Global average pooling operation for temporal data.
Inherits From: Layer
, Module
tf.keras.layers.GlobalAveragePooling1D(
data_format='channels_last', **kwargs
)
Examples:
input_shape = (2, 3, 4)
x = tf.random.normal(input_shape)
y = tf.keras.layers.GlobalAveragePooling1D()(x)
print(y.shape)
(2, 4)
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_mean or np.mean .
|
Call arguments |
inputs
|
A 3D tensor.
|
mask
|
Binary tensor of shape (batch_size, steps) indicating whether
a given step should be masked (excluded from the average).
|
|
- 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)
|