Max pooling operation for temporal data.
layer_max_pooling_1d(
object,
pool_size = 2L,
strides = NULL,
padding = "valid",
data_format = "channels_last",
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
Integer, size of the max pooling windows.
Integer, or NULL. Factor by which to downscale. E.g. 2 will
halve the input. If NULL, it will default to pool_size.
One of "valid" or "same" (case-insensitive).
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).
Fixed batch size for layer
An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
Whether the layer weights will be updated during training.
Initial weights for layer.
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).
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).
Other pooling layers:
layer_average_pooling_1d(),
layer_average_pooling_2d(),
layer_average_pooling_3d(),
layer_global_average_pooling_1d(),
layer_global_average_pooling_2d(),
layer_global_average_pooling_3d(),
layer_global_max_pooling_1d(),
layer_global_max_pooling_2d(),
layer_global_max_pooling_3d(),
layer_max_pooling_2d(),
layer_max_pooling_3d()