tf.keras.layers.MaxPool1D

View source on GitHub

Max pooling operation for 1D temporal data.

View aliases

Main aliases

tf.keras.layers.MaxPooling1D

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.MaxPool1D, tf.compat.v1.keras.layers.MaxPooling1D

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).