Global max pooling operation for 1D temporal data.
Inherits From: Layer
, Module
tf.keras.layers.GlobalMaxPooling1D(
data_format='channels_last', keepdims=False, **kwargs
)
Downsamples the input representation by taking the maximum value over
the time dimension.
For example:
x = tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]])
x = tf.reshape(x, [3, 3, 1])
x
<tf.Tensor: shape=(3, 3, 1), dtype=float32, numpy=
array([[[1.], [2.], [3.]],
[[4.], [5.], [6.]],
[[7.], [8.], [9.]]], dtype=float32)>
max_pool_1d = tf.keras.layers.GlobalMaxPooling1D()
max_pool_1d(x)
<tf.Tensor: shape=(3, 1), dtype=float32, numpy=
array([[3.],
[6.],
[9.], dtype=float32)>
Args |
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) .
|
keepdims
|
A boolean, whether to keep the temporal dimension or not.
If keepdims is False (default), the rank of the tensor is reduced
for spatial dimensions.
If keepdims is True , the temporal dimension are retained with
length 1.
The behavior is the same as for tf.reduce_max or np.max .
|
|
- 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
keepdims =False:
2D tensor with shape (batch_size, features) .
- If
keepdims =True:
- If
data_format='channels_last' :
3D tensor with shape (batch_size, 1, features)
- If
data_format='channels_first' :
3D tensor with shape (batch_size, features, 1)
|