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Dominik Moritz Roth 2024-05-25 21:40:07 +02:00
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1 changed files with 10 additions and 11 deletions
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config.yaml
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@ -43,29 +43,28 @@ import: $
latent_projector: latent_projector:
type: rnn # Options: 'fc', 'rnn' type: rnn # Options: 'fc', 'rnn'
input_size: 19531 # =1s Input size for the Latent Projector (length of snippets). input_size: 195 # =0.01s 19531 # =1s Input size for the Latent Projector (length of snippets).
latent_size: 8 # Size of the latent representation before message passing. latent_size: 4 # Size of the latent representation before message passing.
#layer_shapes: [256, 32] # List of layer sizes for the latent projector (if type is 'fc'). #layer_shapes: [256, 32] # List of layer sizes for the latent projector (if type is 'fc').
#activations: ['ReLU', 'ReLU'] # Activation functions for the latent projector layers (if type is 'fc'). #activations: ['ReLU', 'ReLU'] # Activation functions for the latent projector layers (if type is 'fc').
rnn_hidden_size: 12 # Hidden size for the RNN projector (if type is 'rnn'). rnn_hidden_size: 4 # Hidden size for the RNN projector (if type is 'rnn').
rnn_num_layers: 1 # Number of layers for the RNN projector (if type is 'rnn'). rnn_num_layers: 1 # Number of layers for the RNN projector (if type is 'rnn').
middle_out: middle_out:
output_size: 8 # Size of the latent representation after message passing. output_size: 4 # Size of the latent representation after message passing.
num_peers: 3 # Number of most correlated peers to consider. num_peers: 3 # Number of most correlated peers to consider.
predictor: predictor:
layer_shapes: [8, 4] # List of layer sizes for the predictor. layer_shapes: [4] # List of layer sizes for the predictor.
activations: ['ReLU', 'None'] # Activation functions for the predictor layers. activations: ['ELU'] # Activation functions for the predictor layers.
training: training:
epochs: 128 # Number of training epochs. epochs: 1024 # Number of training epochs.
batch_size: 64 # Batch size for training. batch_size: 16 # 64 # Batch size for training.
num_batches: 16 # Batches per epoch num_batches: 4 # Batches per epoch
learning_rate: 0.001 # Learning rate for the optimizer. learning_rate: 0.05 # Learning rate for the optimizer.
eval_freq: -1 # 8 # Frequency of evaluation during training (in epochs). eval_freq: -1 # 8 # Frequency of evaluation during training (in epochs).
save_path: models # Directory to save the best model and encoder. save_path: models # Directory to save the best model and encoder.
num_points: 1000 # Number of data points to visualize
evaluation: evaluation:
full_compression: false # Perform full compression during evaluation full_compression: false # Perform full compression during evaluation