Tensorflow signature (TF签名)is a function that maps inputs and outputs. Tensorflow has two types of signatures, function and callable. Callable is the one you create with tf. a function decorator and can be used as a function or layer in Keras. You can use input_signature to specify inputs of a callable while inout_signature must have the same number of arguments as the Python function decorated with tf. function decorator when they are supplied positionally instead of keyword arguments
Tensorflow signature is a function that maps inputs and outputs
A Tensorflow signature (tf签)is a function that maps inputs and outputs. It can be used as a function or a layer in Keras. When using it as a function, you specify an input tensor and get back its output tensor:
Tensorflow has two types of signatures, function and callable
There are two types of signatures in Tensorflow.
The callable signature is used for creating functions.
The function signature is used for creating layers.
Callable is the one you create with it. function decorator
While there are several ways to create a callable object in TensorFlow, cf. the function is the most common way to do so. When you pass an anonymous function (a function without a name) that takes input tensors and produces an output tensor to tf. function, it returns a wrapper object that remembers all the variables from the original function and their values when it was called by someone else later on. This makes it easy for others to use your code because they don’t have to worry about setting up any particular environment or remembering how everything works — they can just pass in their inputs and get back results!
Callable can be used as a function or a layer in Keras
To get started, you’ll just need to import callable as a function or layer. If you want to use it as a function in tf.Keras, you can import it like this:
from Keras import layers
from tensorflow_models import sequence_to_sequence, vocabularies
import callable as contrib # if you want to use callable as a layer in tf.Keras
“`If you’d rather not have to remember the name of each function used by callable (since they may change), or if your research involves writing code that uses TensorFlow and Keras but which isn’t part of their main codebase (like my work on natural language generation), then importing everything from `tensorflow_models` will make things easier for beginners!
You can use input_signature to specify inputs of a callable
When you write a function, you can specify inputs for the function by using input_signature. For example:
return x + 2
Here, the input signature of this function is (int, int) -> int. This means that add_two takes two integers as input and returns an integer. You can use this signature when calling add_two from another function. For example:
print(add_two(3, 4)) # prints 5
Input_signature needs to specify the types, shapes, and names of arguments
Input_signature needs to specify the types, shapes, and names of arguments (positional or keyword)
type is the data type of the input the shape is the dimensions of the input name is the name of the input
The following are examples of valid input signatures:
Input_signature(int64, int64, int64) specifies that our function takes 3 arguments with data type “int64“ and shape “(3,)“. The name for each argument would be “a“, “b“ and “c“ respectively. This means that if we call this function with 1st arg = -1 2nd arg = 2 3rd arg = 3 then its output should be 40000 since: (-1*2^3+2^3)+3+40000 = 40000
Has the same number of arguments as the function
The inout_signature must have the same number of arguments as the Python function decorated with tf. function decorator when they are supplied positionally instead of keyword arguments. If they are not supplied at all, they are assumed to be None.
TF signatures are traits that uniquely define how a function or layer behaves. They are represented as Python functions. A signature tells you what type, shape, and any other information about the inputs to a layer or a function. It also does the same for outputs.
In short, TF signatures tell us how an operation works!
The signature specifies how many arguments are required by an operation or layer and what their types are; for example:
> def add(x: float32):
> return x + y
In this article, we talked about what tf signatures are and how to create them. We covered the two types of signatures: function and callable.