December 2017
Intermediate to advanced
536 pages
14h 23m
English
Content preview from TensorFlow 1.x Deep Learning Cookbook
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Working with constants, variables, and placeholders
TensorFlow in the simplest terms provides a library to define and perform different mathematical operations with tensors. A tensor is basically an n-dimensional matrix. All types of data, that is, scalar, vectors, and matrices are special types of tensors:
|
Types of data |
Tensor |
Shape |
|
Scalar |
0-D Tensor |
[] |
|
Vector |
1-D Tensor |
[D0] |
|
Matrix |
2-D Tensor |
[D0,D1] |
|
Tensors |
N-D Tensor |
[D0,D1,....Dn-1] |
TensorFlow supports three types of tensors:
- Constants
- Variables
- Placeholders
Constants: Constants are the tensors whose values cannot be changed.
Variables: We use variable tensors when the values require updating within a session. For example, in the case of ...
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