o 7?ef@sPdZddlmmZddlmZddlmZeddGdddeZ e Z dS) zAverage pooling 2D layer.N) Pooling2D) keras_exportzkeras.layers.AveragePooling2Dzkeras.layers.AvgPool2Dcs*eZdZdZ    dfdd ZZS)AveragePooling2DaAverage 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 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)`. rNvalidc s&tjtjjf||||d|dS)N) pool_sizestridespadding data_format)super__init__tfnnZavg_pool)selfrr r r kwargs __class__k/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/keras/src/layers/pooling/average_pooling2d.pyr s zAveragePooling2D.__init__)rNrN)__name__ __module__ __qualname____doc__r __classcell__rrrrrsgr) rZtensorflow.compat.v2compatv2rZ'keras.src.layers.pooling.base_pooling2drZ tensorflow.python.util.tf_exportrrZ AvgPool2Drrrrs  z