message: Update documentation for MaxPooling1D including examples.
diff: @@ -110,14 +110,62 @@ class Pooling1D(Layer):
  @keras_export('keras.layers.MaxPool1D', 'keras.layers.MaxPooling1D')
  class MaxPooling1D(Pooling1D):
  - """Max pooling operation for temporal data.
  + """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)>
  Arguments:
  - pool_size: Integer, size of the max pooling windows.
  - strides: Integer, or None. Factor by which to downscale.
  - E.g. 2 will halve the input.
  + 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.
  