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Developing and training the AlexNet model using Tensorflow on CIFAR-10 dataset

1. import libraries import tensorflow as tf from tensorflow.keras import datasets, layers, models, optimizers, regularizers 2. Load and preprocess CIFAR-10 dataset (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() train_images, test_images = train_images / 255.0, test_images / 255.0 num_train = int(len(train_images) * 0.8) train_images, validation_images = t..
AI, ML 2023. 4. 11. 02:20
Developing and training the AlexNet model using PyTorch on the CIFAR-10 dataset

PyTorch model code based on "ImageNet Classification with Deep Convolutional Neural Networks" paper 1. Library import import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms from tqdm import tqdm 2. AlexNet Network class AlexNet(nn.Module): def __init__(self): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.C..
AI, ML 2023. 4. 10. 01:10
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