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Returns
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String, the current default float type.
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Example
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>>> tf.keras.backend.floatx()
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'float32'
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set_floatx function
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tf.keras.backend.set_floatx(value)
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Sets the default float type.
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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.experimental.set_policy('mixed_float16'). See the mixed precision guide for details.
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Arguments
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value: String; 'float16', 'float32', or 'float64'.
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Example
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>>> tf.keras.backend.floatx()
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'float32'
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>>> tf.keras.backend.set_floatx('float64')
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>>> tf.keras.backend.floatx()
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'float64'
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>>> tf.keras.backend.set_floatx('float32')
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Raises
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ValueError: In case of invalid value.
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image_data_format function
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tf.keras.backend.image_data_format()
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Returns 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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>>> tf.keras.backend.image_data_format()
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'channels_last'
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set_image_data_format function
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tf.keras.backend.set_image_data_format(data_format)
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Sets the value of the image data format convention.
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Arguments
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data_format: string. 'channels_first' or 'channels_last'.
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Example
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>>> tf.keras.backend.image_data_format()
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'channels_last'
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>>> tf.keras.backend.set_image_data_format('channels_first')
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>>> tf.keras.backend.image_data_format()
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'channels_first'
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>>> tf.keras.backend.set_image_data_format('channels_last')
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Raises
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ValueError: In case of invalid data_format value.
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epsilon function
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tf.keras.backend.epsilon()
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Returns 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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>>> tf.keras.backend.epsilon()
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1e-07
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set_epsilon function
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tf.keras.backend.set_epsilon(value)
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Sets the value of the fuzz factor used in numeric expressions.
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Arguments
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value: float. New value of epsilon.
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Example
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>>> tf.keras.backend.epsilon()
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1e-07
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>>> tf.keras.backend.set_epsilon(1e-5)
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>>> tf.keras.backend.epsilon()
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1e-05
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>>> tf.keras.backend.set_epsilon(1e-7)
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is_keras_tensor function
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tf.keras.backend.is_keras_tensor(x)
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