# AlexNet Tutorial ![](/files/-MR96iXrz2g66ahAbZ07) ![](/files/-MR96iXssTZeaz6XPCEM) ## Pytorch ### Building from Scratch [Buiding AlexNet as in the original network. AlexNet Class source code](https://gist.githubusercontent.com/quanhua92/98959ac7e407b2c3e4ef7fa192ac6954/raw/c0a870200ee33a3b99dda9d20b5ee9f65d59bd6b/alexnet_lrn.py) ```python class AlexNet(nn.Module): def __init__(self, num_classes=1000): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=0), nn.ReLU(inplace=True), nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(96, 256, kernel_size=5, padding=2, groups=2), nn.ReLU(inplace=True), nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(256, 384, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(384, 384, kernel_size=3, padding=1, groups=2), nn.ReLU(inplace=True), nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), ) self.classifier = nn.Sequential( nn.Linear(256 * 6 * 6, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, num_classes), ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), 256 * 6 * 6) x = self.classifier(x) return x ``` ### Pretrained model: Pytorch [Run pretrained code: pytorch.org](https://pytorch.org/hub/pytorch_vision_alexnet/) All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`. The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]` and `std = [0.229, 0.224, 0.225]`. #### Import Pretrained AlexNet Model ```python import torch model = torch.hub.load('pytorch/vision:v0.6.0', 'alexnet', pretrained=True) model.eval() ``` #### Dataset examples ```python # Download an example image from the pytorch website import urllib url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") try: urllib.URLopener().retrieve(url, filename) except: urllib.request.urlretrieve(url, filename) ``` #### Run Inference ```python # sample execution (requires torchvision) from PIL import Image from torchvision import transforms input_image = Image.open(filename) preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model # move the input and model to GPU for speed if available if torch.cuda.is_available(): input_batch = input_batch.to('cuda') model.to('cuda') with torch.no_grad(): output = model(input_batch) # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes print(output[0]) # The output has unnormalized scores. To get probabilities, you can run a softmax on it. print(torch.nn.functional.softmax(output[0], dim=0)) ``` ## Keras ### Building from Scratch [Read this post for detail](https://towardsdatascience.com/implementing-alexnet-cnn-architecture-using-tensorflow-2-0-and-keras-2113e090ad98). [My Colab source code](https://colab.research.google.com/drive/1BjozVZ_ppmB66lnwEcVN5gqB3L61CJeX#scrollTo=kN38jAKBZ4-t) ```python model = keras.Sequential() #1st Convolutional Layer model.add(Conv2D(filters=96, input_shape=(32,32,3), kernel_size=(11,11), strides=(4,4), padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same')) #2nd Convolutional Layer model.add(Conv2D(filters=256, kernel_size=(5, 5), strides=(1,1), padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same')) #3rd Convolutional Layer model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) #4th Convolutional Layer model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) #5th Convolutional Layer model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same')) #Passing it to a Fully Connected layer model.add(Flatten()) # 1st Fully Connected Layer model.add(Dense(4096, input_shape=(32,32,3,))) model.add(BatchNormalization()) model.add(Activation('relu')) # Add Dropout to prevent overfitting model.add(Dropout(0.4)) #2nd Fully Connected Layer model.add(Dense(4096)) model.add(BatchNormalization()) model.add(Activation('relu')) #Add Dropout model.add(Dropout(0.4)) #3rd Fully Connected Layer model.add(Dense(1000)) model.add(BatchNormalization()) model.add(Activation('relu')) #Add Dropout model.add(Dropout(0.4)) #Output Layer model.add(Dense(10)) model.add(BatchNormalization()) model.add(Activation('softmax')) #Model Summary model.summary() ``` ### Pretrained Model: Keras --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://ykkim.gitbook.io/dlip/deep-learning-for-perception/example-code/alexnet-tutorial.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.