Preprocesses a tensor or Numpy array encoding a batch of images.
tf.keras.applications.densenet.preprocess_input(
x, data_format=None
)
Usage example with applications.MobileNet:
i = keras.layers.Input([None, None, 3], dtype="uint8")
x = ops.cast(i, "float32")
x = keras.applications.mobilenet.preprocess_input(x)
core = keras.applications.MobileNet()
x = core(x)
model = keras.Model(inputs=[i], outputs=[x])
result = model(image)
Args |
|---|
x
|
A floating point numpy.array or a backend-native 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. None, means
the global setting keras.backend.image_data_format() is used
(unless you changed it, it uses "channels_last").
Defaults to None.
|
Returns |
|---|
Preprocessed array with type float32.
The input pixels values are scaled between 0 and 1 and each channel is
normalized with respect to the ImageNet dataset.
|
Raises |
|---|
ValueError
|
In case of unknown data_format argument.
|
View source on GitHub