110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
from keras_core.api_export import keras_core_export
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from keras_core.layers.pooling.base_pooling import BasePooling
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@keras_core_export(
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["keras_core.layers.MaxPooling2D", "keras_core.layers.MaxPool2D"]
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)
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class MaxPooling2D(BasePooling):
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"""Max pooling operation for 2D spatial data.
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Downsamples the input along its spatial dimensions (height and width)
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by taking the maximum value over an input window
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(of size defined by `pool_size`) for each channel of the input.
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The window is shifted by `strides` along each dimension.
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The resulting output when using the `"valid"` padding option has a spatial
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shape (number of rows or columns) of:
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`output_shape = math.floor((input_shape - pool_size) / strides) + 1`
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(when `input_shape >= pool_size`)
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The resulting output shape when using the `"same"` padding option is:
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`output_shape = math.floor((input_shape - 1) / strides) + 1`
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Args:
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pool_size: int or tuple of 2 integers, factors by which to downscale
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(dim1, dim2). If only one integer is specified, the same
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window length will be used for all dimensions.
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strides: int or tuple of 2 integers, or None. Strides values. If None,
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it will default to `pool_size`. If only one int is specified, the
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same stride size will be used for all dimensions.
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padding: string, either `"valid"` or `"same"` (case-insensitive).
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`"valid"` means no padding. `"same"` results in padding evenly to
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the left/right or up/down of the input such that output has the same
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height/width dimension as the input.
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data_format: string, either `"channels_last"` or `"channels_first"`.
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The ordering of the dimensions in the inputs. `"channels_last"`
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corresponds to inputs with shape `(batch, height, width, channels)`
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while `"channels_first"` corresponds to inputs with shape
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`(batch, channels, height, width)`. It defaults to the
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`image_data_format` value found in your Keras config file at
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`~/.keras/keras.json`. If you never set it, then it will be
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`"channels_last"`.
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Input shape:
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- If `data_format="channels_last"`:
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4D tensor with shape `(batch_size, height, width, channels)`.
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- If `data_format="channels_first"`:
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4D tensor with shape `(batch_size, channels, height, width)`.
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Output shape:
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- If `data_format="channels_last"`:
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4D tensor with shape
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`(batch_size, pooled_height, pooled_width, channels)`.
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- If `data_format="channels_first"`:
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4D tensor with shape
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`(batch_size, channels, pooled_height, pooled_width)`.
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Examples:
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`strides=(1, 1)` and `padding="valid"`:
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>>> x = np.array([[1., 2., 3.],
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... [4., 5., 6.],
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... [7., 8., 9.]])
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>>> x = np.reshape(x, [1, 3, 3, 1])
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>>> max_pool_2d = keras_core.layers.MaxPooling2D(pool_size=(2, 2),
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... strides=(1, 1), padding="valid")
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>>> max_pool_2d(x)
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`strides=(2, 2)` and `padding="valid"`:
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>>> x = np.array([[1., 2., 3., 4.],
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... [5., 6., 7., 8.],
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... [9., 10., 11., 12.]])
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>>> x = np.reshape(x, [1, 3, 4, 1])
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>>> max_pool_2d = keras_core.layers.MaxPooling2D(pool_size=(2, 2),
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... strides=(2, 2), padding="valid")
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>>> max_pool_2d(x)
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`stride=(1, 1)` and `padding="same"`:
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>>> x = np.array([[1., 2., 3.],
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... [4., 5., 6.],
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... [7., 8., 9.]])
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>>> x = np.reshape(x, [1, 3, 3, 1])
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>>> max_pool_2d = keras_core.layers.MaxPooling2D(pool_size=(2, 2),
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... strides=(1, 1), padding="same")
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>>> max_pool_2d(x)
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"""
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def __init__(
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self,
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pool_size=(2, 2),
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strides=None,
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padding="valid",
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data_format=None,
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name=None,
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**kwargs
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):
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super().__init__(
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pool_size,
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strides,
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pool_dimensions=2,
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pool_mode="max",
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padding=padding,
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data_format=data_format,
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name=name,
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**kwargs,
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)
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