Luz  Runolfsson

Luz Runolfsson

1601993220

How To Build Simple Model In Tensorflow

In this tutorial, I will show you How To Build Simple Model In Tensorflow. Tensorflow Framework is the popular framework to design a neural network in Machine Learning. Tensorflow is created at Google. It is an open source machine learning framework for everyone. TensorFlow is an open source library for high-performance numerical computation. Tensorflow has a flexible architecture that allows easy deployment of calculation across a variety of platforms like CPUs, GPUs, TPUs, and from desktops to clusters of servers to mobile and edge devices. It is initially developed by researchers and engineers from the Google Brain team within Google’s AI organization, and it comes with strong support for machine learning and deep learning, and the flexible numerical computation core is used across many other scientific domains.

How To Build Simple Model In Tensorflow

If we want to work with Tensorflow then first, we need to define the tensorflow graph. In this model, we will define two tensors or nodes, and then we add those nodes. Now, let’s dive into what is tensors.

#tensorflow #tensorflow framework #machine learning

What is GEEK

Buddha Community

How To Build Simple Model In Tensorflow
Luz  Runolfsson

Luz Runolfsson

1601993220

How To Build Simple Model In Tensorflow

In this tutorial, I will show you How To Build Simple Model In Tensorflow. Tensorflow Framework is the popular framework to design a neural network in Machine Learning. Tensorflow is created at Google. It is an open source machine learning framework for everyone. TensorFlow is an open source library for high-performance numerical computation. Tensorflow has a flexible architecture that allows easy deployment of calculation across a variety of platforms like CPUs, GPUs, TPUs, and from desktops to clusters of servers to mobile and edge devices. It is initially developed by researchers and engineers from the Google Brain team within Google’s AI organization, and it comes with strong support for machine learning and deep learning, and the flexible numerical computation core is used across many other scientific domains.

How To Build Simple Model In Tensorflow

If we want to work with Tensorflow then first, we need to define the tensorflow graph. In this model, we will define two tensors or nodes, and then we add those nodes. Now, let’s dive into what is tensors.

#tensorflow #tensorflow framework #machine learning

Mckenzie  Osiki

Mckenzie Osiki

1621931885

How TensorFlow Lite Fits In The TinyML Ecosystem

TensorFlow Lite has emerged as a popular platform for running machine learning models on the edge. A microcontroller is a tiny low-cost device to perform the specific tasks of embedded systems.

In a workshop held as part of Google I/O, TensorFlow founding member Pete Warden delved deep into the potential use cases of TensorFlow Lite for microcontrollers.

Further, quoting the definition of TinyML from a blog, he said:

“Tiny machine learning is capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of ways-on-use-case and targeting battery operated devices.”

#opinions #how to design tinyml #learn tinyml #machine learning models low cost #machine learning models low power #microcontrollers #tensoflow latest #tensorflow lite microcontrollers #tensorflow tinyml #tinyml applications #tinyml models

Mckenzie  Osiki

Mckenzie Osiki

1623906928

How To Use “Model Stacking” To Improve Machine Learning Predictions

What is Model Stacking?

Model Stacking is a way to improve model predictions by combining the outputs of multiple models and running them through another machine learning model called a meta-learner. It is a popular strategy used to win kaggle competitions, but despite their usefulness they’re rarely talked about in data science articles — which I hope to change.

Essentially a stacked model works by running the output of multiple models through a “meta-learner” (usually a linear regressor/classifier, but can be other models like decision trees). The meta-learner attempts to minimize the weakness and maximize the strengths of every individual model. The result is usually a very robust model that generalizes well on unseen data.

The architecture for a stacked model can be illustrated by the image below:

#tensorflow #neural-networks #model-stacking #how to use “model stacking” to improve machine learning predictions #model stacking #machine learning

The Best Way to Build a Chatbot in 2021

A useful tool several businesses implement for answering questions that potential customers may have is a chatbot. Many programming languages give web designers several ways on how to make a chatbot for their websites. They are capable of answering basic questions for visitors and offer innovation for businesses.

With the help of programming languages, it is possible to create a chatbot from the ground up to satisfy someone’s needs.

Plan Out the Chatbot’s Purpose

Before building a chatbot, it is ideal for web designers to determine how it will function on a website. Several chatbot duties center around fulfilling customers’ needs and questions or compiling and optimizing data via transactions.

Some benefits of implementing chatbots include:

  • Generating leads for marketing products and services
  • Improve work capacity when employees cannot answer questions or during non-business hours
  • Reducing errors while providing accurate information to customers or visitors
  • Meeting customer demands through instant communication
  • Alerting customers about their online transactions

Some programmers may choose to design a chatbox to function through predefined answers based on the questions customers may input or function by adapting and learning via human input.

#chatbots #latest news #the best way to build a chatbot in 2021 #build #build a chatbot #best way to build a chatbot

Riyad Amin

Riyad Amin

1571046022

Build Your Own Cryptocurrency Blockchain in Python

Cryptocurrency is a decentralized digital currency that uses encryption techniques to regulate the generation of currency units and to verify the transfer of funds. Anonymity, decentralization, and security are among its main features. Cryptocurrency is not regulated or tracked by any centralized authority, government, or bank.

Blockchain, a decentralized peer-to-peer (P2P) network, which is comprised of data blocks, is an integral part of cryptocurrency. These blocks chronologically store information about transactions and adhere to a protocol for inter-node communication and validating new blocks. The data recorded in blocks cannot be altered without the alteration of all subsequent blocks.

In this article, we are going to explain how you can create a simple blockchain using the Python programming language.

Here is the basic blueprint of the Python class we’ll use for creating the blockchain:

class Block(object):
    def __init__():
        pass
    #initial structure of the block class 
    def compute_hash():
        pass
    #producing the cryptographic hash of each block 
  class BlockChain(object):
    def __init__(self):
    #building the chain
    def build_genesis(self):
        pass
    #creating the initial block
    def build_block(self, proof_number, previous_hash):
        pass
    #builds new block and adds to the chain
   @staticmethod
    def confirm_validity(block, previous_block):
        pass
    #checks whether the blockchain is valid
    def get_data(self, sender, receiver, amount):
        pass
    # declares data of transactions
    @staticmethod
    def proof_of_work(last_proof):
        pass
    #adds to the security of the blockchain
    @property
    def latest_block(self):
        pass
    #returns the last block in the chain

Now, let’s explain how the blockchain class works.

Initial Structure of the Block Class

Here is the code for our initial block class:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()

As you can see above, the class constructor or initiation method ( init()) above takes the following parameters:

self — just like any other Python class, this parameter is used to refer to the class itself. Any variable associated with the class can be accessed using it.

index — it’s used to track the position of a block within the blockchain.

previous_hash — it used to reference the hash of the previous block within the blockchain.

data—it gives details of the transactions done, for example, the amount bought.

timestamp—it inserts a timestamp for all the transactions performed.

The second method in the class, compute_hash , is used to produce the cryptographic hash of each block based on the above values.

As you can see, we imported the SHA-256 algorithm into the cryptocurrency blockchain project to help in getting the hashes of the blocks.

Once the values have been placed inside the hashing module, the algorithm will return a 256-bit string denoting the contents of the block.

So, this is what gives the blockchain immutability. Since each block will be represented by a hash, which will be computed from the hash of the previous block, corrupting any block in the chain will make the other blocks have invalid hashes, resulting in breakage of the whole blockchain network.

Building the Chain

The whole concept of a blockchain is based on the fact that the blocks are “chained” to each other. Now, we’ll create a blockchain class that will play the critical role of managing the entire chain.

It will keep the transactions data and include other helper methods for completing various roles, such as adding new blocks.

Let’s talk about the helper methods.

Adding the Constructor Method

Here is the code:

class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()

The init() constructor method is what instantiates the blockchain.

Here are the roles of its attributes:

self.chain — this variable stores all the blocks.

self.current_data — this variable stores information about the transactions in the block.

self.build_genesis() — this method is used to create the initial block in the chain.

Building the Genesis Block

The build_genesis() method is used for creating the initial block in the chain, that is, a block without any predecessors. The genesis block is what represents the beginning of the blockchain.

To create it, we’ll call the build_block() method and give it some default values. The parameters proof_number and previous_hash are both given a value of zero, though you can give them any value you desire.

Here is the code:

def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
 def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block

Confirming Validity of the Blockchain

The confirm_validity method is critical in examining the integrity of the blockchain and making sure inconsistencies are lacking.

As explained earlier, hashes are pivotal for realizing the security of the cryptocurrency blockchain, because any slight alteration in an object will result in the creation of an entirely different hash.

Thus, the confirm_validity method utilizes a series of if statements to assess whether the hash of each block has been compromised.

Furthermore, it also compares the hash values of every two successive blocks to identify any anomalies. If the chain is working properly, it returns true; otherwise, it returns false.

Here is the code:

def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True

Declaring Data of Transactions

The get_data method is important in declaring the data of transactions on a block. This method takes three parameters (sender’s information, receiver’s information, and amount) and adds the transaction data to the self.current_data list.

Here is the code:

def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True

Effecting the Proof of Work

In blockchain technology, Proof of Work (PoW) refers to the complexity involved in mining or generating new blocks on the blockchain.

For example, the PoW can be implemented by identifying a number that solves a problem whenever a user completes some computing work. Anyone on the blockchain network should find the number complex to identify but easy to verify — this is the main concept of PoW.

This way, it discourages spamming and compromising the integrity of the network.

In this article, we’ll illustrate how to include a Proof of Work algorithm in a blockchain cryptocurrency project.

Finalizing With the Last Block

Finally, the latest_block() helper method is used for retrieving the last block on the network, which is actually the current block.

Here is the code:

def latest_block(self):
        return self.chain[-1]

Implementing Blockchain Mining

Now, this is the most exciting section!

Initially, the transactions are kept in a list of unverified transactions. Mining refers to the process of placing the unverified transactions in a block and solving the PoW problem. It can be referred to as the computing work involved in verifying the transactions.

If everything has been figured out correctly, a block is created or mined and joined together with the others in the blockchain. If users have successfully mined a block, they are often rewarded for using their computing resources to solve the PoW problem.

Here is the mining method in this simple cryptocurrency blockchain project:

def block_mining(self, details_miner):
            self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awarded with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)

Summary

Here is the whole code for our crypto blockchain class in Python:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()
    def __repr__(self):
        return "{} - {} - {} - {} - {}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()
    def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
    def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block
    @staticmethod
    def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True
    def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True        
    @staticmethod
    def proof_of_work(last_proof):
        pass
    @property
    def latest_block(self):
        return self.chain[-1]
    def chain_validity(self):
        pass        
    def block_mining(self, details_miner):       
        self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awared with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)  
    def create_node(self, address):
        self.nodes.add(address)
        return True
    @staticmethod
    def get_block_object(block_data):        
        return Block(
            block_data['index'],
            block_data['proof_number'],
            block_data['previous_hash'],
            block_data['data'],
            timestamp=block_data['timestamp']
        )
blockchain = BlockChain()
print("GET READY MINING ABOUT TO START")
print(blockchain.chain)
last_block = blockchain.latest_block
last_proof_number = last_block.proof_number
proof_number = blockchain.proof_of_work(last_proof_number)
blockchain.get_data(
    sender="0", #this means that this node has constructed another block
    receiver="LiveEdu.tv", 
    amount=1, #building a new block (or figuring out the proof number) is awarded with 1
)
last_hash = last_block.compute_hash
block = blockchain.build_block(proof_number, last_hash)
print("WOW, MINING HAS BEEN SUCCESSFUL!")
print(blockchain.chain)

Now, let’s try to run our code to see if we can generate some digital coins…

Wow, it worked!

Conclusion

That is it!

We hope that this article has assisted you to understand the underlying technology that powers cryptocurrencies such as Bitcoin and Ethereum.

We just illustrated the basic ideas for making your feet wet in the innovative blockchain technology. The project above can still be enhanced by incorporating other features to make it more useful and robust.

Learn More

Thanks for reading !

Do you have any comments or questions? Please share them below.

#python #cryptocurrency