a SicU@sRdZddlZddlmZddlmZddlmZeddGdd d eZeZ dS) zMax pooling 1D layer.N)backend) Pooling1D) keras_exportzkeras.layers.MaxPool1Dzkeras.layers.MaxPooling1Dcs"eZdZdZdfdd ZZS) MaxPooling1Da Max pooling operation for 1D temporal data. Downsamples the input representation by taking the maximum value over a spatial window of size `pool_size`. The window is shifted by `strides`. The resulting output, when using the `"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]) >>> max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2, ... strides=1, padding='valid') >>> max_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]) >>> max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2, ... strides=2, padding='valid') >>> max_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]) >>> max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2, ... strides=1, padding='same') >>> max_pool_1d(x) Args: pool_size: Integer, size of the max pooling window. strides: Integer, or None. Specifies how much 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, 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)`. Nvalid channels_lastc s.tjtjtjddf||||d|dS)Nmax) pool_mode) pool_sizestridespadding data_format)super__init__ functoolspartialrpool2d)selfr r r rkwargs __class__^/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/layers/pooling/max_pooling1d.pyrks zMaxPooling1D.__init__)rNrr)__name__ __module__ __qualname____doc__r __classcell__rrrrrs Pr) rrkerasr#keras.layers.pooling.base_pooling1dr tensorflow.python.util.tf_exportrr MaxPool1Drrrrs   d