Type SeparableConv2D
Namespace tensorflow.keras.layers
Parent SeparableConv
Interfaces ISeparableConv2D
Depthwise separable 2D convolution. Separable convolutions consist in first performing
a depthwise spatial convolution
(which acts on each input channel separately)
followed by a pointwise convolution which mixes together the resulting
output channels. The `depth_multiplier` argument controls how many
output channels are generated per input channel in the depthwise step. Intuitively, separable convolutions can be understood as
a way to factorize a convolution kernel into two smaller kernels,
or as an extreme version of an Inception block.
Properties
- activation
- activity_regularizer
- activity_regularizer_dyn
- bias
- bias_constraint
- bias_initializer
- bias_regularizer
- built
- data_format
- depth_multiplier
- depthwise_constraint
- depthwise_initializer
- depthwise_kernel
- depthwise_regularizer
- dilation_rate
- dtype
- dtype_dyn
- dynamic
- dynamic_dyn
- filters
- inbound_nodes
- inbound_nodes_dyn
- input
- input_dyn
- input_mask
- input_mask_dyn
- input_shape
- input_shape_dyn
- input_spec
- input_spec_dyn
- kernel
- kernel_constraint
- kernel_initializer
- kernel_regularizer
- kernel_size
- losses
- losses_dyn
- metrics
- metrics_dyn
- name
- name_dyn
- name_scope
- name_scope_dyn
- non_trainable_variables
- non_trainable_variables_dyn
- non_trainable_weights
- non_trainable_weights_dyn
- outbound_nodes
- outbound_nodes_dyn
- output
- output_dyn
- output_mask
- output_mask_dyn
- output_shape
- output_shape_dyn
- padding
- pointwise_constraint
- pointwise_initializer
- pointwise_kernel
- pointwise_regularizer
- PythonObject
- rank
- stateful
- strides
- submodules
- submodules_dyn
- supports_masking
- trainable
- trainable_dyn
- trainable_variables
- trainable_variables_dyn
- trainable_weights
- trainable_weights_dyn
- updates
- updates_dyn
- use_bias
- variables
- variables_dyn
- weights
- weights_dyn