augment | The world 's smallest and fastest classical JavaScript | Interpreter library

I am currently trying to create a program that finds the maximum flow through a network under the constraint that edges with a common source node must have the same flow. It is that constraint that I am having trouble with.

Currently, I have the code to get all flow augmenting routes, but I am hesitant to code the augmentation because I can't figure out how to add in the constraint. I am thinking about maybe a backtracking algorithm that tries to assign flow using the Ford-Fulkerson method and then tries to adjust to fit the constraint.

So my code:
There is a graph class that has as attributes all the vertices and edges as well as methods to get the incident edges to a vertex and the preceding edges to a vertex:

Following AWS Personalize documents, I successfully imported my datasets (User, Item, Interaction) from S3, created an EventTrcker, trained the model, and deployed the campaign. The solution works without any issue and I get the recommendations.

I rely on Putevent to add new user-item interaction events. I also dump those interaction events using Lambda+firehose in my s3. But I am wondering if AWS Personalize internally creates/augments the original user-item interaction dataset? How I can access and download the revised version of the dataset? I cannot see any new dataset in "Dataset groups > Datasets" rather than my original 3 datasets...

I prefer to dump it regularly from AWS Personalize to my S3 storage rather than using my own Lambda+Firehose solution.

This is the output of my Putevent call. I see 200...but not sure it works fine or not...should I see any new dataset in "Dataset groups > Datasets" created by putevents?

I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.

My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall)) but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code? (Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)

This is a part of my code, before data augmentation, model.fit was working, however after augmentation of data i'm getting this error;

AttributeError: module 'scipy.ndimage' has no attribute 'interpolation'

This is the list of all imported libraries;

I have noticed there is a preprocess_input function that is different according to the model you wanna use in tensorflow.keras.applications.

I am using ImageDataGenerator class to augment my data. More specificaly, I am using a CustomDataGenerator, that extend from the ImageDataGenerator class and adds a color transformation.

This is how it looks like:

I have a set of images of numbers that go from 0 to 20 with intermediate classes(0,25 / 0,5 / 0,75). Each number will be defined as a class of its own. I have 22 images per class.

These images will be used for training and testing on a convolutional neural network for classification. I'm not worried about accuracy, it's only a proof of concept so I realise that the dataset is too small for any real reliable outcome. Like I said, it's only meant as a proof of concept.

EDIT: As suggested by @Kaveh, I checked out ImageDataGenerator.flow_from_directory

As far as I could tell, this is used to increase your dataset size using data augmentation. However, what I'm asking is, now that I have these images set in different folders (22 images per folder, each folder making a class) how do I use them. I've always been loading one file that makes up the dataset (example: mnist; through keras). I've never used my own data and therefore, have no idea what the next step is.

İ am working on an image dataset that is categorical 12 classes. İ am using transfer learning with VGG16. However, İ have faced an error: Shapes (None, None) and (None, 28, 28, 12) are incompatible. My code:

I know that in each epoch we have a new set of augmentation. But my question is that if we have a total of 10 sample image, batch_size = 5, and we took steps_per_epoch = 3 instead of 2, then we will pass 5*3 = 15 images in each epoch,

so definitely we will have repetition, my question is that if image x is repeated, will both have the same augmentation value or different.
It depends on whether new augmentation happens in each batch or in each epoch.

Thanks,

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