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|
Randomly crop the images to target height and width.
Inherits From: PreprocessingLayer, Layer, Module
tf.keras.layers.experimental.preprocessing.RandomCrop(
height, width, seed=None, **kwargs
)
This layer will crop all the images in the same batch to the same cropping
location.
By default, random cropping is only applied during training. At inference
time, the images will be first rescaled to preserve the shorter side, and
center cropped. If you need to apply random cropping at inference time,
set training to True when calling the layer.
Input shape:
4D tensor with shape:
(samples, height, width, channels), data_format='channels_last'.
Output shape:
4D tensor with shape:
(samples, target_height, target_width, channels).
Args | |
|---|---|
height
|
Integer, the height of the output shape. |
width
|
Integer, the width of the output shape. |
seed
|
Integer. Used to create a random seed. |
Attributes | |
|---|---|
is_adapted
|
Whether the layer has been fit to data already. |
streaming
|
Whether adapt can be called twice without resetting the state.
|
Methods
adapt
adapt(
data, batch_size=None, steps=None, 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. |
batch_size
|
Integer or None.
Number of samples per state update.
If unspecified, batch_size will default to 32.
Do not specify the batch_size if your data is in the
form of datasets, generators, or keras.utils.Sequence instances
(since they generate batches).
|
steps
|
Integer or None.
Total number of steps (batches of samples)
When training with input tensors such as
TensorFlow data tensors, the default None is equal to
the number of samples in your dataset divided by
the batch size, or 1 if that cannot be determined. If x is a
tf.data dataset, and 'steps' is None, the epoch will run until
the input dataset is exhausted. When passing an infinitely
repeating dataset, you must specify the steps argument. This
argument is not supported with array inputs.
|
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.
|
compile
compile(
run_eagerly=None, steps_per_execution=None
)
Configures the layer for adapt.
| Arguments | |
|---|---|
run_eagerly
|
Bool. Defaults to False. If True, this Model's logic
will not be wrapped in a tf.function. Recommended to leave this as
None unless your Model cannot be run inside a tf.function.
steps_per_execution: Int. Defaults to 1. The number of batches to run
during each tf.function call. Running multiple batches inside a
single tf.function call can greatly improve performance on TPUs or
small models with a large Python overhead.
|
finalize_state
finalize_state()
Finalize the statistics for the preprocessing layer.
This method is called at the end of adapt. This method
handles any one-time operations that should occur after all
data has been seen.
make_adapt_function
make_adapt_function()
Creates a function to execute one step of adapt.
This method can be overridden to support custom adapt logic.
This method is called by PreprocessingLayer.adapt.
Typically, this method directly controls tf.function settings,
and delegates the actual state update logic to
PreprocessingLayer.update_state.
This function is cached the first time PreprocessingLayer.adapt
is called. The cache is cleared whenever PreprocessingLayer.compile
is called.
| Returns | |
|---|---|
Function. The function created by this method should accept a
tf.data.Iterator, retrieve a batch, and update the state of the
layer.
|
merge_state
merge_state(
layers
)
Merge the statistics of multiple preprocessing layers.
This layer will contain the merged state.
| Arguments | |
|---|---|
layers
|
Layers whose statistics should be merge with the statistics of this layer. |
reset_state
reset_state()
Resets the statistics of the preprocessing layer.
update_state
update_state(
data
)
Accumulates statistics for the preprocessing layer.
| Arguments | |
|---|---|
data
|
A mini-batch of inputs to the layer. |