5 iterations 2.0382882833480833\n","Epoch 3 / 10: avg. loss of last 5 iterations 1.994442844390869\n","Epoch 4 / 10: avg. loss of last 5 iterations 1.8422358512878418\n","Epoch 5 / 10: avg. loss of last 5 iterations 1.6841199398040771\n","Epoch 6 / 10: avg. loss of last 5 iterations 1.6605027198791504\n","Epoch 7 / 10: avg. loss of last 5 iterations 1.5576799392700196\n","Epoch 8 / 10: avg. loss of last 5 iterations 1.5360093355178832\n","Epoch 9 / 10: avg. loss of last 5 iterations 1.4459476232528687\n"]}]},{"cell_type":"markdown","metadata":{"id":"2KD1t5l6WpzE"},"source":["### 5.4 Loss Curve\n","\n","Let us visualize the loss curve."]},{"cell_type":"code","metadata":{"id":"XOALtgdcWsHj","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1654384631564,"user_tz":-180,"elapsed":419,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"4fd58d0f-612e-46a5-817b-d46b3f07f66b"},"source":["plt.plot(loss_history)\n","plt.xlabel('Iteration number')\n","plt.ylabel('Loss value')\n","plt.show()"],"execution_count":64,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"XRPl6GSAW1mz"},"source":["### 5.5 Quantitative Analysis\n","\n","Analyze your model quantitatively.\n","\n","*Disclaimer: This code piece is taken from PyTorch examples.*"]},{"cell_type":"code","metadata":{"id":"QM1Fin7mW2B0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654384643232,"user_tz":-180,"elapsed":8356,"user":{"displayName":"Arda Karaman","userId":"09649945184885111594"}},"outputId":"3cd228e4-ad95-44ba-e960-2343adff96bd"},"source":["correct = 0\n","total = 0\n","model = model.to(\"cpu\")\n","with torch.no_grad():\n","    for data in testloader:\n","        images, labels = data\n","        outputs = model(images)\n","        _, predicted = torch.max(outputs.data, 1)\n","        total += labels.size(0)\n","        correct += (predicted == labels).sum().item()\n","\n","print('Accuracy of the network on the 10000 test images: %d %%' % (\n","    100 * correct / total))"],"execution_count":65,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy of the network on the 10000 test images: 45 %\n"]}]}]}