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tf. function is a decorator function provided by Tensorflow 2.0 that **converts regular python code to a callable Tensorflow graph function**, which is usually more performant and python independent. It is used to create portable Tensorflow models.You can use tf. function to **make graphs out of your programs**. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. This will help you create performant and portable models, and it is required to use SavedModel .**Eager execution is slower than graph execution**! Since eager execution runs all operations one-by-one in Python, it cannot take advantage of potential acceleration opportunities.

## What does tf function do?

You can use tf. function to **make graphs out of your programs**. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. This will help you create performant and portable models, and it is required to use SavedModel .

## Is eager execution slower?

**Eager execution is slower than graph execution**! Since eager execution runs all operations one-by-one in Python, it cannot take advantage of potential acceleration opportunities.

### tf.function and Autograph (TF Dev Summit ‘19)

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## What is tf graph ()?

Graphs are **used by tf.** **function s to represent the function’s computations**. Each graph contains a set of tf. Operation objects, which represent units of computation; and tf. Tensor objects, which represent the units of data that flow between operations.

## What is tf Where?

tf. where will return the indices of condition that are non-zero, in the form of a 2-D tensor with shape [n, d] , where n is the number of non-zero elements in condition ( tf. count_nonzero(condition) ), and d is the number of axes of condition ( tf. rank(condition) ). Indices are output in row-major order.

## What is decorator in Python with example?

A decorator in Python is **a function that takes another function as its argument, and returns yet another function** . Decorators can be extremely useful as they allow the extension of an existing function, without any modification to the original function source code.

## What is tf module?

A module is **a named container for tf.Variable s, other tf.Module s and functions which apply to user input**. For example a dense layer in a neural network might be implemented as a tf.Module : class Dense(tf. Module): def __init__(self, input_dim, output_size, name=None):

## What is TF Enable_eager_execution?

With eager execution enabled, TensorFlow functions execute operations immediately (as opposed to adding to a graph to be executed later in a tf. compat. v1. Session ) and return concrete values (as opposed to symbolic references to a node in a computational graph).

## See some more details on the topic tf function decorator here:

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## How do I know if eager execution is enabled?

You can use **tf.** **executing_eagerly()** which returns True when eager execution is On. You can also use tensorboard to check the status of your GPU and inspect the problem in details to identify where might be the problem.

## What is PyTorch eager mode?

PyTorch users are very familiar with eager mode as it **provides the ease-of-use and flexibility** that we all enjoy as researchers. Caffe2 users are more aquainted with graph mode which has the benefits of speed, optimization opportunities, and functionality in C++ runtime environments.

## What is tf Global_variables_initializer ()?

global_variables_initializer() **in a session will your variables hold the values you told them to hold when you declare them** ( tf. Variable(tf. zeros(…)) , tf. Variable(tf. random_normal(…)) ,…).

## Is TensorFlow thread safe?

As we have seen, **the TensorFlow Session object is multithreaded and thread-safe**, so multiple threads can easily use the same session and run ops in parallel. However, it is not always easy to implement a Python program that drives threads as required.

## What is tf gather?

gather() is **used to slice the input tensor based on the indices provided**. Syntax: tensorflow.gather( params, indices, validate_indices, axis, batch_dims, name) Parameters: params: It is a Tensor with rank greater than or equal to axis+1. indices: It is a Tensor of dtype int32 or int64.

## What is TF Ones_like?

**Used in the notebooks**

**Given a single tensor ( tensor ), this operation returns a tensor of the same type and shape as tensor with all elements set to 1**.

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## What is TF unstack?

tf. unstack( value, num=None, axis=0, name=’unstack’ ) **Unpacks tensors from value by chipping it along the axis dimension**.

## What does the axis parameter of TF Expand_dims do?

expand_dims() is used to insert an addition dimension in input Tensor. Parameters: input: It is the input Tensor. axis: **It defines the index at which dimension should be inserted**.

## Why decorators are used in Python?

Decorators are a very powerful and useful tool in Python since it **allows programmers to modify the behaviour of function or class**. Decorators allow us to wrap another function in order to extend the behaviour of the wrapped function, without permanently modifying it.

## What is decorator and generator in Python?

In Python, we can implement decorators concept in two ways: Class decorators. Function decorators. Usually, **a decorator is any callable object that is used to modify the function (or) the class**. A reference to the function (or) class is passed to the decorator and the decorator returns the modified function (or), class …

## What is a decorator explain with an example?

A decorator is **a design pattern in Python that allows a user to add new functionality to an existing object without modifying its structure**. Decorators are usually called before the definition of a function you want to decorate.

## What is main TF in Terraform?

main.tf: **This is our main configuration file where we are going to define our resource definition**. variables.tf: This is the file where we are going to define our variables. outputs.tf: This file contains output definitions for our resources.

## What is TF file in Terraform?

»File Extension

Code in the Terraform language is stored in **plain text files** with the . tf file extension. There is also a JSON-based variant of the language that is named with the . tf. json file extension.

## What is output TF in Terraform?

Terraform output values **allow you to export structured data about your resources**. You can use this data to configure other parts of your infrastructure with automation tools, or as a data source for another Terraform workspace. Outputs are also necessary to share data from a child module to your root module.

## What does TF Reset_default_graph () do?

reset_default_graph. Defined in tensorflow/python/framework/ops.py . **Clears the default graph stack and resets the global default graph**.

## How do I check my TF version?

**To check your TensorFlow version in your Jupyter Notebook such as Google’s Colab, use the following two commands:**

- import tensorflow as tf This imports the TensorFlow library and stores it in the variable named tf .
- print(tf. __version__) This prints the installed TensorFlow version number in the format x.y.z .

## When should you use TF function?

tf. function is a decorator function provided by Tensorflow 2.0 that converts regular python code to a callable Tensorflow graph function, which is usually more performant and python independent. It is used **to create portable Tensorflow models**.

## What is TF gather?

gather() is **used to slice the input tensor based on the indices provided**. Syntax: tensorflow.gather( params, indices, validate_indices, axis, batch_dims, name) Parameters: params: It is a Tensor with rank greater than or equal to axis+1. indices: It is a Tensor of dtype int32 or int64.

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## What is input signature TensorFlow?

TensorFlow Lite supports converting TensorFlow model’s input/output specifications to TensorFlow Lite models. The **input/output specifications** are called “signatures”. Signatures can be specified when building a SavedModel or creating concrete functions.

## When eager execution is enabled?

With eager execution enabled, **TensorFlow functions execute operations immediately** (as opposed to adding to a graph to be executed later in a tf. compat. v1. Session ) and return concrete values (as opposed to symbolic references to a node in a computational graph).

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