Max pooling operation for 1D temporal data.

Inherits From: Layer, Module

Used in the notebooks

Used in the tutorials

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')
<tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy=
        [5.]]], dtype=float32)>

For example, for strides=2 and