6bildSrSSKJs Jr SSKJr SSKJr \"SS5"SS\55r \ r g) zAverage pooling 2D layer.N) Pooling2D) keras_exportzkeras.layers.AveragePooling2Dzkeras.layers.AvgPool2Dc8^\rSrSrSrSU4SjjrSrU=r$)AveragePooling2Da~Average pooling operation for spatial data. Downsamples the input along its spatial dimensions (height and width) by taking the average value over an input window (of size defined by `pool_size`) for each channel of the input. The window is shifted by `strides` along each dimension. The resulting output when using `"valid"` padding option has a shape (number of rows or columns) of: `output_shape = math.floor((input_shape - pool_size) / strides) + 1` (when `input_shape >= pool_size`) The resulting output shape when using the `"same"` padding option is: `output_shape = math.floor((input_shape - 1) / strides) + 1` For example, for `strides=(1, 1)` and `padding="valid"`: >>> x = tf.constant([[1., 2., 3.], ... [4., 5., 6.], ... [7., 8., 9.]]) >>> x = tf.reshape(x, [1, 3, 3, 1]) >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), ... strides=(1, 1), padding='valid') >>> avg_pool_2d(x) For example, for `stride=(2, 2)` and `padding="valid"`: >>> x = tf.constant([[1., 2., 3., 4.], ... [5., 6., 7., 8.], ... [9., 10., 11., 12.]]) >>> x = tf.reshape(x, [1, 3, 4, 1]) >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), ... strides=(2, 2), padding='valid') >>> avg_pool_2d(x) For example, for `strides=(1, 1)` and `padding="same"`: >>> x = tf.constant([[1., 2., 3.], ... [4., 5., 6.], ... [7., 8., 9.]]) >>> x = tf.reshape(x, [1, 3, 3, 1]) >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), ... strides=(1, 1), padding='same') >>> avg_pool_2d(x) 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). `"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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. When unspecified, uses `image_data_format` value found in your TF-Keras config file at `~/.keras/keras.json` (if exists) else 'channels_last'. Defaults to 'channels_last'. 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)`. c b>[TU]"[RR4UUUUS.UD6 g)N) pool_sizestridespadding data_format)super__init__tfnnavg_pool)selfr r r r kwargs __class__s g/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/layers/pooling/average_pooling2d.pyrAveragePooling2D.__init__s8  EENN #    ))rNvalidN)__name__ __module__ __qualname____firstlineno____doc__r__static_attributes__ __classcell__)rs@rrrs"cN   rr) rtensorflow.compat.v2compatv2r*tf_keras.src.layers.pooling.base_pooling2dr tensorflow.python.util.tf_exportrr AvgPool2Drrrr(sJ "!@:-/GHu yu Iu t  r