from pyeddl.layers.base import Layer
[docs]class MaxPooling2D(Layer):
"""Max pooling operation for spatial data.
Args:
pool_size: integer or tuple of 2 integers,
factors by which to downscale (vertical, horizontal).
(2, 2) will halve the input in both spatial dimension.
If only one integer is specified, the same window length
will be used for both dimensions.
strides: Integer, tuple of 2 integers, or None.
Strides values.
If None, it will default to `pool_size`.
padding: One of `"valid"` or `"same"` (case-insensitive).
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
Input shape:
- If `data_format='channels_last'`:
4D tensor with shape:
`(batch_size, rows, cols, channels)`
- If `data_format='channels_first'`:
4D tensor with shape:
`(batch_size, channels, rows, cols)`
Output shape:
- If `data_format='channels_last'`:
4D tensor with shape:
`(batch_size, pooled_rows, pooled_cols, channels)`
- If `data_format='channels_first'`:
4D tensor with shape:
`(batch_size, channels, pooled_rows, pooled_cols)`
"""
[docs] def __init__(self, pool_size=(2, 2), strides=None, padding='valid',
data_format=None, **kwargs):
super(MaxPooling2D, self).__init__()