Cropping layer for 2D input (e.g. picture).
It crops along spatial dimensions, i.e. width and height.
layer_cropping_2d( object, cropping = list(c(0L, 0L), c(0L, 0L)), data_format = NULL, batch_size = NULL, name = NULL, trainable = NULL, weights = NULL )
object |
Model or layer object |
cropping |
int, or list of 2 ints, or list of 2 lists of 2 ints.
|
data_format |
A string, one of |
batch_size |
Fixed batch size for layer |
name |
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. |
trainable |
Whether the layer weights will be updated during training. |
weights |
Initial weights for layer. |
4D tensor with shape:
If data_format
is "channels_last"
: (batch, rows, cols, channels)
If data_format
is "channels_first"
: (batch, channels, rows, cols)
4D tensor with shape:
If data_format
is "channels_last"
: (batch, cropped_rows, cropped_cols, channels)
If data_format
is "channels_first"
: (batch, channels, cropped_rows, cropped_cols)
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_3d()
,
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()
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