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tf.keras.layers.MaxPool1D

TensorFlow 1 version View source on GitHub

Max pooling operation for 1D temporal data.

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)>

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).