" self.add_module('relu1', nn.ReLU(inplace=True)),\n", " self.add_module('norm1', nn.BatchNorm2d(num_input_features)),\n", " def _init_(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False):\n", " bottleneck_output = conv(relu(norm(concated_features)))\n", " concated_features = torch.cat(inputs, 1)\n", "def _bn_function_factory(norm, relu, conv):\n",
Nn sequential grayscale code#
"# is a derivative of the code provided at \n", "# The following code cell that implements the DenseNet-121 architecture \n", "for images, labels in valid_loader: \n", "# i.e., that they contain all classes\n", "# Check that validation set and test sets are diverse\n", "for images, labels in train_loader: \n", "# Also, the label order should be different in the second\n", "# i.e., label indices should be in random order.\n", "# Check that shuffling works properly\n", " for batch_idx, (x, y) in enumerate(train_loader):\n", "Epoch: 2 | Batch index: 0 | Batch size: 128\n" "Epoch: 1 | Batch index: 0 | Batch size: 128\n", "test_loader = DataLoader(dataset=test_dataset, \n", "valid_loader = DataLoader(dataset=valid_dataset, \n", "train_loader = DataLoader(dataset=train_dataset, \n", "valid_dataset = Subset(train_and_valid, valid_indices)\n", "train_dataset = Subset(train_and_valid, train_indices)\n", "test_dataset = datasets.MNIST(root='data', \n", "train_and_valid = datasets.MNIST(root='data', \n", "resize_transform = transforms.Compose([transforms.Resize((32, 32)),\n", In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. Densely connected convolutional networks. "- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. "Furthermore, in this particular notebook, we are considering the DenseNet-121, which is depicted below:\n", Also, ResNets skip connections work via addition\n", between every other layer (but don't connect all layers with each other).
"Note that this is somewhat related yet very different to ResNets. "The following figure illustrates the main concept of DenseNet: within each \"dense\" block, each layer is connected with each previous layer - the feature maps are concatenated.\n", "The network in this notebook is an implementation of the DenseNet-121 architecture on the MNIST digits dataset () to train a handwritten digit classifier. "%watermark -a 'Sebastian Raschka' -v -p torch" "photoUrl": "///-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg", "Deep Learning Models - A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
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