tf.keras.layers.experimental.preprocessing.RandomTranslation Randomly translate each image during training. Inherits From: PreprocessingLayer, Layer, Module  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.layers.experimental.preprocessing.RandomTranslation  
tf.keras.layers.experimental.preprocessing.RandomTranslation(
    height_factor, width_factor, fill_mode='reflect',
    interpolation='bilinear', seed=None, name=None, fill_value=0.0,
    **kwargs
)

 


 Arguments
  height_factor   a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for shifting vertically. A negative value means shifting image up, while a positive value means shifting image down. When represented as a single positive float, this value is used for both the upper and lower bound. For instance, height_factor=(-0.2, 0.3) results in an output shifted by a random amount in the range [-20%, +30%]. height_factor=0.2 results in an output height shifted by a random amount in the range [-20%, +20%].  
  width_factor   a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for shifting horizontally. A negative value means shifting image left, while a positive value means shifting image right. When represented as a single positive float, this value is used for both the upper and lower bound. For instance, width_factor=(-0.2, 0.3) results in an output shifted left by 20%, and shifted right by 30%. width_factor=0.2 results in an output height shifted left or right by 20%.  
  fill_mode   Points outside the boundaries of the input are filled according to the given mode (one of {'constant', 'reflect', 'wrap', 'nearest'}).  
reflect: (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel. 
constant: (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value k = 0. 
wrap: (a b c d | a b c d | a b c d) The input is extended by wrapping around to the opposite edge. 
nearest: (a a a a | a b c d | d d d d) The input is extended by the nearest pixel. 

 
  interpolation   Interpolation mode. Supported values: "nearest", "bilinear".  
  seed   Integer. Used to create a random seed.  
  name   A string, the name of the layer.  
  fill_value   a float represents the value to be filled outside the boundaries when fill_mode is "constant".    Input shape: 4D tensor with shape: (samples, height, width, channels), data_format='channels_last'. Output shape: 4D tensor with shape: (samples, height, width, channels), data_format='channels_last'.
 


 Raise
  ValueError   if either bound is not between [0, 1], or upper bound is less than lower bound.    Methods adapt View source 
adapt(
    data, reset_state=True
)
 Fits the state of the preprocessing layer to the data being passed.
 


 Arguments
  data   The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.  
  reset_state   Optional argument specifying whether to clear the state of the layer at the start of the call to adapt, or whether to start from the existing state. This argument may not be relevant to all preprocessing layers: a subclass of PreprocessingLayer may choose to throw if 'reset_state' is set to False.