Category: Misc

(posted here for more advice)

I am making a project that utilizes MLKit. The classification
model will be a TensorFlow Lite model. I noticed that the detected
objects always return rectangular bounding boxes. I would like them
to return polygonal bounds that are shaped like the object it is
detecting, or if possible, a sort of “3D” bound.

I am aware of certain annotation tools, along with things like
Mask RCNN, but I am not sure how to integrate them into a
TensorFlow Lite model, & I do not know what specific files to
edit. (or if I am supposed to implement it in the model rather than
the base code) or if I can even do it at all.

I want the detected objects to return bounding polygons, or even
3D polygons/image segmentations, instead of bounding boxes, using
MLKit + TensorFlow Lite. How do I achieve this?

The code runs like this:

​

import tensorflow as tf import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D mnist_data = tf.keras.datasets.mnist (train_images, train_labels), (test_images, test_labels) = mnist_data.load_data() def scale_mnist_data(train_images, test_images): return (train_images / 255, test_images / 255) def train_model(model, scaled_train_images, train_labels): scaled_train_images, scaled_test_images = scale_mnist_data(train_images, test_images) 

THE CODE RUNS PERFECTLY WELL AT THIS POINT,BUT HERE….

scaled_train_images = scaled_train_images[..., np.newaxis] scaled_test_images = scaled_test_images[..., np.newaxis] 

I GET THE ERROR- NameError: name ‘scaled_train_images’ is not
defined

NameError Traceback (most recent call last) <ipython-input-5-7e4c845d2449> in <module> 1 # Add a dummy channel dimension 2 ----> 3 scaled_train_images = scaled_train_images[..., np.newaxis] 4 scaled_test_images = scaled_test_images[..., np.newaxis] 

I wonder if inserting this code ” def train_model(model,
scaled_train_images, train_labels):” is fine. But here again, I
bumped into similar issues like history, frame and some other
variables being not able to be defined.

Does anyone have any insight about the contents of this
stackoverflow post:
https://stackoverflow.com/questions/65402617/tensorflow-automl-model-in-react

Getting a little desperate.

Say you’re classifying the flowers dataset. Some images aren’t
as good as others. Would duplicating the images that are good
examples of a certain type help propagate the desired features in
the network?

E.g. if I duplicate a close-up of a certain type flower head
within the dataset (say a rose within /roses), would it make the
network more bias towards the duplicates?

I have a handful of ideal examples, and thousands of very
variable examples. I’m unsure what’s the best strategy to be more
biased towards the good examples in my data..

I’m building an image classifier. I happen to have a small
dataset of ideal data. Can I train a model using this idealised
data, and somehow use it as a base for further training?

I’ve read through the docs; they all use ImageNet or
tensorflow-hub datasets. I can’t seem to find an example of using
your own data.

I am currently studying Industrial Engineering, but I’ve been
learning DL and ML on my own. My goal is to obtain a job related
with ML or AI. I don’t come from a traditional Computer Science
background, do you think this certificate will add value to my
CV?

Hey, this is a question I had that I answered myself after some
research. I can’t find a flair more applicable than ‘Question’ so I
will just answer it myself haha.

I was trying to use tf.keras.preprocessing.text.Tokenizer to
train a model for a language task. I wanted my model to include
certain punctuation in it’s output, like exclamation points and
commas and whatnot, and I wasn’t sure how to do this.

I figured that since the default value for filters in the
Tokenizer constructor is:

filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~tn' 

then I would just have to remove the punctuation that I want to
be recognized. I then spent a few hours training my model.

DON’T DO THIS. It will not treat the
punctuation as separate tokens, but rather your vocabulary will be
filled with examples such as ‘man’ vs ‘man.’ vs ‘man,’, etc. These
will all be separate tokens.

Instead, you should preprocess all of your sentences to include
spaces between any punctuation that you want. This is how I did
it:

def separate_punctuation(s, filters=',.()?'): new_s = '' for char in s: if char in filters: new_s += ' ' + char + ' ' else: new_s += char return new_s.strip() 

This way ‘Hello neighbor, how are you?’ will become ‘Hello
neighbor , how are you ?’. Thus, all punctuation will only take up
one element of your vocabulary and your model will generalize much,
much better.

Hope this saves someone else’s time.