Metrics for Keras model evaluation
Project description
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
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()])
Similar configuration for multi-label binary crossentropy:
import keras
import keras_metrics
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 = keras_metrics.precision(label=1)
# Calculate recall for the first label.
recall = keras_metrics.recall(label=0)
model.compile(optimizer="sgd",
loss="binary_crossentropy",
metrics=[precision, recall])
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