What are Symbolic Tensors in TensorFlow and Keras?
Symbolic tensors are symbolic objects used in TensorFlow and Keras which are not explicitly defined as numerical values. They are usually used in the construction of a graph, and they represent operations that will be performed on the other objects in the graph.
How are they different than other tensors?
Unlike other tensors, which have numerical values, symbolic tensors are “placeholders” for operations that will be performed on other tensors in the graph. This makes them useful for constructing complex computation graphs, as the operations can be defined ahead of time, and the values of the other tensors can be filled in later.
Why do they even exist?
Symbolic tensors exist to provide a way of constructing complex computation graphs in TensorFlow and Keras. They provide a way of defining operations ahead of time, without having to know the values of other tensors in the graph. This makes it easier to construct complex models without having to rewrite the entire graph for each new input.
Where do they come up in TensorFlow and Keras?
Symbolic tensors are used throughout TensorFlow and Keras, but they are especially important when constructing more complex models. They are used in operations such as tf.placeholder, tf.Variable, tf.constant, and tf.add, as well as in the construction of more complex neural networks.
How should we deal with them or what problems can we face when dealing with them?
Symbolic tensors can be tricky to work with, as they are not explicitly defined with numerical values. The most common problem is the _SymbolicException error, which occurs when you attempt to evaluate a symbolic tensor without first filling in the values of the other tensors in the graph. To avoid this error, make sure to always fill in the values of the other tensors before evaluating the symbolic tensor.