def _create_and_connect_agent(self, input_image: MemoryBlock,
input_size: Tuple[int, int, int]):
params = MultipleLayersParams()
params.num_conv_layers = 4
params.n_flocks = [5, 5, 1, 1]
params.n_cluster_centers = [30, 60, 60, 9]
params.compute_reconstruction = True
params.conv_classes = SpConvLayer
params.sp_buffer_size = 5000
params.sp_batch_size = 500
params.learning_rate = 0.1
params.cluster_boost_threshold = 1000
params.max_encountered_seqs = 1000
params.max_frequent_seqs = 500
params.seq_lookahead = 2
params.seq_length = 4
params.exploration_probability = 0
params.rf_size = (2, 2)
params.rf_stride = None
ta_group = NCMGroup(conv_layers_params=params,
model_seed=None,
image_size=input_size)
self.add_node(ta_group)
Connector.connect(input_image, ta_group.inputs.image)
def _create_and_connect_agent(self, input_image: MemoryBlock,
output_reconstruction: InputSlot,
input_size: Tuple[int, int, int]):
params = MultipleLayersParams()
params.num_conv_layers = 3
params.n_cluster_centers = [28, 14, 7]
params.compute_reconstruction = True
params.conv_classes = ConvLayer
params.sp_buffer_size = 5000
params.sp_batch_size = 500
params.learning_rate = 0.2
params.cluster_boost_threshold = 1000
params.max_encountered_seqs = 1000
params.max_frequent_seqs = 500
params.seq_lookahead = 2
params.seq_length = 4
params.exploration_probability = 0
params.rf_size = [(input_size[0], input_size[1]), (1, 1), (1, 1)]
params.rf_stride = None
ta_group = R1NCMGroup(conv_layers_params=params,
model_seed=None,
image_size=input_size)
self.add_node(ta_group)
Connector.connect(input_image, ta_group.inputs.image)
Connector.connect(ta_group.outputs.predicted_reconstructed_input,
output_reconstruction)
