o i ew @sJdZddlmZddlmZddlmZeddGdddeZeZd S) zGlobal max pooling 1D layer.)backend)GlobalPooling1D) keras_exportzkeras.layers.GlobalMaxPooling1Dzkeras.layers.GlobalMaxPool1Dc@seZdZdZddZdS)GlobalMaxPooling1DaGlobal max pooling operation for 1D temporal data. Downsamples the input representation by taking the maximum value over the time dimension. For example: >>> x = tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]) >>> x = tf.reshape(x, [3, 3, 1]) >>> x >>> max_pool_1d = tf.keras.layers.GlobalMaxPooling1D() >>> max_pool_1d(x) Args: 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)`. keepdims: A boolean, whether to keep the temporal dimension or not. If `keepdims` is `False` (default), the rank of the tensor is reduced for spatial dimensions. If `keepdims` is `True`, the temporal dimension are retained with length 1. The behavior is the same as for `tf.reduce_max` or `np.max`. 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 `keepdims`=False: 2D tensor with shape `(batch_size, features)`. - If `keepdims`=True: - If `data_format='channels_last'`: 3D tensor with shape `(batch_size, 1, features)` - If `data_format='channels_first'`: 3D tensor with shape `(batch_size, features, 1)` cCs$|jdkrdnd}tj|||jdS)N channels_last)axiskeepdims) data_formatrmaxr )selfinputs steps_axisr\/var/www/myenv/lib/python3.10/site-packages/keras/src/layers/pooling/global_max_pooling1d.pycallQszGlobalMaxPooling1D.callN)__name__ __module__ __qualname____doc__rrrrrrs 6rN) r keras.srcr.keras.src.layers.pooling.base_global_pooling1dr tensorflow.python.util.tf_exportrrGlobalMaxPool1Drrrrs   >