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tf.keras.layers.Cropping3D
Cropping layer for 3D data (e.g. spatial or spatio-temporal).
tf.keras.layers.Cropping3D(
    cropping=((1, 1), (1, 1), (1, 1)), data_format=None, **kwargs
)
Examples:
input_shape = (2, 28, 28, 10, 3)
x = np.arange(np.prod(input_shape)).reshape(input_shape)
y = tf.keras.layers.Cropping3D(cropping=(2, 4, 2))(x)
print(y.shape)
(2, 24, 20, 6, 3)
| Arguments | |
|---|---|
| cropping | Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints. 
 | 
| data_format | A string, one of channels_last(default) orchannels_first. The ordering of the dimensions in the inputs.channels_lastcorresponds to inputs with shape(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)whilechannels_firstcorresponds to inputs with shape(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). It defaults to theimage_data_formatvalue found in your Keras config file at~/.keras/keras.json. If you never set it, then it will be "channels_last". | 
Input shape:
5D tensor with shape:
- If data_formatis"channels_last":(batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)
- If data_formatis"channels_first":(batch_size, depth, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)
Output shape:
5D tensor with shape:
- If data_formatis"channels_last":(batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)
- If data_formatis"channels_first":(batch_size, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
 https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/layers/Cropping3D