If you go this route, you will need to install the following packages: pandas, jupyter, seaborn, scikit-learn, keras, and tensorflow. Set up a data science environment. Visual Studio Code and the Python extension provide a great editor for data science scenarios. 00:15 Installation von TensorFlow03:15 Installation von Keras03:25 Installation von Visual Studio CodeHast Du Bock auf Machine Learning und Artificial Intell.
Visual Studio Code TensorFlow Snippets This extension includes a set of useful code snippets for developing TensorFlow models in Visual Studio Code. See Getting started for a quick tutorial on how to use this extension. Build an AI-powered Pet Detector with Python, TensorFlow, and Visual Studio Code Let’s build a pet detector to recognize them in pictures! We will walk through the training, optimizing, and deploying of a deep learning model using Azure Notebooks and the Azure Machine Learning service.
In this quickstart, we will train a TensorFlow model with the MNIST dataset locally in Visual Studio Tools for AI.
The MNIST database has a training set of 60,000 examples, and a test set of 10,000 examples of handwritten digits.
Prerequisites
Before you begin, ensure you have the following installed:
Google TensorFlow
Run the following command in a terminal:
Tensorflow In Visual Studio
NumPy and SciPy
Install NumPy and SciPy.
Visual Studio Code Install Tensorflow
Download sample code
Download this GitHub repository containing samples for getting started with deep learning across TensorFlow, CNTK, Theano, and more.
Open solution and train model
Visual Studio Tools Download
Launch Visual Studio and select File > Open > Project/Solution.
Select the Tensorflow Examples folder from the samples repository downloaded and open the TensorflowExamples.sln file.
Find the MNIST project in Solution Explorer, right-click and select Set as StartUp Project.
Click Start.
The output is printed in the console.