classifying images using transfer learning

This Notebook is broken down into multiple steps:

• load the image dataset (Oxford Flowers 102 dataset) from Tensorflow Hub and explore it, map the labels and creat a pipeline where images are resized and batched.
• Build and Train an image classifier on the dataset.
  • Loaded the MobileNet pre-trained network from TensorFlow Hub.
  • a new untrained feed-forward network as a classifier is defined
  • classifier is trained.
  • loss and accuracy during training and validation are displayed
  • make callbacks to save the best model
  • training model is saved
• use the trained model to perform inference on flower images where we preprocess our new images to fit the model's input layer.