Metadata-Version: 2.1
Name: keras-metrics
Version: 1.1.0
Summary: Metrics for Keras model evaluation
Home-page: https://github.com/netrack/keras-metrics
Author: Yasha Bubnov
Author-email: girokompass@gmail.com
License: UNKNOWN
Description: # Keras Metrics
        
        [![Build Status][BuildStatus]](https://travis-ci.org/netrack/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:
        ```sh
        pip install keras-metrics
        ```
        
        ## Usage
        
        The usage of the package is simple:
        ```py
        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:
        ```py
        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:
        ```py
        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:
        ```py
        import numpy as np
        import keras_metrics as km
        import tensorflow as tf
        import tensorflow.keras as keras
        
        model = keras.Sequential()
        model.add(keras.layers.Dense(1, activation="softmax"))
        model.compile(optimizer="sgd",
                      loss="binary_crossentropy",
                      metrics=[km.binary_true_positive()])
        
        x = np.array([[0], [1], [0], [1]])
        y = np.array([1, 0, 1, 0]
        
        # Wrap model.fit into the session with global
        # variables initialization.
        with tf.Session() as s:
            s.run(tf.global_variables_initializer())
            model.fit(x=x, y=y)
        ```
        
        [BuildStatus]: https://travis-ci.org/netrack/keras-metrics.svg?branch=master
        
Keywords: keras metrics evaluation
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
