Tensors and vectors - Python for Integrated Circuits - - An Online Book - |
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Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In TensorFlow library, tensors are the building blocks as all computations are done using tensors. Google’s TensorFlow team says, "A tensor is a generalization of vectors and matrices to potentially higher dimensions. Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes." Tensors can be of two types: A vector is understood as something that has a magnitude and a direction. Without the direction of a vector, a tensor becomes a scalar value that has only magnitude. A vector is used to represent n number of things and can represent area and different attributes, among other things. If a vector is multiplyed with another vector, a scalar quantity is obtained, while if a vector is multiplyed with a scalar value, it just increases or decreases in the same proportion, in terms of its magnitude, without changing its direction. However, if a vector is multiplyed with a tensor, it then will result in a new vector that has a changed magnitude as well as a new direction. In pandas, a series object represents a vector of data. Script to output a vector: ============================================
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