Hands-On Transfer Learning with Python

Data representations

All input and targets in the training set for a neural network training must be represented as tensors (or multi-dimensional arrays). Tensors are actually generalizations of two-dimensional matrices to an arbitrary number of dimensions. Typically, these are floating point tensors or integer tensors. Whatever the raw input data type—image, sound, text—it should be first converted to a suitable tensor representation. This step is called data vectorization. The following are tensors of different dimensions that we will be using frequently in this book:

• Zero-D tensor or scalar: A tensor that contains one single number is called a zero-dimensional tensor, zero-dimensional tensor, or scalar.
• One-dimensional tensor or vector ...