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Introduction to tensorflow concatenate

Tensorflow concatenate is the methodology using which we can join and make one resultant out of two values. Concat() function is used in tensorflow framework for concatenating the tensprs along with 1-d format. In this article, we will have to try to gain knowledge about What is tensorflow concatenate, How to Concatenate, how to use tensorflow concatenate, tensorflow concatenate feature, tensorflow concatenate example and finally conclude our statement.

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What is tensorflow concatenate?

tensorFlowObject.Concat(input values, axis, operation name)

The parameters mentioned in the above syntax are as described one by one below –

Input values – This is the source input tensor or the list of tensors that we want to concatenate.

Axis – It is a tensor value of zero dimensions and helps in specifying the dimensions that needed to be followed while concatenating.

Operation name – It is an optional argument that needs to be passed in order to define the name of the operation to be performed.

Return value – The output of the concat() function is the tensor that has the concatenated value of the supplied input tensor arguments.

The concatenate layer is responsible for joining the input values and inherits its functionality from Module and Layer classes. Instead of concat() function, we can also make use of the function –

tensorflow.keras.layers.Concatenate(axis = 1, ** keyword arguments of standard layer)

Also, note that the shape of all the input tensors that are being supplied should be the same. The exceptional value in this is the axis of concatenation. As previously mentioned, in case of concatenate() function as well only one output tensor containing the combined or joined input tensors is obtained.

How to Concatenate?

One can simply concatenate the two or more tensor values stored in two or more different variables or objects by passing the list of them enclosed in square brackets [tensor1, tensor2, …] like this to input as the second parameter and first parameter with the value of the axis specifying the dimensions of the tensor to the concat() function. For example, if we have two matrices/tensors that have the same shape –

[[ 21, 22, 23], [ 24, 25, 26]]

AND

[[ 27, 28, 29], [30, 31, 32]]

After we make the use of concat() tensorflow function to concatenate both of them, our matrix will look as shown in the below tensor value –

[ 24, 25, 26]], [[ 27, 28, 29], [30, 31, 32]]]

Used tensorflow concatenate

[[[ 21, 22, 23],[ 24, 25, 26]], [[ 27, 28, 29], [30, 31, 32]]]

The tensorkflow concatenate function can only be used provided if you have two or more tensor values of the same shape that you want to concatenate. Note that if the shapes of matrices are not the same then you will need to reshape the vectors before manipulating it for the concatenate() function.

The first step that we will follow once we have our inputs ready with us in the program is to import the necessary libraries and packages in the beginning.

Prepare the input tensors. Store them in objects or make a list of the same and pass it as the argument. If the shape is not the same then reshape before passing it as input.

Pass the axis and the input arguments to the tensor matrix.

tensorflow concatenate feature

The feature or properties of concatenate in tensorflow are as mentioned below –

Activity regularizer – This is an optional function and is used for preparing the output of this concatenation layer in tensorflow.

Dt type input – This is used for retrieval of the layer input and is only applicable if we have only single input for the layer. It then returns an output consisting of a list of tensors or a single tensor.

Losses – This loss is actually associated with the layer of concatenation. The tensors responsible for regularizing the tensors are also generated and created by using the property of loss associated and accesses by the layer. The working is similar to the eager safe which means the access of losses will propagate gradients under a tensorflow.Gradient back to the variables associated with it.

Non- trainable weights

Non- trainable variables

Output_mask – This property or feature is only applicable if the concatenation layer consists of only a single inbound node that means if the connection of only a single incoming layer is created. This feature helps in retrieving the output massk tensor of the layer.

Output_shape – Applicable only if one output layer is present or the shape of all the outputs has the same shape.

Trainableweights

Trainable variables

Set weights

Get weights

Get updates for

Get output shape at

Get output mask at

Get output at

Get losses for

Get input shape at

Get input mask at

Get input at

Get config

From config

Count params

Compute output shape

Compute mask

build

tensorflow concatenate example

Here are the following examples mention below

Example #1

Code:

The output of executing the above program is as shown in the below image –

Example #2

Code:

return educbaModel

The output of executing the above program is as shown in the below image –

Conclusion

The tensorflow concatenate function is used in tensorflow to combine or join two or more source tensors and form a single output consisting of a single tensor that has both the input tensors combined in it. We can provide and specify the input axis dimension which helps in representing the dimensions of tensor.

What Is Hashing In Cybersecurity? Learn The Benefits And Types

blog / Cybersecurity Want to Know What is Hashing in Cybersecurity? The Ultimate Guide

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To safeguard its data, any organization must prevent malware attacks. A crucial way of doing this is for businesses to implement hashing algorithms in their cyber systems to ensure security. We look closely at what is hashing in cybersecurity, its purpose, and other associated details.

What is Hashing in Cybersecurity?

In computer science and cryptography, a hash function is a deterministic procedure that takes an input (or “message”) and returns a string of characters of a fixed size—which is usually a “digest”—that is unique to the input.

A hash function is used in many cybersecurity algorithms and protocols, such as password storage and digital signature. Hashing is also used in a data structure, such as a hash table (a data structure that stores data), for a quick search and insertion.

The Purpose of Hashing

Learning the answer to the question about what is hashing in cybersecurity can help a professional use hashing algorithms for data encryption and data security. Cybersecurity professionals convert a large block of input data using the hashing algorithm into a smaller fixed-length string as the final output.

Businesses always want to secure their data servers and cloud storage systems from vulnerabilities to malicious software. Hashing helps cybersecurity professionals ensure that the data stored on servers and cloud storage systems remains unreadable by hackers.

What is Hashing Used for?

Hashing is a one-way function that turns a file or string of text into a unique digest of the message. The hash value is calculated by a hashing algorithm using the binary data of a particular file. Now let’s look at the different uses that hashing has in cybersecurity.

Storage Password

Hashes provide security to an organization’s cyber system so that hackers cannot steal it; for example, email passwords stored on servers.

Digital Signatures

Hashing is a way to encrypt and decrypt digital signatures, verifying the message’s sender and receiver.

Document Management

The authenticity of data can be verified with the use of hashing algorithms. When a document is entirely written, the cybersecurity specialist will use a hash to secure it.

File Management

Businesses use hashes to index data, recognize files, and erase duplicate files. An organization can save significant time utilizing hashes when working with a cyber system with thousands of files.

A Hashing Example

Suppose you are a cybersecurity professional and wish to digitally sign a piece of software before making it accessible for download on your website. To do so, you will generate a hash of the script or a software application you are signing and then generate another hash after adding your digital signature. Then, the whole thing is encoded in a way that makes it possible to download it.

Types of Hashing in Cybersecurity

As a cybersecurity professional, you can select from a wide variety of different types of hashing. Some of the most widely used for decryption are described below:

1. MD5

The Message Digest hashing algorithm’s fifth iteration is MD5, which creates a 128-bit hash function.

2. SHA-1

SHA-1, the first iteration of the Secure Hash Algorithm, generates a hash function output that is 160 bits long. This SHA is one of the primary hashing algorithms used by professionals in the field of computer science.

3. SHA-2

SHA-2 is not just one hashing algorithm. Instead, it is a group of four algorithms: SHA-224, SHA-256, SHA-384, and SHA-512. The name of each hashing algorithm is the same as the bit output it generates.

4. CRC32

The CRC32 hashing algorithm uses a Cyclic Redundancy Check (CRC) as its primary method for identifying unauthorized changes to data that has been saved. When data is encoded using CRC32, the output hash value will always be of a consistent length. Hashing is performed with the CRC32 method on Zip file formats and File Transfer Protocol (FTP) servers.

ALSO READ: What is Cybersecurity and Why is it a Great Career Choice for You

Benefits of Hashing in Cybersecurity

Hashes are helpful for cybersecurity professionals to discover a threat on a computer system. It also helps them to investigate the entire cyber network to determine whether or not a particular file is present. The following pointers will further help you understand why hashing is essential in cybersecurity.

Hashing is a technique used in database management systems to search for the location of data without making use of an index structure

It makes it easy to determine whether or not two files in a computer system are the same

The retrieval and processing of data can be done very quickly with hash tables

Hash gives a consistent amount of time on average for performing operations such as searching, inserting, and deleting data

Limitations of Hashing in Cybersecurity

Let’s also look at some drawbacks of using hashing in cybersecurity.

Hash algorithms cannot process null values (where the value is missing)

The implementation of hash tables can be difficult

When there are a large number of collisions (two data pieces in a hash table sharing the same hash value), hash becomes inefficient

Common Hashing Algorithms LANMAN

The Microsoft LAN Manager hashing algorithm, more commonly referred to as LANMAN, is primarily responsible for storing passwords.

NTLM

The NT LAN Manager hashing algorithm is another name for the NTLM, which goes by both names. NTLM is quickly replacing LANMAN as the standard authentication method because of its ability to generate password hashes.

Scrypt

It is a hashing algorithm that uses much computing processing power and takes a long time to make a hash compared to other algorithms.

Ethash

The Ethereum network developed and deployed a proof-of-work mining algorithm known as Ethash to ensure the integrity of the blockchain.

Hashing vs. Encryption

Significant differences between hashing and encryption are visible in their respective functionalities.

Process

Encryption is a two-way process using an encryption key to scramble information. In contrast, a decryption key is used to unscramble the information after it has been encrypted by a user. On the other hand, hashing is a one-way function that turns a file or string of text into a unique digest of the message.

Data

Data is mapped to an output of fixed size using hash functions, referred to as hashing. It is employed to confirm the integrity of files containing data. In the case of encryption, the message is encrypted so that only those users with the proper authorization can read it.

Primary Function

Verification of data and ensuring its integrity is the primary goal of hashing. On the other hand, encryption’s primary function is to ensure the confidentiality of data transmission by providing efficient protection facilities.

Gain a Deeper Insight Into Cybersecurity

Hashing in cybersecurity is a convenient option to prevent security threats to your system. To get the knowledge to do so, along with learning more about other aspects of cybersecurity, enroll in the online cybersecurity courses offered by Emeritus. It will not only help you gain expertise in hashing algorithms but also help you build a career in this specialization.

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How Debugging Works In Tensorflow?

Introduction to TensorFlow Debugging

In this article, we will try and understand what the different ways of debugging can be done in TensorFlow. Generally debugging is very useful for finding out the values getting flown down in the code and where exactly the code is breaking. All the languages present in the market provide inbuilt functionality for debugging. Similarly, in TensorFlow also provides different classes and packages with which we can identify the flow of the data in the algorithms and optimize the algorithm’s performance.

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How Debugging Works in TensorFlow?

Now let’s see how the debugging works in TensorFlow.

The core program part where the debugging can be enabled in TensorFlow are:

graph(though the use of this function we can build a computation graph)

session(though the use of this function we can execute the graph)

there are in total 4 ways as shown below through which we can perform debugging in TensorFlow

1. Fetching and Printing Values for a Particular Tensor

This is the easiest to use step where we can add breakpoints and print out the values to get the required information

Advantage:

It is very easy and quick to implement.

And information can be fetched from anywhere we want.

If we print any information at any point, then that will create a reference to that particular tensor which is not a good practice to keep

2. The tf.print function

This method can come handy while checking some output in runtime. It will just create a log for the particular line in the with the use of the session.run() method.

Advantage:

This method is handy as it helps us to monitor the development of the values during the run time.

Since this creates a log of the terminal data during the execution of the algorithm, it might fill up the screen with the logs that are not a good practice Afterall.

just want to discuss the tool which TensorFlow provides called Tensor Board. It’s a web UI for TensorFlow visualization developed by Google and runs locally in the system. Below is the screenshot for the website. It is generally used to visualize the performance of the TensorFlow algorithm and monitor its performance. This Dashboard also comes with a plugin for debugging.

3. TensorBoard visualization

With this visualization, we can use to monitor various things about the out model, such as:

We can summarize the model.

View the performance.

Serialize the data in the model.

Clean the graph and give proper nomenclature.

This is basically more or less a monitoring tool used to monitor the performance of our model.

Now moving on to TensorBoard Debugger.

4. TensorBoard Debugger

As explained earlier, TensorBoard is a visualizing tool so that visualization can be debugged using this plugin. It provides various cool debugging features such as:

We can select particular nodes in the Tensor and debug them.

Graphically we can control the execution of the model.

And finally, we can also visualize the tensors and their values.

Below is the screenshot of this TensorBoard Debugger in action:

The code TensorFlow packages which are used for the debugging are:

Here tf_debug is the debugger that needs to be imported from the TensorFlow.python package to run the debugging on TensorFlow.

And the below two lines are used to invoke the TenorBoard locally through the terminal.

Advantages of TensorFlow Debugging

We can identify we can output value and a particular stage through the use of debugging while the algorithm is getting trained.

Using the Tensor board application, we can identify and see the performance of our algorithm in a graphical format.

We can also run the execution of each and every step of our model using the GUI provided in the Tensor Board.

The TensorBoard application is very user friendly and easy to understand.

With the use of a debugger or rather Tensor Board, we can identify if we still need more data cleaning is required on our training data.

Conclusion

In this article, we learned about the debugging in TensorFlow, the packages present for TensorFlow’s debugging purpose, and how to implement them. We have also seen the use of tensor board applications, which is a useful tool to debug the algorithm while getting trained.

How Can Tensorflow And Pre

Tensorflow and the pre-trained model can be used for feature extraction by setting the ‘trainable’ feature of the previously created ‘base_model’ to ‘False’.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model.

We will understand how to classify images of cats and dogs with the help of transfer learning from a pre-trained network. The intuition behind transfer learning for image classification is, if a model is trained on a large and general dataset, this model can be used to effectively serve as a generic model for the visual world. It would have learned the feature maps, which means the user won’t have to start from scratch by training a large model on a large dataset.

Read More: How can a customized model be pre-trained?

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

Example print("Feature extraction") base_model.trainable = False print("The base model architecture") The base model architecture Model: "mobilenetv2_1.00_160" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 160, 160, 3) 0 __________________________________________________________________________________________________ Conv1 (Conv2D) (None, 80, 80, 32) 864 input_1[0][0] __________________________________________________________________________________________________ bn_Conv1 (BatchNormalization) (None, 80, 80, 32) 128 Conv1[0][0] __________________________________________________________________________________________________ Conv1_relu (ReLU) (None, 80, 80, 32) 0 bn_Conv1[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise (Depthw (None, 80, 80, 32) 288 Conv1_relu[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise_BN (Bat (None, 80, 80, 32) 128 expanded_conv_depthwise[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise_relu (R (None, 80, 80, 32) 0 expanded_conv_depthwise_BN[0][0] __________________________________________________________________________________________________ expanded_conv_project (Conv2D) (None, 80, 80, 16) 512 expanded_conv_depthwise_relu[0][0 __________________________________________________________________________________________________ expanded_conv_project_BN (Batch (None, 80, 80, 16) 64 expanded_conv_project[0][0] __________________________________________________________________________________________________ block_1_expand (Conv2D) (None, 80, 80, 96) 1536 expanded_conv_project_BN[0][0] __________________________________________________________________________________________________ block_1_expand_BN (BatchNormali (None, 80, 80, 96) 384 block_1_expand[0][0] __________________________________________________________________________________________________ block_1_expand_relu (ReLU) (None, 80, 80, 96) 0 block_1_expand_BN[0][0] __________________________________________________________________________________________________ block_1_pad (ZeroPadding2D) (None, 81, 81, 96) 0 block_1_expand_relu[0][0] __________________________________________________________________________________________________ block_1_depthwise (DepthwiseCon (None, 40, 40, 96) 864 block_1_pad[0][0] __________________________________________________________________________________________________ block_1_depthwise_BN (BatchNorm (None, 40, 40, 96) 384 block_1_depthwise[0][0] __________________________________________________________________________________________________ block_1_depthwise_relu (ReLU) (None, 40, 40, 96) 0 block_1_depthwise_BN[0][0] __________________________________________________________________________________________________ block_1_project (Conv2D) (None, 40, 40, 24) 2304 block_1_depthwise_relu[0][0] __________________________________________________________________________________________________ block_1_project_BN (BatchNormal (None, 40, 40, 24) 96 block_1_project[0][0] __________________________________________________________________________________________________ block_2_expand (Conv2D) (None, 40, 40, 144) 3456 block_1_project_BN[0][0] __________________________________________________________________________________________________ block_2_expand_BN (BatchNormali (None, 40, 40, 144) 576 block_2_expand[0][0] __________________________________________________________________________________________________ block_2_expand_relu (ReLU) (None, 40, 40, 144) 0 block_2_expand_BN[0][0] __________________________________________________________________________________________________ block_2_depthwise (DepthwiseCon (None, 40, 40, 144) 1296 block_2_expand_relu[0][0] __________________________________________________________________________________________________ block_2_depthwise_BN (BatchNorm (None, 40, 40, 144) 576 block_2_depthwise[0][0] __________________________________________________________________________________________________ block_2_depthwise_relu (ReLU) (None, 40, 40, 144) 0 block_2_depthwise_BN[0][0] __________________________________________________________________________________________________ block_2_project (Conv2D) (None, 40, 40, 24) 3456 block_2_depthwise_relu[0][0] __________________________________________________________________________________________________ block_2_project_BN (BatchNormal (None, 40, 40, 24) 96 block_2_project[0][0] __________________________________________________________________________________________________ block_2_add (Add) (None, 40, 40, 24) 0 block_1_project_BN[0][0] block_2_project_BN[0][0] __________________________________________________________________________________________________ block_3_expand (Conv2D) (None, 40, 40, 144) 3456 block_2_add[0][0] __________________________________________________________________________________________________ block_3_expand_BN (BatchNormali (None, 40, 40, 144) 576 block_3_expand[0][0] __________________________________________________________________________________________________ block_3_expand_relu (ReLU) (None, 40, 40, 144) 0 block_3_expand_BN[0][0] __________________________________________________________________________________________________ block_3_pad (ZeroPadding2D) (None, 41, 41, 144) 0 block_3_expand_relu[0][0] __________________________________________________________________________________________________ block_3_depthwise (DepthwiseCon (None, 20, 20, 144) 1296 block_3_pad[0][0] __________________________________________________________________________________________________ block_3_depthwise_BN (BatchNorm (None, 20, 20, 144) 576 block_3_depthwise[0][0] __________________________________________________________________________________________________ block_3_depthwise_relu (ReLU) (None, 20, 20, 144) 0 block_3_depthwise_BN[0][0] __________________________________________________________________________________________________ block_3_project (Conv2D) (None, 20, 20, 32) 4608 block_3_depthwise_relu[0][0] __________________________________________________________________________________________________ block_3_project_BN (BatchNormal (None, 20, 20, 32) 128 block_3_project[0][0] __________________________________________________________________________________________________ block_4_expand (Conv2D) (None, 20, 20, 192) 6144 block_3_project_BN[0][0] __________________________________________________________________________________________________ block_4_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_4_expand[0][0] __________________________________________________________________________________________________ block_4_expand_relu (ReLU) (None, 20, 20, 192) 0 block_4_expand_BN[0][0] __________________________________________________________________________________________________ block_4_depthwise (DepthwiseCon (None, 20, 20, 192) 1728 block_4_expand_relu[0][0] __________________________________________________________________________________________________ block_4_depthwise_BN (BatchNorm (None, 20, 20, 192) 768 block_4_depthwise[0][0] __________________________________________________________________________________________________ block_4_depthwise_relu (ReLU) (None, 20, 20, 192) 0 block_4_depthwise_BN[0][0] __________________________________________________________________________________________________ block_4_project (Conv2D) (None, 20, 20, 32) 6144 block_4_depthwise_relu[0][0] __________________________________________________________________________________________________ block_4_project_BN (BatchNormal (None, 20, 20, 32) 128 block_4_project[0][0] __________________________________________________________________________________________________ block_4_add (Add) (None, 20, 20, 32) 0 block_3_project_BN[0][0] block_4_project_BN[0][0] __________________________________________________________________________________________________ block_5_expand (Conv2D) (None, 20, 20, 192) 6144 block_4_add[0][0] __________________________________________________________________________________________________ block_5_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_5_expand[0][0] __________________________________________________________________________________________________ block_5_expand_relu (ReLU) (None, 20, 20, 192) 0 block_5_expand_BN[0][0] __________________________________________________________________________________________________ block_5_depthwise (DepthwiseCon (None, 20, 20, 192) 1728 block_5_expand_relu[0][0] __________________________________________________________________________________________________ block_5_depthwise_BN (BatchNorm (None, 20, 20, 192) 768 block_5_depthwise[0][0] __________________________________________________________________________________________________ block_5_depthwise_relu (ReLU) (None, 20, 20, 192) 0 block_5_depthwise_BN[0][0] __________________________________________________________________________________________________ block_5_project (Conv2D) (None, 20, 20, 32) 6144 block_5_depthwise_relu[0][0] __________________________________________________________________________________________________ block_5_project_BN (BatchNormal (None, 20, 20, 32) 128 block_5_project[0][0] __________________________________________________________________________________________________ block_5_add (Add) (None, 20, 20, 32) 0 block_4_add[0][0] block_5_project_BN[0][0] __________________________________________________________________________________________________ block_6_expand (Conv2D) (None, 20, 20, 192) 6144 block_5_add[0][0] __________________________________________________________________________________________________ block_6_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_6_expand[0][0] __________________________________________________________________________________________________ block_6_expand_relu (ReLU) (None, 20, 20, 192) 0 block_6_expand_BN[0][0] __________________________________________________________________________________________________ block_6_pad (ZeroPadding2D) (None, 21, 21, 192) 0 block_6_expand_relu[0][0] __________________________________________________________________________________________________ block_6_depthwise (DepthwiseCon (None, 10, 10, 192) 1728 block_6_pad[0][0] __________________________________________________________________________________________________ block_6_depthwise_BN (BatchNorm (None, 10, 10, 192) 768 block_6_depthwise[0][0] __________________________________________________________________________________________________ block_6_depthwise_relu (ReLU) (None, 10, 10, 192) 0 block_6_depthwise_BN[0][0] __________________________________________________________________________________________________ block_6_project (Conv2D) (None, 10, 10, 64) 12288 block_6_depthwise_relu[0][0] __________________________________________________________________________________________________ block_6_project_BN (BatchNormal (None, 10, 10, 64) 256 block_6_project[0][0] __________________________________________________________________________________________________ block_7_expand (Conv2D) (None, 10, 10, 384) 24576 block_6_project_BN[0][0] __________________________________________________________________________________________________ block_7_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_7_expand[0][0] __________________________________________________________________________________________________ block_7_expand_relu (ReLU) (None, 10, 10, 384) 0 block_7_expand_BN[0][0] __________________________________________________________________________________________________ block_7_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_7_expand_relu[0][0] __________________________________________________________________________________________________ block_7_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_7_depthwise[0][0] __________________________________________________________________________________________________ block_7_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_7_depthwise_BN[0][0] __________________________________________________________________________________________________ block_7_project (Conv2D) (None, 10, 10, 64) 24576 block_7_depthwise_relu[0][0] __________________________________________________________________________________________________ block_7_project_BN (BatchNormal (None, 10, 10, 64) 256 block_7_project[0][0] __________________________________________________________________________________________________ block_7_add (Add) (None, 10, 10, 64) 0 block_6_project_BN[0][0] block_7_project_BN[0][0] __________________________________________________________________________________________________ block_8_expand (Conv2D) (None, 10, 10, 384) 24576 block_7_add[0][0] __________________________________________________________________________________________________ block_8_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_8_expand[0][0] __________________________________________________________________________________________________ block_8_expand_relu (ReLU) (None, 10, 10, 384) 0 block_8_expand_BN[0][0] __________________________________________________________________________________________________ block_8_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_8_expand_relu[0][0] __________________________________________________________________________________________________ block_8_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_8_depthwise[0][0] __________________________________________________________________________________________________ block_8_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_8_depthwise_BN[0][0] __________________________________________________________________________________________________ block_8_project (Conv2D) (None, 10, 10, 64) 24576 block_8_depthwise_relu[0][0] __________________________________________________________________________________________________ block_8_project_BN (BatchNormal (None, 10, 10, 64) 256 block_8_project[0][0] __________________________________________________________________________________________________ block_8_add (Add) (None, 10, 10, 64) 0 block_7_add[0][0] block_8_project_BN[0][0] __________________________________________________________________________________________________ block_9_expand (Conv2D) (None, 10, 10, 384) 24576 block_8_add[0][0] __________________________________________________________________________________________________ block_9_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_9_expand[0][0] __________________________________________________________________________________________________ block_9_expand_relu (ReLU) (None, 10, 10, 384) 0 block_9_expand_BN[0][0] __________________________________________________________________________________________________ block_9_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_9_expand_relu[0][0] __________________________________________________________________________________________________ block_9_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_9_depthwise[0][0] __________________________________________________________________________________________________ block_9_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_9_depthwise_BN[0][0] __________________________________________________________________________________________________ block_9_project (Conv2D) (None, 10, 10, 64) 24576 block_9_depthwise_relu[0][0] __________________________________________________________________________________________________ block_9_project_BN (BatchNormal (None, 10, 10, 64) 256 block_9_project[0][0] __________________________________________________________________________________________________ block_9_add (Add) (None, 10, 10, 64) 0 block_8_add[0][0] block_9_project_BN[0][0] __________________________________________________________________________________________________ block_10_expand (Conv2D) (None, 10, 10, 384) 24576 block_9_add[0][0] __________________________________________________________________________________________________ block_10_expand_BN (BatchNormal (None, 10, 10, 384) 1536 block_10_expand[0][0] __________________________________________________________________________________________________ block_10_expand_relu (ReLU) (None, 10, 10, 384) 0 block_10_expand_BN[0][0] __________________________________________________________________________________________________ block_10_depthwise (DepthwiseCo (None, 10, 10, 384) 3456 block_10_expand_relu[0][0] __________________________________________________________________________________________________ block_10_depthwise_BN (BatchNor (None, 10, 10, 384) 1536 block_10_depthwise[0][0] __________________________________________________________________________________________________ block_10_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_10_depthwise_BN[0][0] __________________________________________________________________________________________________ block_10_project (Conv2D) (None, 10, 10, 96) 36864 block_10_depthwise_relu[0][0] __________________________________________________________________________________________________ block_10_project_BN (BatchNorma (None, 10, 10, 96) 384 block_10_project[0][0] __________________________________________________________________________________________________ block_11_expand (Conv2D) (None, 10, 10, 576) 55296 block_10_project_BN[0][0] __________________________________________________________________________________________________ block_11_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_11_expand[0][0] __________________________________________________________________________________________________ block_11_expand_relu (ReLU) (None, 10, 10, 576) 0 block_11_expand_BN[0][0] __________________________________________________________________________________________________ block_11_depthwise (DepthwiseCo (None, 10, 10, 576) 5184 block_11_expand_relu[0][0] __________________________________________________________________________________________________ block_11_depthwise_BN (BatchNor (None, 10, 10, 576) 2304 block_11_depthwise[0][0] __________________________________________________________________________________________________ block_11_depthwise_relu (ReLU) (None, 10, 10, 576) 0 block_11_depthwise_BN[0][0] __________________________________________________________________________________________________ block_11_project (Conv2D) (None, 10, 10, 96) 55296 block_11_depthwise_relu[0][0] __________________________________________________________________________________________________ block_11_project_BN (BatchNorma (None, 10, 10, 96) 384 block_11_project[0][0] __________________________________________________________________________________________________ block_11_add (Add) (None, 10, 10, 96) 0 block_10_project_BN[0][0] block_11_project_BN[0][0] __________________________________________________________________________________________________ block_12_expand (Conv2D) (None, 10, 10, 576) 55296 block_11_add[0][0] __________________________________________________________________________________________________ block_12_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_12_expand[0][0] __________________________________________________________________________________________________ block_12_expand_relu (ReLU) (None, 10, 10, 576) 0 block_12_expand_BN[0][0] __________________________________________________________________________________________________ block_12_depthwise (DepthwiseCo (None, 10, 10, 576) 5184 block_12_expand_relu[0][0] __________________________________________________________________________________________________ block_12_depthwise_BN (BatchNor (None, 10, 10, 576) 2304 block_12_depthwise[0][0] __________________________________________________________________________________________________ block_12_depthwise_relu (ReLU) (None, 10, 10, 576) 0 block_12_depthwise_BN[0][0] __________________________________________________________________________________________________ block_12_project (Conv2D) (None, 10, 10, 96) 55296 block_12_depthwise_relu[0][0] __________________________________________________________________________________________________ block_12_project_BN (BatchNorma (None, 10, 10, 96) 384 block_12_project[0][0] __________________________________________________________________________________________________ block_12_add (Add) (None, 10, 10, 96) 0 block_11_add[0][0] block_12_project_BN[0][0] __________________________________________________________________________________________________ block_13_expand (Conv2D) (None, 10, 10, 576) 55296 block_12_add[0][0] __________________________________________________________________________________________________ block_13_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_13_expand[0][0] __________________________________________________________________________________________________ block_13_expand_relu (ReLU) (None, 10, 10, 576) 0 block_13_expand_BN[0][0] __________________________________________________________________________________________________ block_13_pad (ZeroPadding2D) (None, 11, 11, 576) 0 block_13_expand_relu[0][0] __________________________________________________________________________________________________ block_13_depthwise (DepthwiseCo (None, 5, 5, 576) 5184 block_13_pad[0][0] __________________________________________________________________________________________________ block_13_depthwise_BN (BatchNor (None, 5, 5, 576) 2304 block_13_depthwise[0][0] __________________________________________________________________________________________________ block_13_depthwise_relu (ReLU) (None, 5, 5, 576) 0 block_13_depthwise_BN[0][0] __________________________________________________________________________________________________ block_13_project (Conv2D) (None, 5, 5, 160) 92160 block_13_depthwise_relu[0][0] __________________________________________________________________________________________________ block_13_project_BN (BatchNorma (None, 5, 5, 160) 640 block_13_project[0][0] __________________________________________________________________________________________________ block_14_expand (Conv2D) (None, 5, 5, 960) 153600 block_13_project_BN[0][0] __________________________________________________________________________________________________ block_14_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_14_expand[0][0] __________________________________________________________________________________________________ block_14_expand_relu (ReLU) (None, 5, 5, 960) 0 block_14_expand_BN[0][0] __________________________________________________________________________________________________ block_14_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_14_expand_relu[0][0] __________________________________________________________________________________________________ block_14_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_14_depthwise[0][0] __________________________________________________________________________________________________ block_14_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_14_depthwise_BN[0][0] __________________________________________________________________________________________________ block_14_project (Conv2D) (None, 5, 5, 160) 153600 block_14_depthwise_relu[0][0] __________________________________________________________________________________________________ block_14_project_BN (BatchNorma (None, 5, 5, 160) 640 block_14_project[0][0] __________________________________________________________________________________________________ block_14_add (Add) (None, 5, 5, 160) 0 block_13_project_BN[0][0] block_14_project_BN[0][0] __________________________________________________________________________________________________ block_15_expand (Conv2D) (None, 5, 5, 960) 153600 block_14_add[0][0] __________________________________________________________________________________________________ block_15_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_15_expand[0][0] __________________________________________________________________________________________________ block_15_expand_relu (ReLU) (None, 5, 5, 960) 0 block_15_expand_BN[0][0] __________________________________________________________________________________________________ block_15_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_15_expand_relu[0][0] __________________________________________________________________________________________________ block_15_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_15_depthwise[0][0] __________________________________________________________________________________________________ block_15_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_15_depthwise_BN[0][0] __________________________________________________________________________________________________ block_15_project (Conv2D) (None, 5, 5, 160) 153600 block_15_depthwise_relu[0][0] __________________________________________________________________________________________________ block_15_project_BN (BatchNorma (None, 5, 5, 160) 640 block_15_project[0][0] __________________________________________________________________________________________________ block_15_add (Add) (None, 5, 5, 160) 0 block_14_add[0][0] block_15_project_BN[0][0] __________________________________________________________________________________________________ block_16_expand (Conv2D) (None, 5, 5, 960) 153600 block_15_add[0][0] __________________________________________________________________________________________________ block_16_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_16_expand[0][0] __________________________________________________________________________________________________ block_16_expand_relu (ReLU) (None, 5, 5, 960) 0 block_16_expand_BN[0][0] __________________________________________________________________________________________________ block_16_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_16_expand_relu[0][0] __________________________________________________________________________________________________ block_16_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_16_depthwise[0][0] __________________________________________________________________________________________________ block_16_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_16_depthwise_BN[0][0] __________________________________________________________________________________________________ block_16_project (Conv2D) (None, 5, 5, 320) 307200 block_16_depthwise_relu[0][0] __________________________________________________________________________________________________ block_16_project_BN (BatchNorma (None, 5, 5, 320) 1280 block_16_project[0][0] __________________________________________________________________________________________________ Conv_1 (Conv2D) (None, 5, 5, 1280) 409600 block_16_project_BN[0][0] __________________________________________________________________________________________________ Conv_1_bn (BatchNormalization) (None, 5, 5, 1280) 5120 Conv_1[0][0] __________________________________________________________________________________________________ out_relu (ReLU) (None, 5, 5, 1280) 0 Conv_1_bn[0][0] ================================================================================================== Total params: 2,257,984 Trainable params: 0 Non-trainable params: 2,257,984 _________________________________________________________________________ Explanation

The convolutional base created from the previous step is frozen and used as a feature extractor.

A classifier is added on top of it to train the top-level classifier.

Freezing is done by setting layer.trainable = False.

This step avoids the weights in a layer from getting updated during training.

MobileNet V2 has many layers, hence setting the model’s entire trainable flag to False would freeze all the layers.

When layer.trainable = False, the BatchNormalization layer runs in inference mode, and won’t update mean and variance statistics.

When a model is unfreezed, it contains BatchNormalization layer to do fine-tuning.

This can be done by passing training = False when the base model is called.

Else, the updates applied to non-trainable weights will spoil what the model has learned.

Top 5 Important Models Of Tensorflow

Introduction to TensorFlow Models

Hadoop, Data Science, Statistics & others

Various TensorFlow Models

The Heart of Everyday technology today is a neural network. These neural networks inspire deep Learning Models.

Neural Networks are like neurons in the human brain; these neurons have the capability to solve complex problems. These simple neurons are interconnected to each other to form a Layered Neural Network. This Layered neural network contains Input Layers, Output layers, Hidden Layers, Nodes, and Weights. The input layer is the first layer from where the input is given. The last layer is the output layer. The Hidden layers are the middle layers that carry out processing with the help of nodes (Neurons/operations) and weights (signal strength).

Various models of TensorFlow are:

1. Convolutional Neural Network

Layers in CNN:

Convolution layer: It is a Layer where we convolve the data or image using filters or kernels. These Filters we apply to the data through the sliding window. The depth of the filter is the same as the input. For a colour image, RGB values give the filter of depth 3. It involves taking the element-wise product of the filters’ image and then summing those values for every sliding action. The output of a convolution of a 3d filter with a colour image is a 2d matrix.

Activation Layer: The activations functions are between convolutional layers that receive an input signal, perform non-linear transformations and send the transformed signal to the next input layer of neurons. Different activations functions are sigmoid, tanh, Relu, Maxout, Leaky ReLU, ELU. The most widely used activation function is Relu. Non-linear transformations are used to make a network capable of learning and performing complex tasks.

Pooling Layer: This layer is responsible for reducing the number of parameters and complex computation in the network. At pooling, Average pooling and Max pooling are performed.

Fully Connected Layer: It connects every neuron to every previous neuron. It is the output layer on CNN. This is the last phase of CNN. CNN should be used when the input data is the image, 2d data can be converted to 1d, and when the model requires a great computation amount.

2. Recurrent Neural Network

A recurrent Neural network (RNN) is a network of at least one feedback connection forming a loop. RNN is powerful as to retain information for some time, to do temporal processing and learning sequences. RNN retain information means it store information about the past, which helps learn the sequence. The RNN can be – Simple RNN with at least one feedback connection or a fully connected RNN. One example of RNN is Text generation. The model will be trained with lots of words or with some author’s book. The model will then predict the next character(o) of the word(format). The auto prediction, which is now available in emails or smartphones, is a good example of RNN. RNN is invented for predicting sequences. RNN is helpful for video classification, Sentiment analysis, character generation, Image captioning, etc.

3. LSTM

LSTM (Long short term memory) is one of the most efficient problems for sequence prediction. RNN is quite effective when dealing with short term dependencies. RNN failed to remember the context and something which is said long before. LSTM networks are very good at holding long term dependencies/memories. LSTM is useful for handwriting recognition, handwriting generation, Music generation, image captioning, language translation.

4. Restricted Boltzman Machine

It is an undirected graphical model and has a major role in deep learning frameworks like TensorFlow. It is an algorithm used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modelling. In RBM, there are visible layers and Hidden layers. The first layer of RBM is the visible or input layer. The nodes perform calculations and are connected across layers, but no two nodes of the same layer are linked, so there is no internode communication restriction in RBM. Each node processes input and makes a stochastic decision on whether to transmit the input or not.

5. Autoencoders

Encoder: Takes input image and compresses it, and produces code.

Decoder: It reconstructs the original image from the code.

Autoencoders are Data specific; it means that they can compress images only on which it is trained. The Autoencoder, which is trained to compress images for cats, would not compress images of humans.

6. Self-Organizing Maps

Self-Organizing maps are helpful for feature reduction. They are used to map high dimensional data to lower dimensions which provide good visualization of the data. It consists of an input layer, weights, and Kohonen layers. Kohonen layer is also called a feature map or competitive layer. The self-organizing map is good for data visualization, dimensionality reduction, NLP, etc.

Conclusion

TensorFlow has huge capabilities to train different models with more great efficiency. In this article, we studied different deep learning models which can be trained on the TensorFlow framework. We hope that you have gained insight into some of the deep learning models.

What We Can Learn From New Zealand’s Successful Fight Against Covid

Follow all of PopSci’s COVID-19 coverage here, including news on federal policies, how to avoid spreading illness while participating in protests, and a state-by-state breakdown of confirmed cases.

On June 8, New Zealand announced that its last person known to be infected with COVID-19 has recovered. This means that, at least for now, the island nation has eliminated the disease. The country has tallied 1,504 confirmed and probable infections and 22 deaths.

Several other countries with smaller populations have reportedly also quashed transmission of COVID-19, including Fiji and Montenegro. A handful of other nations, including Iceland and Taiwan, have recently brought the number of active cases of COVID-19 to nearly zero.

New Zealand’s milestone offers a glimmer of hope for countries such as the United States, where in many communities the rate of new cases of COVID-19 is still rising. It indicates that a combination of social distancing, testing and contact-tracing, and clear communication can have a huge impact on reining in the virus. While it’s far too late to prevent COVID-19 from gaining a foothold in the U.S., we can still learn from New Zealand’s coordinated and aggressive response.

“This is an extraordinary public health achievement,” Gavin Yamey, director of the Duke Center for Policy Impact in Global Health, told Popular Science in an email.

Part of the reason for New Zealand’s success lies in its small size and isolated location (the country is home to around 5 million people—a population roughly equivalent to that of South Carolina). More importantly, though, New Zealand reacted swiftly after confirming its first case on February 28. The country opted for an ambitious strategy designed to stamp out the virus rather than mitigate its effects, says Ingrid Katz, the associate faculty director of the Harvard Global Health Institute.

“Elimination strategies utilize strong response measures at the beginning of the pandemic that are then eased over time,” she says. “Mitigation, on the other hand, focuses on flattening the curve, and the response becomes more intense over time.”

New Zealand ramped up widespread diagnostic testing early, created a meticulous nationwide contact tracing system, called for a strict stay-at-home order, and closed its borders while the number of confirmed cases was still very low, Katz notes. As a result, it managed to avoid the explosive epidemics seen in other parts of the world.

“The country enacted a shutdown early enough so that a large body of community transmission did not have time to become established,” says William Hanage, epidemiologist in the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health. “An early shutdown also means the shutdown does not need to last as long in order to bring the virus under control. The delays in shutdowns elsewhere have been a major problem.”

Crucially, the government of New Zealand also made an effort to unify people and make sure they understood what to expect when the country went into lockdown. “They had one of the world’s finest communicators in [Prime Minister] Jacinda Ardern, who explained clearly and frequently what was happening and why,” Yamey says. “She made people feel that they were part of a communal effort to care for each other. She promoted solidarity.”

Embed from Getty Images

Unfortunately, many of the techniques that helped to bring COVID-19 under control in New Zealand would not work in the United States.

“New Zealand succeeded with a coordinated, strict, country-wide lockdown, which was not feasible in our country due to lack of federal leadership,” Katz says. She also notes that the heavy restrictions that New Zealand placed on travel would be less feasible in a country as large as the United States.

This means that it’s unlikely that the U.S. will be able to achieve elimination anytime soon (although social distancing and shutdowns have still managed to avert an estimated 60 million infections in the country, researchers reported this week). However, we might be able to apply some lessons from New Zealand to our response going forward.

“While it may seem like it is too late for the United States, we can use New Zealand as a model for responding to a second wave of coronavirus, as well as for scaling up testing and contact tracing,” Katz says.

During a news conference, Ardern acknowledged that it’s fairly certain that the country will eventually see more new cases of COVID-19. “Elimination is not a point in time,” she said. “It is a sustained effort.” Although New Zealand has relaxed all its restrictions on events, restaurants, public transport, and other services, its borders will remain closed for the foreseeable future to everyone except residents and their immediate families.

Because the vast majority of New Zealand’s population is still susceptible to COVID-19, it will need to be ready to nip any future outbreaks in the bud. “That’s why the country continues to stress border management—it will require 14-day quarantine for anyone entering—as well as face masks, physical distancing, strong surveillance, and having a world class ‘test and trace’ system ready to go in case they see a new case,” Yamey says.

Several of the strategies that New Zealand is now using to prevent fresh outbreaks of COVID-19 could also be used in the U.S. For example, a contact tracing app released by New Zealand’s Ministry of Health encourages people to scan QR placed codes at the entrances of businesses and other public buildings to create a “digital diary” of places they have visited (similar apps are also being used in South Korea, Italy, and a number of other countries).

The elimination of COVID-19 in New Zealand highlights both the impact of proactive action against the disease and the importance of remaining vigilant against a virus that is likely to remain with us for many months yet.

“They must work to maintain these gains and utilize lessons learned during their first run-in with COVID-19,” Katz says. “New Zealand shows us that COVID-19 can be eliminated with strong governmental action, but also acts as a reminder that the threat of COVID-19 persists, and that they must be ready to act swiftly and aggressively again.”

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