tf.keras.layers.MaxPool1D

TensorFlow 1 version View source on GitHub

Max pooling operation for 1D temporal data.

tf.keras.layers.MaxPool1D(
    pool_size=2, strides=None, padding='valid', data_format='channels_last',
    **kwargs
)

Downsamples the input representation by taking the maximum value over the window defined by pool_size. The window is shifted by strides. The resulting output when using "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) 
<tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy= 
array([[[2.], 
        [3.], 
        [4.], 
        [5.]]], dtype=float32)> 

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) 
<tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy= 
array([[[2.], 
        [4.]]], dtype=float32)> 

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) 
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy= 
array([[[2.], 
        [3.], 
        [4.], 
        [5.], 
        [5.]]], dtype=float32)> 

Arguments:

  • 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" adds no padding. "same" adds padding such that if the stride is 1, the output shape is the same as the input shape.
  • 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).