86 lines
3.2 KiB
Python
86 lines
3.2 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.AveragePooling3D", "keras_core.layers.AvgPool3D"]
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)
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class AveragePooling3D(BasePooling):
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"""Average pooling operation for 3D data (spatial or spatio-temporal).
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Downsamples the input along its spatial dimensions (depth, height, and
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width) by taking the average value over an input window (of size defined by
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`pool_size`) for each channel of the input. The window is shifted by
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`strides` along each dimension.
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Args:
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pool_size: int or tuple of 3 integers, factors by which to downscale
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(dim1, dim2, dim3). 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 3 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
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`(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while
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`"channels_first"` corresponds to inputs with shape
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`(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
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It defaults to the `image_data_format` value found in your Keras
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config file at `~/.keras/keras.json`. If you never set it, then it
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will be `"channels_last"`.
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Input shape:
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- If `data_format="channels_last"`:
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5D tensor with shape:
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`(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
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- If `data_format="channels_first"`:
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5D tensor with shape:
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`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`
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Output shape:
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- If `data_format="channels_last"`:
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5D tensor with shape:
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`(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`
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- If `data_format="channels_first"`:
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5D tensor with shape:
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`(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)`
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Example:
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```python
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depth = 30
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height = 30
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width = 30
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channels = 3
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inputs = keras_core.layers.Input(shape=(depth, height, width, channels))
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layer = keras_core.layers.AveragePooling3D(pool_size=3)
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outputs = layer(inputs) # Shape: (batch_size, 10, 10, 10, 3)
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```
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"""
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def __init__(
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self,
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pool_size,
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strides=None,
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padding="valid",
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data_format="channels_last",
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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=3,
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pool_mode="average",
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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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