deepof.models.get_TCN_decoder

deepof.models.get_TCN_decoder(input_shape: tuple, latent_dim: int, conv_filters: int = 64, kernel_size: int = 4, conv_stacks: int = 1, conv_dilations: tuple = (8, 4, 2, 1), padding: str = 'causal', use_skip_connections: bool = True, dropout_rate: int = 0, activation: str = 'relu')

Return a Temporal Convolutional Network (TCN) decoder.

Builds a neural network that can be used to decode a latent space into a sequence of motion tracking instances. Each layer contains a residual block with a convolutional layer and a skip connection. See the following paper for more details: https://arxiv.org/pdf/1803.01271.pdf,

Parameters:

  • input_shape – shape of the input data

  • latent_dim – dimensionality of the latent space

  • conv_filters – number of filters in the TCN layers

  • kernel_size – size of the convolutional kernels

  • conv_stacks – number of TCN layers

  • conv_dilations – list of dilation factors for each TCN layer

  • padding – padding mode for the TCN layers

  • use_skip_connections – whether to use skip connections between TCN layers

  • dropout_rate – dropout rate for the TCN layers

  • activation – activation function for the TCN layers

Returns:

a keras model that can be trained to decode a latent space into a sequence of motion tracking instances using temporal convolutional networks.

Return type:

keras.Model