tf.keras.layers.Cropping3D | TensorFlow v2.0.0 (original) (raw)
tf.keras.layers.Cropping3D
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Cropping layer for 3D data (e.g. spatial or spatio-temporal).
Inherits From: Layer
View aliases
Compat aliases for migration
SeeMigration guide for more details.
tf.compat.v1.keras.layers.Cropping3D
tf.keras.layers.Cropping3D(
cropping=((1, 1), (1, 1), (1, 1)), data_format=None, **kwargs
)
Arguments | |
---|---|
cropping | Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints. If int: the same symmetric cropping is applied to depth, height, and width. If tuple of 3 ints: interpreted as two different symmetric cropping values for depth, height, and width:(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop). If tuple of 3 tuples of 2 ints: interpreted as((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop)) |
data_format | A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.channels_last corresponds to inputs with shape(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)while channels_first corresponds to inputs with shape(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). It defaults to the image_data_format value 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_format
is"channels_last"
:(batch, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)
- If
data_format
is"channels_first"
:(batch, depth, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)
Output shape:
5D tensor with shape:
- If
data_format
is"channels_last"
:(batch, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)
- If
data_format
is"channels_first"
:(batch, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)
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Last updated 2020-10-01 UTC.