%global _empty_manifest_terminate_build 0
Name:		python-keras-metrics
Version:	1.1.0
Release:	1
Summary:	Metrics for Keras model evaluation
License:	MIT License
URL:		https://github.com/netrack/keras-metrics
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3c/39/46e985d0718d692384c5feb006bb2dcb5846ce60b1ec94db323747b53c90/keras-metrics-1.1.0.tar.gz
BuildArch:	noarch

Requires:	python3-Keras

%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




%package -n python3-keras-metrics
Summary:	Metrics for Keras model evaluation
Provides:	python-keras-metrics
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-keras-metrics
# 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




%package help
Summary:	Development documents and examples for keras-metrics
Provides:	python3-keras-metrics-doc
%description help
# 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




%prep
%autosetup -n keras-metrics-1.1.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-keras-metrics -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.0-1
- Package Spec generated