\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# Dummy dataset\nX = torch.rand((100, 10))\ny = torch.randint(0, 2, (100,))\n\n# Define the model\nclass SimpleModel(nn.Module):\n def __init__(self):\n super(SimpleModel, self).__init__()\n self.fc1 = nn.Linear(10, 5)\n self.relu = nn.ReLU()\n self.fc2 = nn.Linear(5, 2)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.relu(x)\n x = self.fc2(x)\n return x\n\n# Instantiate the model, loss function, and optimizer\nmodel = SimpleModel()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# Training loop\nepochs = 100\nfor epoch in range(epochs):\n # Forward pass\n outputs = model(X)\n loss = criterion(outputs, y)\n\n # Backward pass and optimization\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n # Print the loss every 10 epochs\n if (epoch + 1) % 10 == 0:\n print(f'Epoch [{epoch + 1}\/{epochs}], Loss: {loss.item():.4f}')\n\n# Test the trained model\nwith torch.no_grad():\n test_outputs = model(X)\n predicted_classes = torch.argmax(test_outputs, dim=1)\n\n accuracy = torch.sum(predicted_classes == y).item() \/ len(y)\n print(f'Test Accuracy: {accuracy * 100:.2f}%')\n\n<\/pre><\/div>\n\n\nIn the above code, the torch.nn module in PyTorch helps in the process of creation, training and testing a simple neural network using PyTorch’s ‘torch.nn’ module. This code can be modified into more complex neural networks. Let us look at the output.<\/p>\n\n\n\ntorch.nn Output<\/em><\/strong><\/figcaption><\/figure>\n\n\n\nExample 2: Convolutional Neural Network (CNN)<\/h3>\n\n\n\n Let us look at another Python code regarding torch.nn module of Python programming language.<\/p>\n\n\n
\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\n\n# Define a simple Convolutional Neural Network (CNN)\nclass CNN(nn.Module):\n def __init__(self):\n super(CNN, self).__init__()\n self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)\n self.relu = nn.ReLU()\n self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n self.fc1 = nn.Linear(64 * 7 * 7, 128)\n self.fc2 = nn.Linear(128, 10)\n\n def forward(self, x):\n x = self.conv1(x)\n x = self.relu(x)\n x = self.pool(x)\n x = self.conv2(x)\n x = self.relu(x)\n x = self.pool(x)\n x = x.view(-1, 64 * 7 * 7)\n x = self.fc1(x)\n x = self.relu(x)\n x = self.fc2(x)\n return x\n\n# Load the MNIST dataset and apply transformations\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\ntrain_dataset = torchvision.datasets.MNIST(root='.\/data', train=True, transform=transform, download=True)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n\n# Instantiate the CNN, define loss function, and choose an optimizer\nmodel = CNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# Training loop\nepochs = 5\nfor epoch in range(epochs):\n total_loss = 0.0\n for images, labels in train_loader:\n optimizer.zero_grad() # Zero the gradients\n outputs = model(images) # Forward pass\n loss = criterion(outputs, labels) # Compute the loss\n loss.backward() # Backward pass\n optimizer.step() # Update the weights\n\n total_loss += loss.item() * images.size(0)\n\n average_loss = total_loss \/ len(train_loader.dataset)\n print(f'Epoch [{epoch + 1}\/{epochs}], Loss: {average_loss:.4f}')\n\nprint('Training finished.')\n\n# Save the trained model (optional)\ntorch.save(model.state_dict(), 'mnist_cnn_model.pth')\n<\/pre><\/div>\n\n\nIn the example above, we have constructed a CNN (Convolutional neural network) and train it as well. Let us look at the output.<\/p>\n\n\n\nCNN Using Torch.nn <\/em><\/strong><\/figcaption><\/figure>\n\n\n\nConclusion<\/h2>\n\n\n\n torch.nn is a fundamental module in PyTorch that empowers developers to build and train neural networks efficiently. With a rich set of pre-defined layers, loss functions, and optimization algorithms, it simplifies the process of creating complex models. Whether you’re building a simple feedforward network or a sophisticated CNN, torch.nn has you covered. <\/p>\n\n\n\n
So, go ahead and experiment with different architectures and hyperparameters to unleash the full potential of your neural networks. The possibilities are endless with torch.nn!<\/p>\n\n\n\n
Recommended: What Is Cross Entropy In Python?<\/a><\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"PyTorch is a popular Python library that helps all deep learning enthusiasts. The torch.nn module is a very important component of PyTorch which helps with the building and training of neural networks. In this article, we will take a deep dive into the torch.nn module, its key components, and the implementation of the module in […]<\/p>\n","protected":false},"author":80,"featured_media":63907,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-61060","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python-modules"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/61060","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/users\/80"}],"replies":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/comments?post=61060"}],"version-history":[{"count":0,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/61060\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media\/63907"}],"wp:attachment":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media?parent=61060"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/categories?post=61060"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/tags?post=61060"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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