Trending December 2023 # Remini Pregnant Ai Generator To Create Pregnancy Photoshoot # Suggested January 2024 # Top 13 Popular

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Remini is an AI photo enhancer that can generate stunningly realistic photos of yourself in different scenarios such as being pregnant, wearing a wedding dress, or traveling to exotic locations. The app uses artificial intelligence to analyze your photos and create new ones that match the style and pose of a model image. One of the most popular filters on the app is the Remini Pregnant AI, which simulates how you would look like with a baby bump.

Many TikTok users have been trying out this filter and sharing their results online with some even fooling their followers into thinking they are actually expecting. In this article, we will show you how to use the Remini Pregnant AI Photoshoot Generator and have fun with it. We will also explain how it works and what are some of the benefits of using it.

What is Remini App?

Remini App is a photo enhancer app that uses artificial intelligence (AI) to create hyper realistic photos of yourself. The app allows you to upload your selfies and choose from various models that will copy your style and pose and apply them to your results. You can also choose different angles, expressions, locations and looks for your photos.

Remini AI is also a photo generator app that uses AI to create photos of yourself in different scenarios. You can use the app to try out new hairstyles, makeup looks, outfits or even see what your future baby might look like. If you want more information about the Remini Baby AI Generator, see the article How the Remini Baby AI Generator Can Show Your Future Child. The app offers a variety of different models to choose from, each with its own unique style.

What is Remini Pregnant AI Generator?

One of the most popular filters on Remini AI app is the pregnant AI model. This filter uses AI to simulate pregnancy by adding a baby bump to your photo or this filter seamlessly blends your face onto a heavily pregnant model making it look realistic.

Many users have started to use this Remini pregnant AI generator and share on TikTok, where it has become a viral trend among users. Many TikTokers are using the filter to show their followers what they would look like if they were expecting.

How to Use the Remini Pregnant AI Generator

Here are the steps on how to use the Remini AI Pregnant Photoshoot Generator:

Step 1: Download the Remini app from the App Store or Google Play. The app is free to download and use, but it has some premium features that require a subscription.

Step 2: Open the app and sign in with your account. You can use your email, phone number, Facebook, or Google account to sign in.

Step 3: Tap on the “AI Photo Generator” tab at the bottom of the screen. This is where you can access various AI-powered features, such as Baby AI Generator, Face Swap, Age Progression, etc.

Step 4: Select the “AI Pregnancy Model” option. This is the feature that will help you generate photos of yourself with a pregnant belly. Upload 8 selfies of yourself. You can choose selfies from your gallery or take new ones with your camera. The selfies should be clear and well-lit, and show your face and upper body. The app will use these selfies to create your dataset, which will influence the results of the generator.

Step 5: Wait few minutes for the app to process your dataset and train the AI model. You only need to do this once, and then you can use the model to generate unlimited photos of yourself.

Step 6: Choose a model image that you like. Tap on the “Generate” button and wait for a few seconds for the app to create your photo. Save or share your photo. You can save your photo to your device or share it with your friends on social media platforms, such as Instagram, Facebook, TikTok, etc.

How Does Remini Pregnant AI Work

The discriminator is responsible for judging whether the images are real or fake. The discriminator tries to catch the generator by spotting any flaws or inconsistencies in the images. The generator and the discriminator compete with each other in a game-like scenario, where they learn from their mistakes and improve their performance over time. The result is a high-quality image that looks like a real photo of yourself with a pregnant belly.

Why is the Remini Pregnant AI Trending on TikTok?

The pregnant AI generator has gained popularity on TikTok for several reasons. First of all, the filter is fun and entertaining to use, as it allows users to experiment with their appearance and imagine themselves in a different life stage. Some users are using the filter to express their desire or curiosity for having children, while others are using it for comedic purposes.

Benefits and Drawbacks of the Remini Pregnant AI

The Remini pregnant AI generator has both benefits and drawbacks for its users and viewers. On one hand, the filter can be seen as a positive and harmless way of having fun and exploring one’s identity and preferences. The filter can also be used as a tool for self-expression, creativity, or education.

On the other hand, the filter can also pose some ethical and social issues for its users and viewers. For example, some users may use the filter to deceive or manipulate others with fake pregnancy claims, which can have serious consequences for their relationships or reputation. Moreover, some viewers may feel offended or hurt by the filter, especially if they are struggling with infertility or pregnancy loss.

How do I get the Pregnant AI generator on TikTok?

You need to download and open the Remini app, select AI Photos, upload your selfies, choose the pregnant filter, and generate your photo. Then, you can save the photo to your camera roll and upload it to TikTok.

How much does the Remini pregnant AI generator cost?

The Remini app offers a free trial for 3 days, after which you need to pay for a Lite or Pro subscription to use the app. The Lite subscription costs $4.99 per month or $29.99 per year, while the Pro subscription costs $9.99 per month or $59.99 per year.

Is the pregnant AI generator accurate?

The pregnant AI generator is not meant to be accurate or realistic, but rather a fun and entertaining way of seeing yourself in a different scenario. The filter does not take into account your genetics, health, or other factors that may affect your pregnancy appearance.

Is the pregnant AI generator safe? Can I use the pregnant AI generator with someone else’s photo?

You should not use the pregnant AI generator with someone else’s photo without their consent, as this may violate their rights and privacy.


In conclusion, the pregnant AI generator is a fascinating and controversial filter that has taken over TikTok. The filter uses AI to create realistic photos of users with baby bumps, which can be fun and entertaining, but also problematic and insensitive. Whether you love it or hate it, you have to admit that it’s impressive what technology can do nowadays.

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Free Ai Resume Maker: Create Your Own Ai Resume

How to Build your Resume with Appy Pie’s Online CV Generator?

Appy Pie’s resume builder is one of the most popular CV makers because it simplifies the process into 3 easy steps:

Find the right template.

Sign up/Log in to Appy Pie CV creator. Find the right pre-set template from our vast library of options. Make sure the template that you choose matches the job type. You can also start from scratch if you wish to build an entirely unique resume.

Add your details

Add your details in the right boxes and columns using the easy-to-use drag-and-drop editor to create your CV. You can also include images if your job is creative or you need to showcase work samples.

Finalize, save, and share

Once you have added the text and images, you can finalize it with filters. Appy Pie’s free resume maker lets you save the resume, print it directly, share it, or even embed it on your blog or website.

Make your own Job-Winning Resume with Appy Pie’s Free Online Resume Maker

A resume is a one or two-page document that highlights your top skills. While the real test of your talent will be the interview, to be able to get that interview, you need a killer resume. It is like a marketing copy that aims to market your skills and talent. Most hiring managers get hundreds of resumes each day. And most of them look quite similar. If you want your resume to stand out from the pile of other papers, you need a powerful resume creator.

Among all the available resume builders online, Appy Pie’s CV generator is the best platform to create CV online. Appy Pie’s professional resume builder is easily the best resume builder and has gained massive popularity.

A professional CV maker lets you make your CV unique and attractive so that your future employers are compelled to shortlist you immediately, taking you a step closer to your dream job. Seize the opportunity with a creative, professional, and effective resume. Appy Pie’s online CV maker – taking you one step closer to your dream job.

You need no tech skills and no prior experience to use this unique CV maker. Beautiful visual resumes for creative jobs and minimalistic, professional-looking CVs to impress the traditional hiring managers, Appy Pie’s CV builder and online resume editor has it all.

Why Choose Appy Pie Design to Create a Professional Resume?

There are many free online resume builders to create resumes online that can help you apply for your dream job with confidence. The free CV maker from Appy Pie stands apart from other free CV builders.

Templates for Every Industry

Creative, visual, minimalistic, or formal. Choose from a wide range of resume templates and create a resume to suit the kind of job you are looking for. Appy Pie’s resume maker has the right template for all industries. The right resume helps job seekers highlight their skills and land their dream job with ease.

Easy and Free

Making professional resumes shouldn’t take up all your time. With the free resume builder from Appy Pie, you can create impressive and effective resumes with ease. The drag-and-drop editor lets you choose, customize, and create CVs and cover letters with minimum effort.

Save Multiple Versions

Every job is unique. And you need to tweak your resume for each job that you apply to. Appy Pie’s online resume maker lets you create multiple versions of your CV. Make suitable versions of your resume for the jobs that you want to apply to — landing a job made quick and easy.

Secure Password Generator Using Python

This article was published as a part of the Data Science Blogathon.

In this article, we will see how to build the password generator. Password generate is a python application that will generate the random string of the desired length. Nowadays we are using many applications and websites that require passwords. Setting strong passwords is very important to avoid any attack by attackers and to keep our information safe. Now we will build this application using python, Tkinter, and pyperclip.


Let us see the requirements that are needed for us to build this application.

Python: Python is the programming language that we will use to build the application.

Tkinter: Tkinter is a Graphical User Interface library. using Tkinter is one of the easiest ways to build any GUI-based applications. In this application, we use Tkinter to build the window where we generate a random password.

pyperclip: Pyperclip is a module in python that is used for copying and pasting the text. So in our application after generating the password we will also have an option to copy the password.

Random: Passwords are generated randomly so to generate these passwords randomly we use the random module. This random module generates the random numbers.

Strings: The string module in python helps in creating and customizing strings.

Now let us move into the implementation part.

For any project, we have to start by importing the required modules. For our application, we will import Tkinter, Pyperclip, Random, and strings.

If these libraries are not preinstalled, then you have to install them and then you have to import them. For installing these libraries you have to use pip install to install them. I basically use jupyter notebook to run the code so I open the anaconda prompt and run these commands to install the libraries. You can use any prompt to install them.

To install Tkinter

pip install tkinter

To install pyperclip

pip install pyperclip

To install random

pip install random

To install strings

pip install strings

Now import all the libraries. From Tkinter import all the libraries. So to import everything that has in that particular module we use *.

from tkinter import * import random, string import pyperclip Initialize Window

Our next step is to initialize the window where we generate the password by giving the number of digits in the password. For this we use Tkinter. First, we initialize the win variable with Tk() function. Using the geometry function we will set the width and height of the window. and using the title function we will pass the title of the window. Here we set it to “PASSWORD GENERATOR” with height as 500 and width as 500. Here using configure method I set the background color of the window.

win = Tk() win.geometry("500x500") win.title("PASSWORD GENERATOR") win.configure(bg="#ffc252")

At the top of the window, the text is placed saying PASSWORD GENERATOR in bold letters with ariel font and font size 18 and with some background color. Here we use the Pack() function to arrange the widgets in the window.

Label(win, text = 'PASSWORD GENERATOR' , font ='ariel 15 bold',bg="#ffc252").pack()

Now we have to place an input box where the user can input the number of digits the password should contain. Before that, we place the text, “PASSWORD LENGTH” with Arial font and font size 10 with bold letters. Using IntVar() function, we can set integer data as this function holds integer data, and later we can retrieve the data.  Spinbox() provides a range of values for the user to input. Here, users can enter the digits or scroll the numbers and select the length of the password. And here it generates passwords from lengths 8 to 32.

Python Code:

The text and the spinbox will look like this.

Define Password Generator

Coming to StringVar() f also similar to the IntVar() function but here stringVar() function holds string data. Now we define a function called Generator which generates random passwords. Firstly the password is initialized with an empty string. Setting a password that contains only numerical digits or with only alphabets doesn’t provide enough security for your system or any application. For any password, it should be a combination of uppercase letters, lower case letters, numerical digits, and some punctuations. For the first four digits of the password, we set it to, a random uppercase letter, random lowercase letter, random digit, and random punctuation. And remaining values will be the random combination of uppercase, lowercase, digits, and punctuations.

pass_str = StringVar() def Generator(): password = '' " for x in range (0,4): password = random.choice(string.ascii_uppercase) + random.choice(string.ascii_lowercase) + random.choice(string.digits) + random.choice(string.punctuation) for y in range(pass_len.get()- 4): password = password + random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits + string.punctuation) pass_str.set(password) Generate Buttons

Now create a button where it follows the Generator command and the Generator is the function that we defined for generating the password. The button contains the text “GENERATE PASSWORD” on it with blue background and white foreground. These buttons are very user-friendly with a nice GUI. You don’t need to stick to the UI that was defined in this article. You can change some text fonts, colors, and many more and you can make your window more beautiful. And you can play around with this and can make your window according to your expectations.

Generate=Button(win, text = "GENERATE PASSWORD" , command = Generator,padx=5,pady=5 ) Generate.configure(background="blue", foreground='white',font=('ariel',10,'bold')) Generate.pack(side=TOP,pady=20) Entry(win , textvariable = pass_str).pack()

The generate password button will look like this

Our next step is to copy the password. We use pyperclip to copy the password. Get the string in the pass_str function and then copy it using pyperclip and we create a button which follows the command copy_password with the text “COPY TO CLIPBOARD” button next we configure the button that means how the button should look like. The button contains blue color background and white color foreground and we use ariel font with font size 10 and bold letters. Here we use the pack() function to organize the widgets according to the size of the frame And we set some top pady here.

def Copy_password(): pyperclip.copy(pass_str.get()) copy=Button(win, text = 'COPY TO CLIPBOARD', command = Copy_password) copy.configure(background="blue", foreground='white',font=('ariel',10,'bold')) copy.pack(side=TOP,pady=20)

The copy to the clipboard button will look like this.

Now run the main loop to execute the entire application.


Here you can see I created the password of 8 digits and the password that I got is ” Sh8_90Ny”. This is a user-friendly application and a very useful application.


Password Generator is an interesting, exciting, and thrilling application. We can use a secret password generator for building strong passwords and this password generator doesn’t store any password anywhere. I clears all the data as soon as you left the window. So without any hesitation, you can build your secret and strong passwords using this password generator.

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A Compressive Guide To Build Image Caption Generator

This article was published as a part of the Data Science Blogathon.


Image caption generator is the most fascinating application I found while working with NLP. It’s cool to train your system to label the images you feed to it. As interesting as it sounds, it is equally challenging to implement this application. It is a bit harder than object detection and image classification, which

Before you start reading this blog make sure you have some idea about how we process images and texts, LSTM (Long-Short term memory), and CNNs (Convolutional neural networks). You can check out my analytics profile if you want to read about these topics. Let’s start building our application without wasting our time.

Understanding the Data

The dataset we have contains about 8000 images each of which is annotated with 5 captions that provide a description of entities in the image. We have 6000 images for training, 1000 for validation, and 1000 for testing. The dataset differs in various aspects such as the number of images, number of captions, the format of the captions, and the image size.

The dataset will have the following files after you download it: –

contains all the 8000 images we have

has the image id along with the 5 captions for that particular image.

– The image ids of the train images.

– The image ids of the test images

Model Architecture

If you look at this carefully, you will see there are two models, one is the image-based model, and the other is a language-based model. We will use CNNs to extract the image features and then LSTM to translate the features and objects given by CNN into a natural sentence. We will use CNN as an encoder and LSTM as a decoder.

To encode our images, we will use transfer learning. We will use VGG-16 for the extraction of image features. You can use many more models like InceptionV3, ResNet, etc.

Building the Image Caption Generator

We will use Google collab and the TensorFlow library to build this application.

Importing Necessary Libraries #Import Necessary Libraries from os import listdir from pickle import dump from keras.applications.vgg16 import VGG16 from keras.preprocessing.image import load_img from keras.preprocessing.image import img_to_array from keras.applications.vgg16 import preprocess_input from keras.models import Model from pickle import load from numpy import array import tensorflow from pickle import load from chúng tôi import Tokenizer from keras.preprocessing.sequence import pad_sequences from tensorflow.keras.utils import to_categorical from keras.utils.vis_utils import plot_model from keras.models import Model from keras.layers import Input from keras.layers import Dense from keras.layers import Embedding from keras.layers import Dropout from keras.layers.merge import add from keras.callbacks import ModelCheckpoint import re import string Extracting features from the Images def extract_features(directory): VGG_model = VGG16() VGG_model = Model(inputs= VGG_model.inputs, outputs=VGG_model.layers[-2].output) print(VGG_model.summary()) attribute = dict() for i in listdir(directory): file_name = directory + '/' + i image = load_img(file_name, target_size=(224, 224)) image = img_to_array(image) image = image.reshape((1, 224, 224, 3)) image = preprocess_input(image) attributes = VGG_model.predict(image) image_id = file.split('.')[0] attribute[image_id] = attributes return attribute

features = extract_features(directory) print(‘Extracted Features: %d’ % len(attribute)) dump(attribute, open(‘features.pkl’, ‘wb’))

In the above code, we are reconstructing the VGG-16 model. By popping off the last layer. This model is generally used to classify the images, but in this task, we don’t want to classify any image so we will be removing the last layer because we are more interested in the internal representation of the images.

After popping off the last layer, we use a for loop to go through every image in the dataset. We have also initialized an empty dictionary in which we will save our extracted features of the image. Since we can not send the image directly to our model, we will have to reshape it according to the preferred size of the model. So, we have used reshape function to reshape the image and then saved the features of the images in the dictionary.

Loading the text data # load doc into memory def load_doc(file_name): file = open(file_name, 'r') caption = file.close() return caption # extract captions for images def load_descriptions(document): map = dict() # process lines for line in document.split('n'): tokens = line.split() if len(line) < 2: continue image_id, image_description = tokens[0], tokens[1:] # removing filename from image id image_id = image_id.split('.')[0] # converting the description tokens back to string image_description = ' '.join(image_description) # create the list if needed if image_id not in map: map[image_id] = list() # store description map[image_id].append(image_description) return map

Here we are making a function to load the text data. This function will be used to load chúng tôi which contains descriptions for all the images. This file contains unique image ids with their respective descriptions.

Next, we are creating a function that will extract the descriptions of all the images using a for a loop. Each of the image identifiers maps to one or more text-based descriptions. So, what this function will do is it will create a dictionary and will put all the captions in a list with the particular image id.

By using the for loop, we are reading each sentence from the document by splitting them based on a new line character called ‘n’. Then we make tokens of the descriptions by splitting them based on white spaces.

After this, we are checking if any image id has no caption, if the length of the description is less than 2, then I don’t want my loop to break, I’ll continue looping over all the sentences. You can use some other threshold if you want. I analyzed the data a bit and then decided to make the threshold 2.

Next, I am storing the image descriptions for corresponding images id’s. ‘token [0]’ has the image id, and after 0 elements in token, we have all the words in it, as you can see from the fig above, so for image descriptions we use ‘token[1:]’. I am then storing the unique image id which means removing the ‘jpg#’ part from the image id by splitting it on ‘.’ and storing the 0th element in a list. The image_id will look like this:

After this, I am joining back all the tokens to the text string using the ‘join’ function and save it in a dictionary

Cleaning the text data def clean_descriptions(desc): table = str.maketrans('', '', string.punctuation) for key, desc_list in desc.items(): for i in range(len(desc_list)): description = desc_list[i] # tokenize description = description.split() # converting the text to lower case description = [term.lower() for term in description] # removing the punctuation from each token description = [w.translate(table) for w in description] # removing the hanging 's' and 'a' # removing the tokens with numbers in them description = [termfor term in description if term.isalpha()] # storing it as astring desc_list[i] = ' '.join(description)

This function is self-explanatory and is very easy to understand. We are looping over all the descriptions, tokenizing them, looping on all the words by using list comprehension, and then cleaning them one by one. After executing this function, you will see the descriptions like:

def to_vocabulary(desc): all_desc = set() for key in desc.keys(): [all_desc.update(d.split()) for d in desc[key]] return all_desc

Now we are creating a function called ‘to_vocabulary’ which will transform the descriptions into a set so that we can get an idea of the size of our dataset vocabulary. It takes descriptions as the argument, then for each key, we are picking up the corresponding description such that all the words in that description are first split on the basis of white spaces and then those words are added to a set called ‘all_desc’. You will get a dictionary with all the unique words in the dataset.

After this, we will save the descriptions using this function called ‘save_descriptions’.

def save_descriptions(desc, file_name): lines = list() for key, desc_list in desc.items(): for description in desc_list: lines.append(key + ' ' + description) data = 'n'.join(lines) file = open(file_name, 'w') file.write(data) file.close()

# load descriptions doc = load_doc(filename) # parse descriptions descriptions = load_descriptions(doc) print(‘Loaded: %d ‘ % len(descriptions)) # clean descriptions clean_descriptions(descriptions) # summarize vocabulary vocabulary = to_vocabulary(descriptions) print(‘Vocabulary Size: %d’ % len(vocabulary)) # save to file save_descriptions(descriptions, ‘descriptions.txt’) get to know what we are trying to do.

# load doc into memory def load_doc(file_name): file = open(file_name, 'r') text = file.close() return text def load_set(file_name): document = load_doc(file_name) data_set = list() # process line by line for line in document.split('n'): # skip empty lines if len(line) < 1: continue # get the image identifier identifier = line.split('.')[0] data_set.append(identifier) return set(data_set) # load clean descriptions into memory def load_clean_descriptions(file_name, data_set): # load document doc = load_doc(file_name) desc = dict() for line in doc.split('n'): # split line by white space tokens = line.split() # split id from description image_id, image_desc = tokens[0], tokens[1:] if image_id in data_set: # create list if image_id not in descriptions: desc[image_id] = list() # wrap desc in tokens description = 'startseq ' + ' '.join(image_desc) + ' endseq' # store desc[image_id].append(description) return desc

The next function we see is the load clean description which is used to load the clean text descriptions for a given set of identifiers, thereby returning a dictionary of identifiers and the corresponding list of text descriptions. If you remember, we made a function named ‘save_description’ which saved all the clean text or descriptions in a file named “’descriptions.txt”, so we will use that cleaned file to load the clean descriptions. After separating the image_id and image_desc, we are initializing an if statement which skips all the images which are not present in the training dataset.

We then add ‘startseq’ at the start of all the descriptions and ‘endseq’ at the end of all the descriptions. We do this because we need the first word to start the process of image captioning and the last word to signal the end of the caption.

# load photo features def load_photo_features(file_name, data_set): # load all features all_features = load(open(file_name, 'rb')) # filter features features = {k: all_features[k] for k in data_set} return features  Encoding the Text Data def to_lines(desc): all_description = list() for key in desc.keys(): [all_description.append(d) for d in desc[key]] return all_description def create_tokenizer(desc): lines = to_lines(desc) tokenizer = Tokenizer() tokenizer.fit_on_texts(lines) return tokenizer def max_length(desc): lines = to_lines(desc) return max(len(d.split()) for d in lines)

Since our model can not understand strings, we need to convert all our descriptions into numbers so that our model can understand this data. The function ‘to_lines’ is used to convert the dictionary of clean descriptions into a list of descriptions, basically creating individual lines of descriptions and storing them in a list. In the next function, we use the ‘to_lines’ function to convert the dictionary to a list. We then define a tokenizer object using the Tokenizer() class and use the ‘fit_on_texts’ method to create individual tokens from the given descriptions.

‘max_length’ function is used to calculate the descriptions with the most words. It will loop over all the descriptions and then split them on the basis of white spaces and then calculate the description length using ‘len’ function and return the maximum length of the description in our dataset. You will see that this function will be used for padding later on.

train = load_set(filename) print(‘Dataset: %d’ % len(train)) # descriptions train_descriptions = load_clean_descriptions(‘/content/gdrive/MyDrive/image_caption/descriptions.txt’, train) print(‘Descriptions: train=%d’ % len(train_descriptions)) # photo features train_features = load_photo_features(‘/content/gdrive/MyDrive/image_caption/features.pkl’, train) print(‘Photos: train=%d’ % len(train_features)) # prepare tokenizer tokenizer = create_tokenizer(train_descriptions) vocab_size = len(tokenizer.word_index) + 1 print(‘Vocabulary Size: %d’ % vocab_size) # determine the maximum sequence length max_length = max_length(train_descriptions) print(‘Description Length: %d’ % max_length)

We are now ready to encode the text. Please note that each description will be split into words and the model will actually generate the next word by providing one word and an image feature as input. Let’s understand this in an easier way:

If you see the figure above, we are providing two inputs to our model. 1st is the image features and 2nd is the encoded text. The output is the next word in the sequence. Now the first word of the description will be provided to the model as an input along with the image to generate the next word. Just like the word ‘man’ is generated and subsequently ‘is’ and ‘driving’ is generated. The generated words will be combined and recursively provided as input to generate a caption for an image.

 # create sequences of images, input sequences and output words for an image def create_sequences(tokenizer, max_length, desc_list, photo): x_1 = [] x_2 = [] y = [] for description in desc_list: # encode the sequence sequence = tokenizer.texts_to_sequences([description])[0] # spliting one sequence into multiple x and y pairs for i in range(1, len(sequence)): # split into input and output pair input_seq, output_seq = sequence[:i], sequence[i] # pad input sequence in_seq = pad_sequences([input_seq], maxlen=max_length)[0] # encode output sequence out_seq = to_categorical([output_seq], num_classes=vocab_size)[0] # store x_1.append(photo) x_2.append(input_seq) y.append(output_seq) return array(x_1), array(x_2), array(y) #Below code is used to progressively load the batch of data # data generator, will be used in model.fit_generator() def data_generator(desc, photos, tokenizer, max_length): # loop for ever over images while 1: for key, desc_list in desc.items(): # retrieving the photo features photo = photos[key][0] input_img, input_seq, output_word = create_sequences(tokenizer, max_length, description_list, photo) yield [[input_img, input_seq], output_word]

These two functions are a bit complex, but let’s try understanding them. We already know how we need our sequence to be. So before making a sequence, we first need to encode it. We will use this ‘create_sequences’ function in another function called ‘data_generator.’ If you look carefully, we are looping over all the descriptions one by one. The first loop is initiated in the data generator function, which gives a list of all the descriptions of a particular image. If you run:

 for key, desc_list in descriptions.items(): print(desc_list)

The output will be:

A description is a dictionary that contains all the clean captions and the image ids. The second loop is initiated in the ‘create_sequences’ function, looping over all the descriptions.

for key, desc_list in descriptions.items(): #1st loop) for desc in desc_list: #(2nd loop) print(desc)

The output will be:

Look at the create_sequences function; we are initializing some empty lists and looping over all the captions. Then we use the ‘text_to_sequences’ method to encode the caption. The output of

 for desc in desc_list: # encode the sequence seq = tokenizer.texts_to_sequences([desc])[0]

will be somewhat like this:

All your captions will be encoded in numbers. If you don’t know how ‘texts_to_sequences’ works, I would strongly recommend going through this link.

Now, after encoding, we are starting another for loop, which will help us in making the input and output sequence.

 for key, desc_list in descriptions.items(): for desc in desc_list: seq = tokenizer.texts_to_sequences([desc])[0] print(seq) for i in range(1, len(seq)): in_seq, out_seq = seq[:i], seq[i] print(in_seq,out_seq)

the output of this will be:

Then we are using the to_categorical method to one hot encode the output. You can read more about this method on this link.

Model Building # define the captioning model from keras.layers import LSTM def define_model(vocab_size, max_length): # feature extractor model inputs_1 = Input(shape=(4096,)) fe_1 = Dropout(0.5)(inputs_1) fe_2 = Dense(256, activation='relu')(fe_1) # sequence model inputs_2 = Input(shape=(max_length,)) se_1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2) se_2 = Dropout(0.5)(se_1) se_3 = LSTM(256)(se_2) # decoder model decoder_1 = add([fe_2, se_3]) decoder_2 = Dense(256, activation='relu')(decoder_1) outputs = Dense(vocab_size, activation='softmax')(decoder_2) # tie it together [image, sequence] [word] model = Model(inputs=[inputs1, inputs2], outputs=outputs) # summarize model print(model.summary()) return model

We will combine 2 encoder models (feature extractor model and sequence model) and then feed them to the decoder model. The feature extractor model is built to take input in the form of a vector containing 4096 elements. We are using the Input class of keras.layers to do this. Hence this model expects the input image feature to be a vector of 4096 elements. Then we are using a dropout layer for regularization, which is usually used to reduce the overfitting of the training dataset. Then we are using a dense layer to process 4096 elements of the input layer, producing a 256-element representation of an image.

The sequence model takes input sentences or descriptions to be fed to the embedding layer. The input sequences are of length 34 words, and the parameter mask_true is set as true to ignore the padded values. Then we are using a dropout layer to reduce overfitting. After this, we will use an LSTM layer having 256 memory units to process these text descriptions of the sentences.

Once the encoder model is ready, the decoder model merges the vectors from both the input models by doing an addition operation. Next, we are utilizing a fully connected layer with 256 units.

 # train the model model = define_model(vocab_size, max_length) epochs = 20 steps = len(train_descriptions) for i in range(epochs): # create the data generator generator = data_generator(train_descriptions, train_features, tokenizer, max_length) # fit for one epoch model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1) # save model'model_' + str(i) + '.h5')

In the next cell, we are training our model by saving it after each epoch, so by the end of all the 20 epochs, we will have 20 separate .h5 files in our directory. Since our system doesn’t have enough ram, we will use progressive learning to train our model. You can learn about progressive learning from this link. The model architecture looks something like this:

Predicting the Image Caption

To evaluate our model, we will use BLEU Score, which stands for Bilingual Evaluation Understudy score summarizes how close a particular generated text is to the expected text. It is prevalent in machine translation, but it can be used to evaluate other models such as image captioning, text summarization, speech recognition, etc.

Suppose we have a Spanish sentence: Hoy hace buen tiempo

Which we want to convert into English sentences. Now there can be multiple English translations that are equally appropriate translations of the Spanish sentence. Like

Statement 1 – The weather is nice today,

Statement 2 – The weather is excellent today.

Similarly, in the case of an image captioning problem, we can have several captions for a single image. As we saw in our problem, we have 5 captions for each image. So, the question is, how can we evaluate the associated models with equally good answers?

In classification type of problems where we have to predict if the image is of a dog or cat, here we just have only 1 answer, and we can measure the accuracy there. But what should we do when we have multiple answers? It would be challenging to measure accuracy in that case, so we incorporate BLEU Score in such cases. BLEU Score helps measure how good a particular machine-generated caption is by automatically computing the score. BLEU Score will be high if the predicted caption is close to the actual caption. If there is a complete mismatch, the score would be close to 0. Let’s see how to implement this on google collab.

# map an integer to a word def word_for_id(integer, tokenizer): for word, index in tokenizer.word_index.items(): if index == integer: return word return None

This function maps a given word using its corresponding word id. This function takes an integer value and tokenizer as an input argument. Inside this function, we are just checking if for a given the word and the corresponding index we have a match or not. If there is a match, return the actual word; otherwise, return NONE.

# generate a description for an image def generate_desc(model, tokenizer, photo, max_length): # seed the generation process in_text = 'startseq' for i in range(max_length): # integer encode input sequence sequence = tokenizer.texts_to_sequences([in_text])[0] # pad input sequence = pad_sequences([sequence], maxlen=max_length) # predict next word yhat = model.predict([photo,sequence], verbose=0) # convert probability to integer yhat = argmax(yhat) # map integer to word word = word_for_id(yhat, tokenizer) if word is None: break # append as input for generating the next word in_text += ' ' + word # we will stop if we predict the endseq if word == 'endseq': break return in_text

The following function is ‘generate_desc,’ which is required to generate the caption for a given image in a training or test dataset. The model takes 4 arguments: model, tokenizer, photo, and max_length. This function is self-explanatory. We are just trying to predict the captions for any given image.

Evaluating the Results # evaluate the skill of the model def evaluate_model(model, descriptions, photos, tokenizer, max_length): actual, predicted = list(), list() # step over the whole set for key, desc_list in descriptions.items(): # generate description yhat = generate_desc(model, tokenizer, photos[key], max_length) # store actual and predicted references = [d.split() for d in desc_list] actual.append(references) predicted.append(yhat.split()) # calculate BLEU score print('BLEU-1: %f' % corpus_bleu(actual, predicted, weights=(1.0, 0, 0, 0))) print('BLEU-2: %f' % corpus_bleu(actual, predicted, weights=(0.5, 0.5, 0, 0))) print('BLEU-3: %f' % corpus_bleu(actual, predicted, weights=(0.3, 0.3, 0.3, 0))) print('BLEU-4: %f' % corpus_bleu(actual, predicted, weights=(0.25, 0.25, 0.25, 0.25)))

We initialize two lists, one for actual description and the other for predicted description. We then use for loop to generate predicted descriptions using the generate_desc function. Similarly, the actual descriptions are saved in a variable named ‘references. Lastly, we are calculating the BLEU score based on these lists to summarize how close these predicted descriptions are to actual descriptions. Let’s implement all this on our test data:

# load test set test = load_set(filename) print('Dataset: %d' % len(test)) # descriptions test_descriptions = load_clean_descriptions('descriptions.txt', test) print('Descriptions: test=%d' % len(test_descriptions)) # photo features test_features = load_photo_features('features.pkl', test) print('Photos: test=%d' % len(test_features)) # load the model which has minimum loss, in this case it was model_18 filename = 'model_18.h5' model = load_model(filename) # evaluate model evaluate_model(model, test_descriptions, test_features, tokenizer, max_length) Conclusion

4- We used BLEU Score to evaluate our model.


Tech Coronavirus Roundup: From Extra Mobile Data To An Office Noise Generator

With three-quarters of Americans now under some kind of coronavirus lockdown, you might not think there would be much call for extra mobile data. But the reality is that some home broadband connections are now congested, so many people are relying on a mix of fixed and mobile data to meet their Internet needs.

AT&T has now stepped up to the plate …

Extra mobile data

AT&T is now offering additional mobile hotspot data.

Using technology to stay in touch with friends, family and colleagues has never been more important. That’s why starting April 2 through May 13 we’re giving AT&T mobility consumers and small businesses more ways to connect.

UK broadband data caps removed

In the UK, all the major home broadband providers have agreed to remove data caps.

The UK’s major internet service and mobile providers, namely BT/EE, Openreach, Virgin Media, Sky, TalkTalk, O2, Vodafone, Three, Hyperoptic, Gigaclear, and KCOM have all agreed the following commitments, effective immediately […]

All providers will remove all data allowance caps on all current fixed broadband services.

Government seeks to block harmful coronavirus hoaxes

Every crisis inevitably sees a host of deliberate hoaxes and misunderstandings on social media.

Facebook and Twitter have already been clamping down on fake news, and now the UK government wants to help, reports Gizmodo.

Cabinet Office staff say that as many as 10 cases of potentially dangerously wrong virus news are being found each day primarily on your dad’s Facebook page. The Rapid Response Unit plans to address the harms caused by armchair experts issuing what could end up being “dangerous misinformation.”

Among the misinformation removed by Twitter are two tweets by Brazil’s President Jair Bolsonaro, notes CNET – a move which should arguably be extended to the president of a certain other country.

Germany working on contact tracing app

Germany is set to be the latest company to launch a contact tracing app, designed to alert you if you’ve been in close proximity to someone subsequently testing positive for COVID-19, reports Reuters.

Germany hopes to launch a smartphone app within weeks to help trace coronavirus infections, after a broad political consensus emerged that adopting an approach pioneered by Singapore can be effective without invading people’s privacy […]

That would resemble Singapore’s TraceTogether app, which records the recent history of such contacts on a device. Should the smartphone’s owner test positive for COVID-19, the respiratory illness the coronavirus can cause, that data could be downloaded so that contact-tracing teams can quickly get in touch with others at risk […]

[The app] would enable the proximity and duration of contact between people to be saved for two weeks on cell phones anonymously and without the use of location data.

Our own poll found that fewer than 13% of readers would trust a government app, but just over half would trust an OS-level feature created in a partnership between Apple and Google.

Airbnb extends refund window

CNBC reports that Airbnb is now allowing customers to cancel bookings made up until May 31.

Airbnb announced it will allow guests to receive full refunds for trips starting on or before May 31 that were booked prior to March 14 as the company continues to struggle through the coronavirus’ impact on the travel industry.

So that hosts are not left without any income at all for these bookings, the company has set aside $250M to provide some degree of compensation.

Specifically, Airbnb will pay hosts 25% of what they would normally receive through their cancellation policies.

Office noise generator

Finally, if you’re finding it too quiet when working from home, TNW notes that help is at hand.

MyNoise is a noise generator that will help you re-create an office-like ambience in the comfort of your own home — and you really are spoilt for choice.

You can choose from various presets — such as air conditioning, chatty colleagues, copy machine, printer and scanner, keyboards, and writing — and adjust the toggles for each individual sound to create the desired atmosphere.

It’s a web-based app.

Previous roundups:

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God Mode Ai: The Ai

Unlock the Power of Automation with God Mode: The AI Tool that Automates Complex Tasks! Boost Efficiency, Save Time and Streamline Your Workflow Today! Try Now!

God Mode is an AI-powered tool that has the ability to self-generate tasks, take user prompts, and act on new tasks until it meets the original objective. It’s a unique tool that has been designed to automate complex tasks that would otherwise take a lot longer to complete manually. The tool is not entirely automated, as the user has approval rights for every step, allowing for redirection as well.

I began testing the tool with a task that I have already done manually, which was to create a strategy plan to research and engage with art buyers inside large retailers and merch brands. God Mode immediately conducted market research via Google, found lists, and started writing text files with notes.

After letting it run for over an hour, I added a couple of feedback notes to course correct the results. The tool successfully researched and developed a plan, created documents with engagement steps, and created a Python file to perform a specific task. However, it did not pull any contact information, which was a problem. It began to loop over and over, trying to pull contacts from LinkedIn, Google, and directories, but couldn’t pull the data properly.

I decided to test another idea, inspired by @elonmusk’s interview on Monday night, where he talked about finding the meaning of life. I set out to create “TruthGPT” to automate data research, store the data, interpret the dataset, and output findings and understandings on its own.

It worked much more effectively this time, and below are the list of the initial tasks of research.

See More: God Mode Auto GPT: How AI is Revolutionizing Automation

God Mode successfully researched the concept extensively, then created its own documentation to store the findings. It then trained itself on the data, which is quite impressive. After that, it created and ran a Python file, our own “TruthGPT”, to interpret its own dataset and give an output.

The tool successfully researched the concept extensively, then created its own documentation to store the findings. It then trained itself on the data, which is quite impressive. After that, it created and ran a Python file, our own “TruthGPT”, to interpret its own dataset and give an output.

The tool successfully created a text file with the five potential keys to reality as tasked to do, and well, simply put, we made a “TruthGPT” and found the meaning of life. (YAY! 🎉)

(Jokes aside, no, I don’t believe this is an actual “TruthGPT”). However, the process of automating complex tasks with ChatGPT is quite interesting, and soon, I don’t think there will be any gaps.

God Mode takes user prompts and acts on new tasks until it meets the original objective. The user has approval rights for every step, which allows for redirection if needed.

God Mode can automate complex tasks that would otherwise take a lot longer to complete manually.

The benefits of using God Mode include faster task completion, increased efficiency, and the ability to redirect tasks if needed.

In conclusion, God Mode is an AI-powered tool that is capable of automating complex tasks, thus increasing efficiency and reducing the time required to complete them. The tool takes user prompts and acts on new tasks until the original objective is met, with the user having approval rights for every step. While the tool is not entirely automated, it offers a unique approach to automating tasks with the ability to redirect tasks if necessary.

The tool has been tested, and it has shown its ability to conduct market research, create plans, and run Python files. Although there were some limitations in pulling data from sources, the tool was able to perform the desired tasks effectively. Additionally, the tool was tested on the creation of “TruthGPT,” which successfully researched and interpreted data to provide an output, even if it was just for fun.

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