Month: December 2020

I am running a tensorflow application that sets inter_op,
intra_op and OMP_NUM_THREADS, however, it completely ignores these
settings and seems to run with the defaults. Here’s how I’m setting
them:

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 import tensorflow as tf print('Using Thread Parallelism: {} NUM_INTRA_THREADS, {} NUM_INTER_THREADS, {} OMP_NUM_THREADS'.format(os.environ['NUM_INTRA_THREADS'], os.environ['NUM_INTER_THREADS'], os.environ['OMP_NUM_THREADS'])) session_conf = tf.compat.v1.ConfigProto(inter_op_parallelism_threads=int(os.environ['NUM_INTER_THREADS']), intra_op_parallelism_threads=int(os.environ['NUM_INTRA_THREADS'])) sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf) tf.compat.v1.keras.backend.set_session(sess) I have validated that it's reading the right values (the print prints the values as expected). I have also tried with other Tensorflow 2 versions with no success. I am at a loss as to what I'm doing wrong. Version Info: tensorflow 2.2.0 py37_2 intel tensorflow-base 2.2.0 0 intel tensorflow-estimator 2.2.0 pyh208ff02_0 keras 2.4.3 0 keras-base 2.4.3 py_0 keras-preprocessing 1.1.0 py_1 

The code runs like this:

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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.

(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?

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.

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.

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..