6bifSrSSKrSSKJr SSKJr SSKJr \"SS5"SS \55r\r g) zAverage pooling 1D layer.N)backend) Pooling1D) keras_exportzkeras.layers.AveragePooling1Dzkeras.layers.AvgPool1Dc8^\rSrSrSrSU4SjjrSrU=r$)AveragePooling1Dab Average pooling for temporal data. Downsamples the input representation by taking the average value over the window defined by `pool_size`. The window is shifted by `strides`. The resulting output when using "valid" padding option has a shape of: `output_shape = (input_shape - pool_size + 1) / strides)` The resulting output shape when using the "same" padding option is: `output_shape = input_shape / strides` For example, for strides=1 and padding="valid": >>> x = tf.constant([1., 2., 3., 4., 5.]) >>> x = tf.reshape(x, [1, 5, 1]) >>> x >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2, ... strides=1, padding='valid') >>> avg_pool_1d(x) For example, for strides=2 and padding="valid": >>> x = tf.constant([1., 2., 3., 4., 5.]) >>> x = tf.reshape(x, [1, 5, 1]) >>> x >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2, ... strides=2, padding='valid') >>> avg_pool_1d(x) For example, for strides=1 and padding="same": >>> x = tf.constant([1., 2., 3., 4., 5.]) >>> x = tf.reshape(x, [1, 5, 1]) >>> x >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2, ... strides=1, padding='same') >>> avg_pool_1d(x) Args: pool_size: Integer, size of the average pooling windows. strides: Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to `pool_size`. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, steps, features)` while `channels_first` corresponds to inputs with shape `(batch, features, steps)`. Input shape: - If `data_format='channels_last'`: 3D tensor with shape `(batch_size, steps, features)`. - If `data_format='channels_first'`: 3D tensor with shape `(batch_size, features, steps)`. Output shape: - If `data_format='channels_last'`: 3D tensor with shape `(batch_size, downsampled_steps, features)`. - If `data_format='channels_first'`: 3D tensor with shape `(batch_size, features, downsampled_steps)`. c t>[TU]"[R"[R SS94UUUUS.UD6 g)Navg) pool_mode) pool_sizestridespadding data_format)super__init__ functoolspartialrpool2d)selfr r rrkwargs __class__s g/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/layers/pooling/average_pooling1d.pyrAveragePooling1D.__init__sA    gnn > #    )Nvalid channels_last)__name__ __module__ __qualname____firstlineno____doc__r__static_attributes__ __classcell__)rs@rrrs"aJ#   rr) r#r tf_keras.srcr*tf_keras.src.layers.pooling.base_pooling1dr tensorflow.python.util.tf_exportrr AvgPool1Drrrr*sJ  @:-/GHs ys Is p  r