TensorFlow Snippets installs statistics

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.

Features

The extension includes three sets of snippets.

The first are used to generate entire python files: • tf:trainer: Generates an entire training framework based on learn API. • tf:cnn-classifier: Generates a convolutional neural network classifier. • tf:mnist: Generates data loader for mnist dataset. • tf:cifar10: Generates data loader for CIFAR10 dataset. • tf:cifar100: Generates data loader for CIFAR100 dataset.

The second set generate medium size snippets: • tf:resnet-block: Generates a ResNet block.

The third set generate common TensorFlow operations: • tf:import: Imports TensorFlow package. • tf:conv2d: Defines a 2D convolution layer. • tf:transposed-conv2d: Defines a transposed 2D convolution layer. • tf:separable-conv2d: Defines a separable 2D convolution layer. • tf:dense: Defines a dense (fully connected) layer. • tf:max-pool2d: Defines a max pooling layer. • tf:dropout: Defines a dropout layer. • tf:batch-norm: Defines a batch normalization layer. • tf:cross-entropy: Defines a softmax cross entropy loss.

Installation

Install Visual Studio Code from here.

Install this extension by selecting Install Extension in the command pallette (cmd-shift-p) and searching for "TensorFlow Snippets".

The generated code also relies on the following python dependencies:

Getting started

Make sure the requirements are installed.

Create a new directory for your project. Create trainer.py, open the file and type: tf:trainer enter. Create cnn.py, open the file and type: tf:cnn-classifier enter. Create mnist.py, open the file and type: tf:mnist enter.

Edit trainer.py, and insert two lines at the top:

In the MODELS dictionary, add a new element "cnn": cnn, also in the DATASETS dictionary add a new element "mnist": mnist. Now run the trainer:

Open another terminal window in the same directory, and type:

Point your browser at http://localhost:6006.

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