tf.keras.layers.Cropping1D

TensorFlow 1 version

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Cropping layer for 3D data (e.g. spatial or spatio-temporal).

Inherits From: Layer, Module

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Compat aliases for migration

See Migration 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
)

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. 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_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch_size, 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".

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_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape (batch_size, 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_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)
If data_format is "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_format is "channels_last": (batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)
If data_format is "channels_first": (batch_size, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)

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tf.keras / layers / layers.Cropping1D

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Arguments

Input shape:

Output shape: