tf.keras.applications.resnet50.preprocess_input  Preprocesses a tensor or Numpy array encoding a batch of images.  View aliases  Main aliases 
tf.keras.applications.resnet.preprocess_input Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.applications.resnet.preprocess_input, tf.compat.v1.keras.applications.resnet50.preprocess_input  
tf.keras.applications.resnet50.preprocess_input(
    x, data_format=None
)
 Usage example with applications.MobileNet: i = tf.keras.layers.Input([None, None, 3], dtype = tf.uint8)
x = tf.cast(i, tf.float32)
x = tf.keras.applications.mobilenet.preprocess_input(x)
core = tf.keras.applications.MobileNet()
x = core(x)
model = tf.keras.Model(inputs=[i], outputs=[x])

image = tf.image.decode_png(tf.io.read_file('file.png'))
result = model(image)

 


 Args
  x   A floating point numpy.array or a tf.Tensor, 3D or 4D with 3 color channels, with values in the range [0, 255]. The preprocessed data are written over the input data if the data types are compatible. To avoid this behaviour, numpy.copy(x) can be used.  
  data_format   Optional data format of the image tensor/array. Defaults to None, in which case the global setting tf.keras.backend.image_data_format() is used (unless you changed it, it defaults to "channels_last").   
 


 Returns   Preprocessed numpy.array or a tf.Tensor with type float32. The images are converted from RGB to BGR, then each color channel is zero-centered with respect to the ImageNet dataset, without scaling. 
 

 


 Raises
  ValueError   In case of unknown data_format argument.     

    
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