TensorFlow 1 version | View source on GitHub |
Cropping layer for 2D input (e.g. picture).
Inherits From: Layer
tf.keras.layers.Cropping2D(
cropping=((0, 0), (0, 0)), data_format=None, **kwargs
)
It crops along spatial dimensions, i.e. height and width.
Arguments | |
---|---|
cropping
|
Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
|
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, height, width, channels) while channels_first
corresponds to inputs with shape
(batch, channels, height, width) .
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:
4D tensor with shape:
- If
data_format
is"channels_last"
:(batch, rows, cols, channels)
- If
data_format
is"channels_first"
:(batch, channels, rows, cols)
Output shape:
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)
Examples:
# Crop the input 2D images or feature maps
model = Sequential()
model.add(Cropping2D(cropping=((2, 2), (4, 4)),
input_shape=(28, 28, 3)))
# now model.output_shape == (None, 24, 20, 3)
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Cropping2D(cropping=((2, 2), (2, 2))))
# now model.output_shape == (None, 20, 16. 64)