TensorFlow is precise to a python package, and a lot of features are identical to that of Python. But the core of TensorFlow has distributed runtime. This functionality implements in many languages, and one of them is Python.

It is the diagram of Tensor Flow’s distributed Execution engine or the runtime engine. The other way to visualize the above picture is to think of it as a virtual machine whose language like C, C++, R, Java, etc. The use of these API’s in TensorFlow is explained below.

C API for TensorFlow
The only APIs having the official backing of TensorFlow are C and Python API (some parts). C APIs should be used whenever we are about to make TensorFlow API for some other languages, as lots of languages have ways to connect with C language.
C++ API for TensorFlow
The runtime of TensorFlow is written in C++, and mostly C++ is connected to TensorFlow through header files in tensor flow. C++ API still is in experimental stages of development, but Google commits to work with C++.
R API for TensorFlow
The R API for TensorFlow made by RStudio has some different approaches for providing API support. R API fully contains the python API, which is different from what TensorFlow goes with its APIs. But the users of R have all the access to features of Python API.
Python API for TensorFlow
Python API is the core language when it comes to Tensor Flow and its development. It is one of the first languages supported by TensorFlow and still supports most of the features. The Python API is so diverse that we will have to choose which level of API in TensorFlow; we want to work on.

APIs inside TensorFlow Project
The APIs inside TensorFlow are still Python-based, and they have low-level options for its users, such as tf.manual or tf.nnrelu, which are used to build neural networks architecture. These APIs also use in designing a deep neural network having higher levels of abstraction.
The functionality available by this collection is as follows-
• Automatic checkpoints
• Automatic logging
• Separate training/ evaluation/prediction
• Simplified training distribution.
TensorFlow is offering experienced multi-queue, multi-thread, and queue-runner design, which use for loading data. The developers of TensorFlow delivered the dataset API to address this issue and provide a candy interface as a bonus.

APIs Outside TensorFlow Project
Some other TensorFlow APIs that develop outside of the TensorFlow project by machine learning enthusiasts.
• TFLearn: This API cannot be seen as TF Learn, which is Tensor Flow’s tf.contrib.learn. It is a type of separate Python package.
• Tensor Layer: It comes as a separate package and is different from what Tensor Flow’s layers API has in its bag.
• Pretty Tensor: It is a Google project which offers a fluent interface with chaining.
• Sonnet: It is a project of Google?s Deep Mind which features a modular approach.
We have to know about the TensorFlow API for different languages. Besides, we also studied how TensorFlow is different from Python and how it got its own identity in machine learning and deep neural network areas.

TensorFlow Forming Graphs

A Partial differential equation (PDE) is the primary type of differential equation, which involves partial derivative with the unknown function of several independent variables. Regarding partial differential equations, we are focusing on creating new graphs.
Let we have to assume there is a pond with dimension 500*500 square-
N=500
Now, we will compute the partial differential equations and form the respective graph using it. Generate the steps given below for computing graph.
Upgrading v1 to v2 in TensorFlow code is given below:

1. import tensorflow.compat.v1 as tf
2. tf.disable_v2_behavior()
Step 1- Firstly, import libraries for simulation.
1. import tensorflow as tf
2. import numpy as np
3. import matplotlib.pyplot as plt

Step 2- Include functions for the transformation of a 2D array into a convolutional kernel and simplified the 2D convolution operation of the forming graph.
Example:

1. def make_kernel(a): 
2. a = np.asarray(a) 
3. a = a.reshape (list(a.shape) + [1,1]) 
4. return tf.constant(a, dtype=1) 
5. 
6. def simple_conv(x, j): 
7. ""2D convolutional operation is generated below"": 
8. x = tf.expand_dims(tf.expand_dims(x, 0), -1) 
9. y = tf.nn.depthwise_conv2d(x, j, [1, 1, 1, 1], padding = 'SAME') 
10. return y[0, :, :, 0] 
11. def laplace(x): 
12. """Computing 2D laplacian of the arrays""": 
13. laplace_j = make_kernel ([[0.5, 1.0, 0.5], [1.0, -6., 1.0], [0.5, 1.0, 0.5]]) 
14. return simple_conv(x, laplace_j) 
15. 
16. sess = tf.InteractiveSession() 
17. We are going to step 3 now. 
Step 3- Include the number of iterations and compute the graph to display the records accordingly:-
1. N = 500 
2. 
3. # Initial Conditions -- some raindrops hit the pond: 
4. 
5. # Setting the zero here: 
6. u_init = np.zeros([N, N], dtype = np.float32) 
7. ut_init = np.zeros([N, N], dtype = np.float32) 
8. 
9. #Few rain drops hit a pond at random points: 
10. for n in range(100): 
11. a,b = np.random.randint(0, N, 2) 
12. u_init[a,b] = np.random.uniform() 
13. plt.imshow(u_init) 
14. plt.show() 
15. 
16. # Parameters of Graphs 
17. # eps -- time resolution 
18. # damping -- wave damping 
19. eps = tf.placeholder(tf.float32, shape = ()) 
20. damping = tf.placeholder(tf.float32, shape = ()) 
21. 
22. # Creating variable for simulation state 
23. U = tf.Variable(u_init) 
24. Ut = tf.Variable(ut_init) 
25. 
26. # Discretized PDE updated rule: 
27. U_ = U + eps * Ut 
28. Ut_ = Ut + eps*(laplace(U) - damping * Ut) 
29. 
30. # Updating the state of rules: 
31. step =tf.group(U.assign(U_), Ut.assign(Ut_)) 
32. 
33. # Initializing state to initial conditions 
34. tf.initialize_all_variables().run() 
35. 
36. # Running 1000 steps of PDE and forming graph 
37. for i in range(1000): 
38. # Step simulating: 
39. step.run({eps: 0.03, damping: 0.04}) 
40. # Visualizing every 50 steps 
41. if i % 500 == 0: 
42. plt.imshow(U.eval()) 
43. plt.show() 
Output:

So, this brings us to the end of blog. This Tecklearn ‘TensorFlow APIs’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Introduction to Deep Learning and AI
• What is Deep Learning?
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• Real-Life use cases of Deep Learning
• Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
• Pre-requisites for AI & DL
• Python Programming Language
• Installation & IDE
Environment Set Up and Essentials
• Installation
• Python – NumPy
• Python for Data Science and AI
• Python Language Essentials
• Python Libraries – Numpy and Pandas
• Numpy for Mathematical Computing
More Prerequisites for Deep Learning and AI
• Pandas for Data Analysis
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• Normalization
• Data Set
• Machine Learning Concepts
• Regression
• Logistic Regression
• SVM – Support Vector Machines
• Decision Trees
• Python Libraries for Data Science and AI
Introduction to Neural Networks
• Creating Module
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• Sigmoid Function
• Multi-layered perception
• Weights, Biases
• Activation Functions
• Gradient Decent or Error function
• Epoch, Forward & backword propagation
• What is TensorFlow?
• TensorFlow code-basics
• Graph Visualization
• Constants, Placeholders, Variables
Multi-layered Neural Networks
• Error Back propagation issues
• Drop outs
Regularization techniques in Deep Learning
Deep Learning Libraries
• Tensorflow
• Keras
• OpenCV
• SkImage
• PIL
Building of Simple Neural Network from Scratch from Simple Equation
• Training the model
Dual Equation Neural Network
• TensorFlow
• Predicting Algorithm
Introduction to Keras API
• Define Keras
• How to compose Models in Keras
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers
• What is Batch Normalization
• Saving and Loading a model with Keras
• Customizing the Training Process
• Using TensorBoard with Keras
• Use-Case Implementation with Keras
GPU in Deep Learning
• Introduction to GPUs and how they differ from CPUs
• Importance of GPUs in training Deep Learning Networks
• The GPU constituent with simpler core and concurrent hardware
• Keras Model Saving and Reusing
• Deploying Keras with TensorBoard
Keras Cat Vs Dog Modelling
• Activation Functions in Neural Network
Optimization Techniques
• Some Examples for Neural Network
Convolutional Neural Networks (CNN)
• Introduction to CNNs
• CNNs Application
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• Understanding and Visualizing a CNN
RNN: Recurrent Neural Networks
• Introduction to RNN Model
• Application use cases of RNN
• Modelling sequences
• Training RNNs with Backpropagation
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model
Application of Deep Learning in image recognition, NLP and more
Real world projects in recommender systems and others
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