How to calculate precision and recall in Keras

Question

I am building a multi-class classifier with Keras 2.02 (with Tensorflow backend)，and I do not know how to calculate precision and recall in Keras. Please help me.

Answer 1

Python package keras-metrics could be useful for this (I'm the package's author).

import keras import keras_metrics  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=[keras_metrics.precision(), keras_metrics.recall()]) 

UPDATE: Starting with Keras version 2.3.0, such metrics as precision, recall, etc. are provided within library distribution package.

The usage is the following:

model.compile(optimizer="sgd",               loss="binary_crossentropy",               metrics=[keras.metrics.Precision(), keras.metrics.Recall()]) 

Answer 2

As of Keras 2.0, precision and recall were removed from the master branch. You will have to implement them yourself. Follow this guide to create custom metrics : Here.

Precision and recall equation can be found Here

Or reuse the code from keras before it was removed Here.

There metrics were remove because they were batch-wise so the value may or may not be correct.