Cropping layer for 3D data (e.g. spatial or spatio-temporal).
layer_cropping_3d(
object,
cropping = list(c(1L, 1L), c(1L, 1L), c(1L, 1L)),
data_format = NULL,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)
What to compose the new Layer
instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()
).
The return value depends on object
. If object
is:
missing or NULL
, the Layer
instance is returned.
a Sequential
model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object)
is returned.
int, or list of 3 ints, or list of 3 lists of 2 ints.
If int: the same symmetric cropping is applied to depth, height, and width.
If list 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 list of 3 list of 2 ints:
interpreted as
((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))
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".
Fixed batch size for layer
An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
Whether the layer weights will be updated during training.
Initial weights for layer.
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)
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)
Other convolutional layers:
layer_conv_1d_transpose()
,
layer_conv_1d()
,
layer_conv_2d_transpose()
,
layer_conv_2d()
,
layer_conv_3d_transpose()
,
layer_conv_3d()
,
layer_conv_lstm_2d()
,
layer_cropping_1d()
,
layer_cropping_2d()
,
layer_depthwise_conv_1d()
,
layer_depthwise_conv_2d()
,
layer_separable_conv_1d()
,
layer_separable_conv_2d()
,
layer_upsampling_1d()
,
layer_upsampling_2d()
,
layer_upsampling_3d()
,
layer_zero_padding_1d()
,
layer_zero_padding_2d()
,
layer_zero_padding_3d()