Preprocesses a tensor or Numpy array encoding a batch of images.
tf.keras.applications.resnet_v2.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)
Arguments |
|---|
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 inputs pixel values are scaled between -1 and 1, sample-wise.
|
Raises |
|---|
ValueError
|
In case of unknown data_format argument.
|
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TensorFlow 1 version
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