Using a pre-trained model that is trained on huge datasets like ImageNet, COCO, etc. we can quickly specialize these architectures to work for our unique dataset. This process is termed as transfer learning. Pre-trained models for image classification and object detection tasks are usually trained on fixed input image sizes. These typically range from 224x224x3 to somewhere around 512x512x3 and mostly have an aspect ratio of 1 i.e. the width and height of the image are equal. If they are not equal then the images are resized to be of equal height and width.
The 2.2M parameters in MobileNet are frozen, but there are 1.3K trainable parameters in the dense layers. You need to apply the sigmoid activation function in the final neurons to ouput a probability score for each genre apart. By doing so, you are relying on multiple logistic regressions to train simultaneously inside the same model. Every final neuron will act as a seperate binary classifier for one single class, even though the features extracted are common to all final neurons. When generating predictions with this model, you should expect an independant probability score for each genre and that all probability scores do not necessarily sum up to 1. This is different from using a softmax layer in multi-class classification where the sum of probability scores in the output is equal to 1.
You can easily create learning curves for your deep learning models. First, you must update your call to the fit function to include reference to a validation dataset. This is a portion of the training set not used to fit the model, and is instead used to evaluate the performance of the model during training.
A good deep learning model has a carefully carved architecture. It needs enormous training data, effective hardware, skilled developers, and a vast amount of time to train and hyper-tune the model to achieve satisfactory performance. Therefore, building a deep learning model from scratch and training is practically impossible for every deep learning task. Here comes the power of Transfer Learning. Transfer Learning is the approach of making use of an already trained model for a related task.