From 89305e43589cc6f487b282ec7e37dd07d5c1bcff Mon Sep 17 00:00:00 2001 From: Whovillage Date: Wed, 1 Feb 2023 10:39:43 +0200 Subject: [PATCH] Update README.md --- subjects/ai/keras/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/subjects/ai/keras/README.md b/subjects/ai/keras/README.md index 67e5acc0..cde386c2 100644 --- a/subjects/ai/keras/README.md +++ b/subjects/ai/keras/README.md @@ -112,7 +112,7 @@ The goal of this exercise is to learn to train the neural network. Once the arch - `compile`: The compilation step aims to set the loss function, to choose the algoithm to minimize the chosen loss function and to choose the metric the model outputs. - The **optimizer**. We’ll stick with a pretty good default: the Adam gradient-based optimizer. Keras has many other optimizers you can look into as well. - - The **loss function**. Depending on the problem to solve: classification or regression Keras proposes different loss functions. In classification Keras distinguishes between `binary_crossentropy` (2 classes) and `categorical_crossentropy` (>2 classes), so we’ll use the latter. + - The **loss function**. Depending on the problem to solve: classification or regression Keras proposes different loss functions. In classification Keras distinguishes between `binary_crossentropy` (2 classes) and `categorical_crossentropy` (>2 classes), so we’ll use the former. - The **metric(s)**. A list of metrics. Depending on the problem to solve: classification or regression Keras proposes different loss functions. For example for classification the metric can be the accuracy. - `fit`: Training a model in Keras literally consists only of calling fit() and specifying some parameters. There are a lot of possible parameters, but we’ll only manually supply a few: