This code trains a convolutional neural network to recognize characters from the TV show "The Big Bang Theory". The code imports necessary libraries including TensorFlow, OpenCV, and Keras. It sets the paths to the weights and data and defines parameters for the image size, number of channels, batch size, and number of epochs. The code then creates an ImageDataGenerator for data augmentation and sets its parameters for image transformations. It generates a train and validation generator using the directories and parameters provided by the ImageDataGenerator. The number of classes is obtained from the training generator. A function is defined to create a VGG16 model with additional layers. The model is then created, trained on the training and validation data, and saved.
The VGG16 architecture is a deep convolutional neural network that was developed by the Visual Geometry Group at the University of Oxford. It consists of 16 layers, including 13 convolutional layers and 3 fully connected layers. The convolutional layers use small 3x3 filters, and the network has a very deep architecture, which allows it to learn complex features from images. The model was originally trained on the ImageNet dataset, which contains over 1 million images and 1000 classes, and achieved state-of-the-art performance on this task at the time of its development. Due to its effectiveness at feature extraction, the VGG16 architecture has been widely used as a pre-trained model for transfer learning in many computer vision applications.