keras_visual_layer_type module#

class edgeimpulse_api.models.keras_visual_layer_type.KerasVisualLayerType(
value=<no_arg>,
names=None,
module=None,
qualname=None,
type=None,
start=1,
boundary=None,
)[source]#

Bases: str, Enum

allowed enum values

BATCH_NORMALIZATION = 'batchNormalization'#
CONV1D = 'conv1d'#
CONV2D = 'conv2d'#
DENSE = 'dense'#
DROPOUT = 'dropout'#
FLATTEN = 'flatten'#
FOMO_AKIDANET_A50 = 'fomo_akidanet_a50'#
FOMO_MOBILENET_V2_A01 = 'fomo_mobilenet_v2_a01'#
FOMO_MOBILENET_V2_A35 = 'fomo_mobilenet_v2_a35'#
OBJECT_SSD_MOBILENET_V2_FPNLITE_320X320 = 'object_ssd_mobilenet_v2_fpnlite_320x320'#
RESHAPE = 'reshape'#
TRANSFER_AKIDANET_IMAGENET_160_A100 = 'transfer_akidanet_imagenet_160_a100'#
TRANSFER_AKIDANET_IMAGENET_160_A25 = 'transfer_akidanet_imagenet_160_a25'#
TRANSFER_AKIDANET_IMAGENET_160_A50 = 'transfer_akidanet_imagenet_160_a50'#
TRANSFER_AKIDANET_IMAGENET_224_A100 = 'transfer_akidanet_imagenet_224_a100'#
TRANSFER_AKIDANET_IMAGENET_224_A25 = 'transfer_akidanet_imagenet_224_a25'#
TRANSFER_AKIDANET_IMAGENET_224_A50 = 'transfer_akidanet_imagenet_224_a50'#
TRANSFER_KWS_CONV2D_TINY = 'transfer_kws_conv2d_tiny'#
TRANSFER_KWS_MOBILENETV1_A1_D100 = 'transfer_kws_mobilenetv1_a1_d100'#
TRANSFER_KWS_MOBILENETV2_A35_D100 = 'transfer_kws_mobilenetv2_a35_d100'#
TRANSFER_KWS_SYNTIANT_NDP10X = 'transfer_kws_syntiant_ndp10x'#
TRANSFER_MOBILENETV1_A1_D100 = 'transfer_mobilenetv1_a1_d100'#
TRANSFER_MOBILENETV1_A25_D100 = 'transfer_mobilenetv1_a25_d100'#
TRANSFER_MOBILENETV1_A2_D100 = 'transfer_mobilenetv1_a2_d100'#
TRANSFER_MOBILENETV2_160_A1 = 'transfer_mobilenetv2_160_a1'#
TRANSFER_MOBILENETV2_160_A35 = 'transfer_mobilenetv2_160_a35'#
TRANSFER_MOBILENETV2_160_A5 = 'transfer_mobilenetv2_160_a5'#
TRANSFER_MOBILENETV2_160_A75 = 'transfer_mobilenetv2_160_a75'#
TRANSFER_MOBILENETV2_A05 = 'transfer_mobilenetv2_a05'#
TRANSFER_MOBILENETV2_A1 = 'transfer_mobilenetv2_a1'#
TRANSFER_MOBILENETV2_A35 = 'transfer_mobilenetv2_a35'#
TRANSFER_ORGANIZATION = 'transfer_organization'#