preprocess_input

These assume you have already converted images into a three channel,
224 by 224 matrix with load_img and img_to_array. The
processing differs based on the model so set the appropriate model
that you are using.

Provides a consistent interface to the 'Keras' Deep Learning Library
directly from within R. 'Keras' provides specifications for describing dense
neural networks, convolution neural networks (CNN) and recurrent neural networks
(RNN) running on top of either 'TensorFlow' or 'Theano'. Type conversions between
Python and R are automatically handled correctly, even when the default
choices would otherwise lead to errors. Includes complete R documentation
and many working examples.

Taylor Arnold

kerasR

R Interface to the Keras Deep Learning Library

Andrie de Vries

preprocess_input function

<dl><dt>img</dt>
<dd>the input image, as an array</dd>
<dt>model</dt>
<dd>the model you wish to preprocess to</dd></dl>

Arguments

Taylor B. Arnold, <a href="/link/taylor.arnold%40acm.org?package=kerasR&version=0.8.1" data-mini-rdoc="kerasR::taylor.arnold@acm.org">taylor.arnold@acm.org</a>

Author

Preprocess input for pre-defined imagenet networks — preprocess_input

<dl>

<dt>img</dt>
<dd>the input image, as an array</dd>


<dt>model</dt>
<dd>the model you wish to preprocess to</dd>

</dl>

Taylor B. Arnold, <a href='mailto:taylor.arnold@acm.org'>taylor.arnold@acm.org</a>

preprocess_input: Preprocess input for pre-defined imagenet networks

Description

Usage

Arguments

Author

References