a Sic¢ã@sPdZddlmmZddlmZddlmZeddƒGdd„deƒƒZ e Z dS) zMax pooling 2D layer.éN)Ú Pooling2D)Ú keras_exportzkeras.layers.MaxPool2Dzkeras.layers.MaxPooling2Dcs"eZdZdZd‡fdd„ Z‡ZS)Ú MaxPooling2DaMax pooling operation for 2D spatial data. Downsamples the input along its spatial dimensions (height and width) by taking the maximum 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 the `"valid"` padding option, has a spatial 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]) >>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), ... strides=(1, 1), padding='valid') >>> max_pool_2d(x) For example, for `strides=(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]) >>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), ... strides=(2, 2), padding='valid') >>> max_pool_2d(x) Usage Example: >>> input_image = tf.constant([[[[1.], [1.], [2.], [4.]], ... [[2.], [2.], [3.], [2.]], ... [[4.], [1.], [1.], [1.]], ... [[2.], [2.], [1.], [4.]]]]) >>> output = tf.constant([[[[1], [0]], ... [[0], [1]]]]) >>> model = tf.keras.models.Sequential() >>> model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), ... input_shape=(4, 4, 1))) >>> model.compile('adam', 'mean_squared_error') >>> model.predict(input_image, steps=1) array([[[[2.], [4.]], [[4.], [4.]]]], dtype=float32) For example, for stride=(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]) >>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), ... strides=(1, 1), padding='same') >>> max_pool_2d(x) Args: pool_size: integer or tuple of 2 integers, window size over which to take the maximum. `(2, 2)` will take the max value over a 2x2 pooling window. 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. Specifies how far the pooling window moves for each pooling step. 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)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "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)`. Returns: A tensor of rank 4 representing the maximum pooled values. See above for output shape. ©érNÚvalidc s*tƒjtjjjjf||||dœ|¤ŽdS)N)Ú pool_sizeÚstridesÚpaddingÚ data_format)ÚsuperÚ__init__ÚtfÚcompatÚv1ÚnnÚmax_pool)Úselfrr r r Úkwargs©Ú __class__©ú^/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/layers/pooling/max_pooling2d.pyr —s ÿûúzMaxPooling2D.__init__)rNrN)Ú__name__Ú __module__Ú __qualname__Ú__doc__r Ú __classcell__rrrrrs }ûr) rÚtensorflow.compat.v2rÚv2rÚ#keras.layers.pooling.base_pooling2drÚ tensorflow.python.util.tf_exportrrÚ MaxPool2DrrrrÚs