93 lines
3.3 KiB
Python
93 lines
3.3 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.AveragePooling1D", "keras_core.layers.AvgPool1D"]
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)
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class AveragePooling1D(BasePooling):
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"""Average pooling for temporal data.
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Downsamples the input representation by taking the average value over the
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window defined by `pool_size`. The window is shifted by `strides`. The
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resulting output when using "valid" padding option has a shape of:
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`output_shape = (input_shape - pool_size + 1) / strides)`
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The resulting output shape when using the "same" padding option is:
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`output_shape = input_shape / strides`
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Args:
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pool_size: int, size of the max pooling window.
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strides: int or None. Specifies how much the pooling window moves
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for each pooling step. If None, it will default to `pool_size`.
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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, steps, features)`
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while `"channels_first"` corresponds to inputs with shape
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`(batch, features, steps)`. It defaults to the `image_data_format`
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value found in your Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be `"channels_last"`.
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Input shape:
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- If `data_format="channels_last"`:
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3D tensor with shape `(batch_size, steps, features)`.
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- If `data_format="channels_first"`:
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3D tensor with shape `(batch_size, features, steps)`.
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Output shape:
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- If `data_format="channels_last"`:
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3D tensor with shape `(batch_size, downsampled_steps, features)`.
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- If `data_format="channels_first"`:
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3D tensor with shape `(batch_size, features, downsampled_steps)`.
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Examples:
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`strides=1` and `padding="valid"`:
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>>> x = np.array([1., 2., 3., 4., 5.])
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>>> x = np.reshape(x, [1, 5, 1])
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>>> avg_pool_1d = keras_core.layers.AveragePooling1D(pool_size=2,
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... strides=1, padding="valid")
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>>> avg_pool_1d(x)
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`strides=2` and `padding="valid"`:
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>>> x = np.array([1., 2., 3., 4., 5.])
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>>> x = np.reshape(x, [1, 5, 1])
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>>> avg_pool_1d = keras_core.layers.AveragePooling1D(pool_size=2,
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... strides=2, padding="valid")
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>>> avg_pool_1d(x)
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`strides=1` and `padding="same"`:
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>>> x = np.array([1., 2., 3., 4., 5.])
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>>> x = np.reshape(x, [1, 5, 1])
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>>> avg_pool_1d = keras_core.layers.AveragePooling1D(pool_size=2,
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... strides=1, padding="same")
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>>> avg_pool_1d(x)
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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=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=1,
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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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