Keras Metrics

This package provides metrics for evaluation of Keras classification models. The metrics are safe to use for batch-based model evaluation.

Installation

To install the package from the PyPi repository you can execute the following command:

pip install keras-metrics

Usage

The usage of the package is simple:

import keras import keras_metrics as km model = models . Sequential () model . add ( keras . layers . Dense ( 1 , activation = "sigmoid" , input_dim = 2 )) model . add ( keras . layers . Dense ( 1 , activation = "softmax" )) model . compile ( optimizer = "sgd" , loss = "binary_crossentropy" , metrics = [ km . binary_precision (), km . binary_recall ()])

Similar configuration for multi-label binary crossentropy:

import keras import keras_metrics as km model = models . Sequential () model . add ( keras . layers . Dense ( 1 , activation = "sigmoid" , input_dim = 2 )) model . add ( keras . layers . Dense ( 2 , activation = "softmax" )) # Calculate precision for the second label. precision = km . binary_precision ( label = 1 ) # Calculate recall for the first label. recall = km . binary_recall ( label = 0 ) model . compile ( optimizer = "sgd" , loss = "binary_crossentropy" , metrics = [ precision , recall ])

Keras metrics package also supports metrics for categorical crossentropy and sparse categorical crossentropy:

import keras_metrics as km c_precision = km . categorical_precision () sc_precision = km . sparse_categorical_precision () # ...

Tensorflow Keras

Tensorflow library provides the keras package as parts of its API, in order to use keras_metrics with Tensorflow Keras, you are advised to perform model training with initialized global variables: