244 lines
6.5 KiB
Python
244 lines
6.5 KiB
Python
import json
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import os
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from keras_core.api_export import keras_core_export
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# The type of float to use throughout a session.
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_FLOATX = "float32"
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# Epsilon fuzz factor used throughout the codebase.
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_EPSILON = 1e-7
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# Default image data format, one of "channels_last", "channels_first".
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_IMAGE_DATA_FORMAT = "channels_last"
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# Default backend: TensorFlow.
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_BACKEND = "tensorflow"
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@keras_core_export(["keras_core.config.floatx", "keras_core.backend.floatx"])
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def floatx():
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"""Return the default float type, as a string.
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E.g. `'float16'`, `'float32'`, `'float64'`.
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Returns:
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String, the current default float type.
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Example:
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>>> keras_core.config.floatx()
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'float32'
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"""
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return _FLOATX
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@keras_core_export(
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["keras_core.config.set_floatx", "keras_core.backend.set_floatx"]
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)
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def set_floatx(value):
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"""Set the default float dtype.
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Note: It is not recommended to set this to `"float16"` for training,
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as this will likely cause numeric stability issues.
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Instead, mixed precision, which leverages
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a mix of `float16` and `float32`. It can be configured by calling
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`keras_core.mixed_precision.set_global_policy('mixed_float16')`.
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Args:
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value: String; `'float16'`, `'float32'`, or `'float64'`.
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Example:
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>>> keras_core.config.floatx()
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'float32'
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>>> keras_core.config.set_floatx('float64')
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>>> keras_core.config.floatx()
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'float64'
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>>> keras_core.config.set_floatx('float32')
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Raises:
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ValueError: In case of invalid value.
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"""
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global _FLOATX
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accepted_dtypes = {"float16", "float32", "float64"}
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if value not in accepted_dtypes:
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raise ValueError(
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f"Unknown `floatx` value: {value}. "
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f"Expected one of {accepted_dtypes}"
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)
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_FLOATX = str(value)
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@keras_core_export(["keras_core.config.epsilon", "keras_core.backend.epsilon"])
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def epsilon():
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"""Return the value of the fuzz factor used in numeric expressions.
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Returns:
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A float.
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Example:
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>>> keras_core.config.epsilon()
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1e-07
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"""
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return _EPSILON
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@keras_core_export(
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["keras_core.config.set_epsilon", "keras_core.backend.set_epsilon"]
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)
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def set_epsilon(value):
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"""Set the value of the fuzz factor used in numeric expressions.
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Args:
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value: float. New value of epsilon.
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Example:
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>>> keras_core.config.epsilon()
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1e-07
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>>> keras_core.config.set_epsilon(1e-5)
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>>> keras_core.config.epsilon()
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1e-05
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>>> keras_core.config.set_epsilon(1e-7)
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"""
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global _EPSILON
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_EPSILON = value
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@keras_core_export(
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[
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"keras_core.config.image_data_format",
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"keras_core.backend.image_data_format",
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]
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)
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def image_data_format():
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"""Return the default image data format convention.
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Returns:
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A string, either `'channels_first'` or `'channels_last'`.
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Example:
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>>> keras_core.config.image_data_format()
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'channels_last'
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"""
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return _IMAGE_DATA_FORMAT
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@keras_core_export(
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[
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"keras_core.config.set_image_data_format",
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"keras_core.backend.set_image_data_format",
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]
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)
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def set_image_data_format(data_format):
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"""Set the value of the image data format convention.
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Args:
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data_format: string. `'channels_first'` or `'channels_last'`.
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Example:
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>>> keras_core.config.image_data_format()
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'channels_last'
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>>> keras_core.config.set_image_data_format('channels_first')
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>>> keras_core.config.image_data_format()
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'channels_first'
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>>> keras_core.config.set_image_data_format('channels_last')
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"""
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global _IMAGE_DATA_FORMAT
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data_format = str(data_format).lower()
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if data_format not in {"channels_first", "channels_last"}:
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raise ValueError(
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"The `data_format` argument must be one of "
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"{'channels_first', 'channels_last'}. "
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f"Received: data_format={data_format}"
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)
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_IMAGE_DATA_FORMAT = data_format
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def standardize_data_format(data_format):
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if data_format is None:
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return image_data_format()
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data_format = str(data_format).lower()
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if data_format not in {"channels_first", "channels_last"}:
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raise ValueError(
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"The `data_format` argument must be one of "
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"{'channels_first', 'channels_last'}. "
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f"Received: data_format={data_format}"
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)
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return data_format
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# Set Keras base dir path given KERAS_HOME env variable, if applicable.
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# Otherwise either ~/.keras or /tmp.
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if "KERAS_HOME" in os.environ:
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_keras_dir = os.environ.get("KERAS_HOME")
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else:
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_keras_base_dir = os.path.expanduser("~")
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if not os.access(_keras_base_dir, os.W_OK):
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_keras_base_dir = "/tmp"
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_keras_dir = os.path.join(_keras_base_dir, ".keras")
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# Attempt to read Keras config file.
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_config_path = os.path.expanduser(os.path.join(_keras_dir, "keras.json"))
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if os.path.exists(_config_path):
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try:
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with open(_config_path) as f:
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_config = json.load(f)
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except ValueError:
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_config = {}
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_floatx = _config.get("floatx", floatx())
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assert _floatx in {"float16", "float32", "float64"}
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_epsilon = _config.get("epsilon", epsilon())
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assert isinstance(_epsilon, float)
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_backend = _config.get("backend", _BACKEND)
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_image_data_format = _config.get("image_data_format", image_data_format())
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assert _image_data_format in {"channels_last", "channels_first"}
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set_floatx(_floatx)
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set_epsilon(_epsilon)
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set_image_data_format(_image_data_format)
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_BACKEND = _backend
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# Save config file, if possible.
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if not os.path.exists(_keras_dir):
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try:
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os.makedirs(_keras_dir)
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except OSError:
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# Except permission denied and potential race conditions
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# in multi-threaded environments.
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pass
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if not os.path.exists(_config_path):
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_config = {
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"floatx": floatx(),
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"epsilon": epsilon(),
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"backend": _BACKEND,
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"image_data_format": image_data_format(),
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}
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try:
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with open(_config_path, "w") as f:
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f.write(json.dumps(_config, indent=4))
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except IOError:
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# Except permission denied.
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pass
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# Set backend based on KERAS_BACKEND flag, if applicable.
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if "KERAS_BACKEND" in os.environ:
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_backend = os.environ["KERAS_BACKEND"]
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if _backend:
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_BACKEND = _backend
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@keras_core_export("keras_core.backend.backend")
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def backend():
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"""Publicly accessible method for determining the current backend.
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Returns:
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String, the name of the backend Keras is currently using.
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Example:
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>>> keras.backend.backend()
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'tensorflow'
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"""
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return _BACKEND
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