For example, if you are using TensorFlow, you might save your model as a .h5 file using the Keras API. If you are using PyTorch, you might save your model as a .pt file using the torch.save() function. By saving your model as a file, you can easily load it into a deployment environment (such as FastAPI) and use it to make predictions on new images

The models are developed in Python [46], using the Keras [47] and Tensorflow [48] libraries. Detailson the code and dependencies to run the experiments are listed in a Readme file available togetherwith the code in the Supplemental Material.

Keras is a user centric library whereas Tensorflow especially Tensorflow 1.0 is a machine centric library. ML researchers think in terms of terms of layers, automatic differentiation engines think in terms of computational graphs.As far as I’m concerned my time is more valuable than the cycles of a machine so I’d rather use something like Keras.

A matrix is a linear map but linear maps are far more intuitive to think about than matrices

Here’s my preferred way of doing it, which uses Keras’s Tokenizer class

vectors here have length 7 instead of 6 because of the extra 0 element at the beginning. This is an inconsequential detail - Keras reserves index 0 and never assigns it to any word.

from vis.modifiers import Jitter

from vis.utils.vggnet import VGG16

@mingrutar - Correction: config = tf.ConfigProto() config.gpu_options.allow_growth = True session = tf.Session(config=config)