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Images
Data Augmentation
In this tutorial, you’ll learn how to perform data augmentation on images using TensorFlow. Data Augmentation is an
important component of modern deep learning models. To improve your model performance, it is often necessary to
augment data to increase the model’s training data. Using the augmentation techniques we’ll learn in this tutorial,
you can create images that have:
Adjusted brightness, contrast, or saturation
Added color noise or grayscale noise
Upsampled the image
Implement Data Augmentation in TensorFlow
Let’s say you have a dataset of images of cats. Using this dataset, you train a classification model to predict the
class of an image of a cat.
Docker Images
In this tutorial, you’ll use a Docker image to perform data augmentation on the dataset of images. You have the
choice of using a prebuilt image or building your own image from scratch. In this tutorial, you’ll use the following
Docker image:
Standard Image
Type Docker Image
apt archive:nvidia/cudatoolkit-9.0-37.08.01-ubuntu16.04_cud9-runtime.deb
You may use any Ubuntu 16.04 standard image.
In this tutorial, you’ll replace the standard image of the container with your own image.
Build Images from Scratch
Follow these steps to build an image from scratch:
Activate the Docker service on your PC:
log in to your Docker desktop
in the left navigation bar, select Explore to open the Docker Explorer
If you’re using MacOS, press Command+Spacebar to open the menu bar. Then, select More and then Docker from the
Menu Bar
If you’re using Windows, the Docker menu bar is under Taskbar and is usually on the far right side of the screen.
Double-click the Docker Explorer icon on the Dock or in Windows taskbar and click Create Image
Click the Parameters tab.
Enter a name for the image (I called mine augmentation-tf2.0).
Select Ubuntu 16.04 and click Save.
The image name and version is captured in the Parameters fields
Activate the docker service on your PC. You’ll learn more about this in the next section.
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We present a detailed study of p53, the tumour suppressor protein which plays an important role in controlling the cell cycle and in apoptosis. One of the most extensively investigated mutant proteins is p53 R273H. The R273 residue lies in the core of the protein, and it has been proposed that this mutation causes a local conformational change in p53. Our experimental strategy combines solid-state NMR spectroscopy (SSNMR) and molecular dynamics simulations. A 13C-labelled protein sample is prepared by chemical cross-linking of a natural abundance form of the p53 mutant to a non-labelled background protein. The labelled protein is then analysed by SSNMR spectroscopy, and together with the simulations, the local protein structure can be identified. We combine SSNMR spectroscopy and molecular dynamics to study conformational dynamics of the R273H mutant. Our analysis shows that the protein chain adopts a closed conformation in the gas phase, whereas protein folding upon molecular recognition of the DNA oligomerization domain requires the presence of water. The combination of experimental and computational methods applied in this study reveals the conformational heterogeneity of p53. The particular mutant mutant studied here leads to a dynamic ensemble, where the majority of the protein remains in an open conformation in the gas phase, but it folds on DNA.Q:
Google Maps – Possible to make accurate nautical geolocation?
I’m creating a webapp that will need to know the exact latitude and longitude of locations.
Are there any options on google maps that give me this accuracy?
For example:
The latitude and longitude for the location are given as Sydney, NSW – and I want to give them as accurate as possible.
A:
There is no “correct” accuracy, I’ve been using Google
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