MaxPooling1D layer

[source]

MaxPooling1D class

tf_keras.layers.MaxPooling1D(
    pool_size=2, strides=None, padding="valid", data_format="channels_last", **kwargs
)

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)
<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" 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).