ConvNeuralNet with pytorch.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OM2yUvrwhqGD"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torchvision.datasets import CIFAR10\n",
        "import torch.nn as nn \n",
        "import torch.nn.functional as F \n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from torchsummary import summary\n",
        "from torchvision.utils import make_grid\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "01e4731d58f84c68a7a66fae16d5e8d5",
            "e0f3568c637c4a6d93fe5261052aca8c",
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            "bb098efa50584a338ee7bd2227f96aaa",
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            "8da97a8992764acf87327d968df0d607"
          ]
        },
        "id": "Gfj2a94ShqGF",
        "outputId": "90890b0b-86ea-421c-fad6-3e5c674cbd7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "01e4731d58f84c68a7a66fae16d5e8d5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "tfms=transforms.Compose([transforms.ToTensor()])\n",
        "train_data=CIFAR10(root='./data',train=True,transform=tfms,download=True)\n",
        "test_data=CIFAR10(root='./data',train=False,download=True,transform=tfms)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afLjRVidhqGF",
        "outputId": "d184ce8f-f2ab-4e00-ed58-97ca271b3318"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "batch_size=32\n",
        "lr=.01\n",
        "karnal_size=3\n",
        "device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "02NxdXyBhqGG"
      },
      "outputs": [],
      "source": [
        "train_loader=DataLoader(train_data,batch_size=batch_size,shuffle=False)\n",
        "test_loader=DataLoader(test_data,batch_size=batch_size,shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_2OjKUkhqGG",
        "outputId": "fe034dea-c153-4df7-b640-9553f0c04fb6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([32, 3, 32, 32])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainiter=iter(train_loader)\n",
        "images,labels=trainiter.next()\n",
        "images.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "U6MBzzbehqGG",
        "outputId": "78f28ab3-4113-46ed-f5f3-8f607f2fa432"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f48c9449850>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow((make_grid(images).numpy().transpose((1,2,0))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hX2DjjPqhqGG"
      },
      "outputs": [],
      "source": [
        "def conv_layer(in_channels,out_channels,kernel_size):\n",
        "\n",
        "    return nn.Sequential(\n",
        "        nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=kernel_size),\n",
        "        nn.MaxPool2d(kernel_size=(2,2)),\n",
        "        nn.ReLU())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xH28PkRNhqGG"
      },
      "outputs": [],
      "source": [
        "class Conv_net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Conv_net, self).__init__()\n",
        "        self.layer1=conv_layer(3,16,3)\n",
        "        self.layer2=conv_layer(16,32,3)\n",
        "        self.layer3=conv_layer(32,64,3)\n",
        "        self.flatten=nn.Flatten()\n",
        "        self.fc1=nn.Linear(64*2*2,50)\n",
        "        self.fc2=nn.Linear(50,10)\n",
        "    def forward(self,x):\n",
        "        x=self.layer1(x)\n",
        "        x=self.layer2(x)\n",
        "        x=self.layer3(x)\n",
        "        x=self.flatten(x)\n",
        "        x=self.fc1(x)\n",
        "        x=self.fc2(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sF7J9S-BhqGH",
        "outputId": "eec20a14-8b1d-4ec0-eeaa-cc9de7e6f071"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Conv_net(\n",
              "  (layer1): Sequential(\n",
              "    (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "    (2): ReLU()\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "    (2): ReLU()\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "    (2): ReLU()\n",
              "  )\n",
              "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
              "  (fc1): Linear(in_features=256, out_features=50, bias=True)\n",
              "  (fc2): Linear(in_features=50, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model=Conv_net()\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vICLgHaJhqGH",
        "outputId": "33bd0bfd-a703-49c2-b3e0-1ccd33b25a06"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 16, 30, 30]             448\n",
            "         MaxPool2d-2           [-1, 16, 15, 15]               0\n",
            "              ReLU-3           [-1, 16, 15, 15]               0\n",
            "            Conv2d-4           [-1, 32, 13, 13]           4,640\n",
            "         MaxPool2d-5             [-1, 32, 6, 6]               0\n",
            "              ReLU-6             [-1, 32, 6, 6]               0\n",
            "            Conv2d-7             [-1, 64, 4, 4]          18,496\n",
            "         MaxPool2d-8             [-1, 64, 2, 2]               0\n",
            "              ReLU-9             [-1, 64, 2, 2]               0\n",
            "          Flatten-10                  [-1, 256]               0\n",
            "           Linear-11                   [-1, 50]          12,850\n",
            "           Linear-12                   [-1, 10]             510\n",
            "================================================================\n",
            "Total params: 36,944\n",
            "Trainable params: 36,944\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 0.24\n",
            "Params size (MB): 0.14\n",
            "Estimated Total Size (MB): 0.39\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "summary(model=model,input_size=(3,32,32))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VMaJcd6qhqGH"
      },
      "outputs": [],
      "source": [
        "loss_fn=nn.CrossEntropyLoss()\n",
        "optimizer=torch.optim.Adam(model.parameters(),lr=lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z0I5g239hqGH"
      },
      "outputs": [],
      "source": [
        "def train(mode,trainloader,loss_fn=None,optimizer=None,seed=32,EPOCHS=100):\n",
        "    torch.manual_seed(seed)\n",
        "    cost=[]\n",
        "    \n",
        "    model.to(device)\n",
        "    for epoch in range(EPOCHS):\n",
        "        train_corr=[]\n",
        "        for idx,(img,lbl) in enumerate(trainloader):\n",
        "            ##1 Compute the Output\n",
        "            img,lbl=img.to(device),lbl.to(device)\n",
        "            yhat=model(img)\n",
        "            \n",
        "            ##2 Compute the Loss\n",
        "            loss=loss_fn(yhat,lbl)\n",
        "\n",
        "            ##3 Compute the gradients\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "\n",
        "            ##4 update the model parameters\n",
        "            optimizer.step()\n",
        "            with torch.no_grad():\n",
        "                yhat=model(img)\n",
        "                loss=loss_fn(yhat,lbl)\n",
        "                core_lbl=(torch.argmax(F.softmax(yhat,dim=1),dim=1)==lbl).sum().item()\n",
        "                ##print('Model accuracy on this batch %.2f', core_lbl)\n",
        "                train_corr.append(core_lbl)\n",
        "        with torch.no_grad():\n",
        "            acc=sum(train_corr)/len(train_data)*100\n",
        "            \n",
        "            print(f'After {epoch+1} Model Training Accuracy {acc}')\n",
        "\n",
        "\n",
        "   \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRU7XIO6hqGI",
        "outputId": "bb657ce4-05d6-4711-b21a-a9dab7ed1b53"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50000"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOGRIJ8FhqGI",
        "outputId": "20cff35c-cec5-460e-9d62-6b2e3d692746"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "After 1 Model Training Accuracy 9.998\n",
            "After 2 Model Training Accuracy 9.998\n",
            "After 3 Model Training Accuracy 9.998\n",
            "After 4 Model Training Accuracy 9.998\n",
            "After 5 Model Training Accuracy 9.998\n",
            "After 6 Model Training Accuracy 9.998\n",
            "After 7 Model Training Accuracy 9.998\n",
            "After 8 Model Training Accuracy 9.998\n",
            "After 9 Model Training Accuracy 9.998\n",
            "After 10 Model Training Accuracy 9.998\n"
          ]
        }
      ],
      "source": [
        "model=Conv_net()\n",
        "cost=train(mode=model,trainloader=train_loader,loss_fn=loss_fn,optimizer=optimizer,EPOCHS=10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "hJtsfIV9ep6-",
        "outputId": "425fd64d-a625-43a5-da1d-2daef5d9f490"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Epoch [1/20], Loss: 2.0392\n",
            "Epoch [2/20], Loss: 1.7994\n",
            "Epoch [3/20], Loss: 1.1547\n",
            "Epoch [4/20], Loss: 1.1812\n",
            "Epoch [5/20], Loss: 0.7705\n",
            "Epoch [6/20], Loss: 1.5763\n",
            "Epoch [7/20], Loss: 1.4711\n",
            "Epoch [8/20], Loss: 0.9615\n",
            "Epoch [9/20], Loss: 1.1125\n",
            "Epoch [10/20], Loss: 0.6064\n",
            "Epoch [11/20], Loss: 1.5008\n",
            "Epoch [12/20], Loss: 0.7809\n",
            "Epoch [13/20], Loss: 0.9461\n",
            "Epoch [14/20], Loss: 0.5715\n",
            "Epoch [15/20], Loss: 1.0826\n",
            "Epoch [16/20], Loss: 0.6299\n",
            "Epoch [17/20], Loss: 0.9109\n",
            "Epoch [18/20], Loss: 0.7368\n",
            "Epoch [19/20], Loss: 0.6406\n",
            "Epoch [20/20], Loss: 0.1131\n"
          ]
        }
      ],
      "source": [
        "# Load in relevant libraries, and alias where appropriate\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# Define relevant variables for the ML task\n",
        "batch_size = 64\n",
        "num_classes = 10\n",
        "learning_rate = 0.001\n",
        "num_epochs = 20\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "# Use transforms.compose method to reformat images for modeling,\n",
        "# and save to variable all_transforms for later use\n",
        "all_transforms = transforms.Compose([transforms.Resize((32,32)),\n",
        "                                     transforms.ToTensor(),\n",
        "                                     transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],\n",
        "                                                          std=[0.2023, 0.1994, 0.2010])\n",
        "                                     ])\n",
        "# Create Training dataset\n",
        "train_dataset = torchvision.datasets.CIFAR10(root = './data',\n",
        "                                             train = True,\n",
        "                                             transform = all_transforms,\n",
        "                                             download = True)\n",
        "\n",
        "# Create Testing dataset\n",
        "test_dataset = torchvision.datasets.CIFAR10(root = './data',\n",
        "                                            train = False,\n",
        "                                            transform = all_transforms,\n",
        "                                            download=True)\n",
        "\n",
        "# Instantiate loader objects to facilitate processing\n",
        "train_loader = torch.utils.data.DataLoader(dataset = train_dataset,\n",
        "                                           batch_size = batch_size,\n",
        "                                           shuffle = True)\n",
        "\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset = test_dataset,\n",
        "                                           batch_size = batch_size,\n",
        "                                           shuffle = True)\n",
        "# Creating a CNN class\n",
        "class ConvNeuralNet(nn.Module):\n",
        "\t#  Determine what layers and their order in CNN object \n",
        "    def __init__(self, num_classes):\n",
        "        super(ConvNeuralNet, self).__init__()\n",
        "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)\n",
        "        self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
        "        self.max_pool1 = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
        "        \n",
        "        self.conv_layer3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3)\n",
        "        self.conv_layer4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3)\n",
        "        self.max_pool2 = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
        "        \n",
        "        self.fc1 = nn.Linear(1600, 128)\n",
        "        self.relu1 = nn.ReLU()\n",
        "        self.fc2 = nn.Linear(128, num_classes)\n",
        "    \n",
        "    # Progresses data across layers    \n",
        "    def forward(self, x):\n",
        "        out = self.conv_layer1(x)\n",
        "        out = self.conv_layer2(out)\n",
        "        out = self.max_pool1(out)\n",
        "        \n",
        "        out = self.conv_layer3(out)\n",
        "        out = self.conv_layer4(out)\n",
        "        out = self.max_pool2(out)\n",
        "                \n",
        "        out = out.reshape(out.size(0), -1)\n",
        "        \n",
        "        out = self.fc1(out)\n",
        "        out = self.relu1(out)\n",
        "        out = self.fc2(out)\n",
        "        return out\n",
        "model = ConvNeuralNet(num_classes)\n",
        "# Set Loss function with criterion\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Set optimizer with optimizer\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
        "\n",
        "total_step = len(train_loader)\n",
        "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
        "for epoch in range(num_epochs):\n",
        "\t#Load in the data in batches using the train_loader object\n",
        "    for i, (images, labels) in enumerate(train_loader):  \n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        \n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lZGhp9LhN6IG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVfGO_y6WmEN",
        "outputId": "ef08bb1c-4eb2-487a-8df1-918b78a914c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Epoch [1/20], Loss: 1.5581\n",
            "Epoch [2/20], Loss: 1.5771\n",
            "Epoch [3/20], Loss: 0.8959\n",
            "Epoch [4/20], Loss: 1.6174\n",
            "Epoch [5/20], Loss: 1.2007\n",
            "Epoch [6/20], Loss: 0.8553\n",
            "Epoch [7/20], Loss: 0.8313\n",
            "Epoch [8/20], Loss: 0.6717\n",
            "Epoch [9/20], Loss: 1.2174\n",
            "Epoch [10/20], Loss: 1.0488\n",
            "Epoch [11/20], Loss: 0.9436\n",
            "Epoch [12/20], Loss: 0.5438\n",
            "Epoch [13/20], Loss: 0.8083\n",
            "Epoch [14/20], Loss: 0.9634\n",
            "Epoch [15/20], Loss: 0.9775\n",
            "Epoch [16/20], Loss: 0.6761\n",
            "Epoch [17/20], Loss: 0.7311\n",
            "Epoch [18/20], Loss: 1.3291\n"
          ]
        }
      ],
      "source": [
        "# Load in relevant libraries, and alias where appropriate\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# Define relevant variables for the ML task\n",
        "batch_size = 64\n",
        "num_classes = 10\n",
        "learning_rate = 0.001\n",
        "num_epochs = 20\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "# Use transforms.compose method to reformat images for modeling,\n",
        "# and save to variable all_transforms for later use\n",
        "all_transforms = transforms.Compose([transforms.Resize((32,32)),\n",
        "                                     transforms.ToTensor(),\n",
        "                                     transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],\n",
        "                                                          std=[0.2023, 0.1994, 0.2010])\n",
        "                                     ])\n",
        "# Create Training dataset\n",
        "train_dataset = torchvision.datasets.CIFAR10(root = './data',\n",
        "                                             train = True,\n",
        "                                             transform = all_transforms,\n",
        "                                             download = True)\n",
        "\n",
        "# Create Testing dataset\n",
        "test_dataset = torchvision.datasets.CIFAR10(root = './data',\n",
        "                                            train = False,\n",
        "                                            transform = all_transforms,\n",
        "                                            download=True)\n",
        "\n",
        "# Instantiate loader objects to facilitate processing\n",
        "train_loader = torch.utils.data.DataLoader(dataset = train_dataset,\n",
        "                                           batch_size = batch_size,\n",
        "                                           shuffle = True)\n",
        "\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset = test_dataset,\n",
        "                                           batch_size = batch_size,\n",
        "                                           shuffle = True)\n",
        "# Creating a CNN class\n",
        "class ConvNeuralNet(nn.Module):\n",
        "\t#  Determine what layers and their order in CNN object \n",
        "    def __init__(self, num_classes):\n",
        "        super(ConvNeuralNet, self).__init__()\n",
        "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)\n",
        "        self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
        "        self.max_pool1 = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
        "        \n",
        "        self.conv_layer3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3)\n",
        "        self.conv_layer4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3)\n",
        "        self.max_pool2 = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
        "        \n",
        "        self.fc1 = nn.Linear(1600, 128)\n",
        "        self.relu1 = nn.ReLU()\n",
        "        self.fc2 = nn.Linear(128, num_classes)\n",
        "    \n",
        "    # Progresses data across layers    \n",
        "    def forward(self, x):\n",
        "        out = self.conv_layer1(x)\n",
        "        out = self.conv_layer2(out)\n",
        "        out = self.max_pool1(out)\n",
        "        \n",
        "        out = self.conv_layer3(out)\n",
        "        out = self.conv_layer4(out)\n",
        "        out = self.max_pool2(out)\n",
        "                \n",
        "        out = out.reshape(out.size(0), -1)\n",
        "        \n",
        "        out = self.fc1(out)\n",
        "        out = self.relu1(out)\n",
        "        out = self.fc2(out)\n",
        "        return out\n",
        "model = ConvNeuralNet(num_classes)\n",
        "# Set Loss function with criterion\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Set optimizer with optimizer\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
        "\n",
        "total_step = len(train_loader)\n",
        "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
        "for epoch in range(num_epochs):\n",
        "\t#Load in the data in batches using the train_loader object\n",
        "    for i, (images, labels) in enumerate(train_loader):  \n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        \n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))"
      ]
    }
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