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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras backend config API."""
import tensorflow.compat.v2 as tf
from tensorflow.python.util.tf_export import keras_export
# The type of float to use throughout a session.
_FLOATX = 'float32'
# Epsilon fuzz factor used throughout the codebase.
_EPSILON = 1e-7
# Default image data format, one of "channels_last", "channels_first".
_IMAGE_DATA_FORMAT = 'channels_last'
@keras_export('keras.backend.epsilon')
@tf.__internal__.dispatch.add_dispatch_support
def epsilon():
"""Returns the value of the fuzz factor used in numeric expressions.
Returns:
A float.
Example:
>>> tf.keras.backend.epsilon()
1e-07
"""
return _EPSILON
@keras_export('keras.backend.set_epsilon')
def set_epsilon(value):
"""Sets the value of the fuzz factor used in numeric expressions.
Args:
value: float. New value of epsilon.
Example:
>>> tf.keras.backend.epsilon()
1e-07
>>> tf.keras.backend.set_epsilon(1e-5)
>>> tf.keras.backend.epsilon()
1e-05
>>> tf.keras.backend.set_epsilon(1e-7)
"""
global _EPSILON
_EPSILON = value
@keras_export('keras.backend.floatx')
def floatx():
"""Returns the default float type, as a string.
E.g. `'float16'`, `'float32'`, `'float64'`.
Returns:
String, the current default float type.
Example:
>>> tf.keras.backend.floatx()
'float32'
"""
return _FLOATX
@keras_export('keras.backend.set_floatx')
def set_floatx(value):
"""Sets the default float type.
Note: It is not recommended to set this to float16 for training, as this will
likely cause numeric stability issues. Instead, mixed precision, which is
using a mix of float16 and float32, can be used by calling
`tf.keras.mixed_precision.set_global_policy('mixed_float16')`. See the
[mixed precision guide](
https://www.tensorflow.org/guide/keras/mixed_precision) for details.
Args:
value: String; `'float16'`, `'float32'`, or `'float64'`.
Example:
>>> tf.keras.backend.floatx()
'float32'
>>> tf.keras.backend.set_floatx('float64')
>>> tf.keras.backend.floatx()
'float64'
>>> tf.keras.backend.set_floatx('float32')
Raises:
ValueError: In case of invalid value.
"""
global _FLOATX
accepted_dtypes = {'float16', 'float32', 'float64'}
if value not in accepted_dtypes:
raise ValueError(
f'Unknown `floatx` value: {value}. Expected one of {accepted_dtypes}')
_FLOATX = str(value)
@keras_export('keras.backend.image_data_format')
@tf.__internal__.dispatch.add_dispatch_support
def image_data_format():
"""Returns the default image data format convention.
Returns:
A string, either `'channels_first'` or `'channels_last'`
Example:
>>> tf.keras.backend.image_data_format()
'channels_last'
"""
return _IMAGE_DATA_FORMAT
@keras_export('keras.backend.set_image_data_format')
def set_image_data_format(data_format):
"""Sets the value of the image data format convention.
Args:
data_format: string. `'channels_first'` or `'channels_last'`.
Example:
>>> tf.keras.backend.image_data_format()
'channels_last'
>>> tf.keras.backend.set_image_data_format('channels_first')
>>> tf.keras.backend.image_data_format()
'channels_first'
>>> tf.keras.backend.set_image_data_format('channels_last')
Raises:
ValueError: In case of invalid `data_format` value.
"""
global _IMAGE_DATA_FORMAT
accepted_formats = {'channels_last', 'channels_first'}
if data_format not in accepted_formats:
raise ValueError(
f'Unknown `data_format`: {data_format}. '
f'Expected one of {accepted_formats}')
_IMAGE_DATA_FORMAT = str(data_format)