Doc new features and new tuned model
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config.yaml
843
config.yaml
@ -7,12 +7,9 @@ feature_extractor:
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length: 8 # Number of last samples to pass directly. Use full input size if set to null.
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- type: 'fourier' # Apply Fourier transform to the input data.
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length: null # Use full input size if set to null. Fourier transform outputs both real and imaginary parts, doubling the size. (Computationally expensive)
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- type: 'wavelet' # Apply selected wavelet transform to the input data.
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wavelet_type: 'haar' # Haar wavelet is simple and fast, but may not capture detailed features well.
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length: null # Use full input size if set to null.
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- type: 'wavelet'
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wavelet_type: 'cgau1' # Complex Gaussian wavelets are used for complex-valued signal analysis and capturing phase information.
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length: null # Use full input size if set to null.
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- type: 'wavelet' # (Pro Tip: Discrete Meyer are great for recognizing spikes)
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wavelet_type: 'dmey' # Discrete Meyer wavelets offer good frequency localization, ideal for signals with oscillatory components.
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length: null # Use full input size if set to null. (Computationally expensive)
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- type: 'wavelet'
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wavelet_type: 'db1' # Daubechies wavelets provide a balance between time and frequency localization.
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length: null # Use full input size if set to null. (Computationally expensive)
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@ -29,11 +26,11 @@ feature_extractor:
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wavelet_type: 'rbio1.3' # Reverse Biorthogonal wavelets are similar to Biorthogonal but optimized for different applications.
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length: null # Use full input size if set to null. (Computationally expensive)
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- type: 'wavelet'
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wavelet_type: 'dmey' # Discrete Meyer wavelets offer good frequency localization, ideal for signals with oscillatory components.
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length: null # Use full input size if set to null. (Computationally expensive)
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wavelet_type: 'haar' # Haar wavelet is simple and fast, but may not capture detailed features well.
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length: null # Use full input size if set to null.
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- type: 'wavelet'
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wavelet_type: 'morl' # Morlet wavelets are useful for time-frequency analysis due to their Gaussian-modulated sinusoid shape.
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length: null # Use full input size if set to null. (Computationally expensive)
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wavelet_type: 'cgau1' # Complex Gaussian wavelets are used for complex-valued signal analysis and capturing phase information.
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length: null # Use full input size if set to null.
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latent_projector:
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type: 'fc' # Type of latent projector: 'fc', 'rnn', 'fourier'
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@ -81,7 +78,7 @@ profiler:
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---
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name: DEFAULT
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project: Spikey_2
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project: Spikey_3
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slurm:
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name: 'Spikey_{config[name]}'
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@ -138,239 +135,19 @@ training:
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eval_freq: 8
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save_path: models
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peer_gradients_factor: 0.25
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value_scale: 1000
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value_scale: 1
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device: cpu
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middle_out:
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residual: false
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---
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name: FC
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name: FC_smol_master6
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import: $
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feature_extractor:
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input size: 10
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input_size: 195
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transforms:
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- type: 'identity'
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latent_projector:
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type: fc
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input_size: 1953
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latent_size: 4
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layer_shapes: [32, 8]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 3
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 1024
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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---
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name: FC_AblLR
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import: $
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latent_projector:
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type: fc
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input_size: 1953
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latent_size: 4
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layer_shapes: [32, 8]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 3
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 1024
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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grid:
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training.learning_rate: [0.1, 0.01, 0.001, 0.0001]
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---
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name: RNN
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import: $
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latent_projector:
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type: rnn
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input_size: 1953
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latent_size: 4
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rnn_hidden_size: 3
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rnn_num_layers: 2
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middle_out:
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region_latent_size: 4
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num_peers: 3
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 1024
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batch_size: 32
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num_batches: 2
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learning_rate: 0.01
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---
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name: FOURIER
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import: $
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latent_projector:
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type: fourier
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input_size: 19531 # 1s
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latent_size: 8
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layer_shapes: [32, 8]
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activations: ['ReLU', 'ReLU']
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pass_raw_len: 1953 # 0.1s
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middle_out:
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region_latent_size: 8
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num_peers: 3
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 1024
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batch_size: 32
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num_batches: 16
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learning_rate: 0.01
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---
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name: FC_AblPeerGrad # Best: 0.33
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import: $
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latent_projector:
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type: fc
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input_size: 1953
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latent_size: 4
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layer_shapes: [32, 8]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 2
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 1024
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batch_size: 16
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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grid:
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training:
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peer_gradients_factor: [0.0, 0.1, 0.25, 0.33, 0.5, 1.0]
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---
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name: FC_NoPeer # Worse
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import: $
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latent_projector:
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type: fc
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input_size: 1953
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latent_size: 4
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layer_shapes: [32, 8]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 0
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 1024
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batch_size: 16
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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---
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name: FC_ScaleAbl # Best: 1000
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import: $
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latent_projector:
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type: fc
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input_size: 1953
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latent_size: 4
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layer_shapes: [32, 8]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 3
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 1024
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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grid:
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training.value_scale: [1, 100, 1000, 10000]
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---
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name: FC_BSAbl3 # 64 is best, everything >=64 is ok
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import: $
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latent_projector:
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type: fc
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input_size: 1953
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latent_size: 4
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layer_shapes: [32, 8]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 3
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 1024
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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grid:
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training.batch_size: [64, 128, 256]
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---
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name: FC_smol_master2
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import: $
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- type: 'identity' # Pass the last n samples of the input data directly.
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scheduler:
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reps_per_version: 8
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@ -378,7 +155,45 @@ scheduler:
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latent_projector:
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type: fc
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input_size: 195
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latent_size: 6
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layer_shapes: [20, 6]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 6
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num_peers: 3
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residual: true
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predictor:
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layer_shapes: [3]
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activations: ['ReLU']
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training:
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epochs: 10000
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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eval_freq: 16
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---
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name: Smol_Feat_fourier
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import: $
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feature_extractor:
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input_size: 1953 # (=0.1s)
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transforms:
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- type: fourier
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#- type: 'wavelet'
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# wavelet_type: 'haar' # 'db1' # 'sym2', 'coif1', 'bior1.3', 'rbio1.3', 'dmey', 'morl', 'haar', 'cgau1'
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- type: identity
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length: 195
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scheduler:
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reps_per_version: 1
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agents_per_job: 1
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latent_projector:
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type: fc
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latent_size: 4
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layer_shapes: [20, 6]
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activations: ['ReLU', 'ReLU']
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@ -399,15 +214,26 @@ training:
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learning_rate: 0.01
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device: cpu
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---
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name: FC_smolTanh
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name: Smol_Feat_db1_1
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import: $
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feature_extractor:
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input_size: 1953 # (=0.1s)
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transforms:
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- type: 'wavelet'
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wavelet_type: 'db1' # 'sym2', 'coif1', 'bior1.3', 'rbio1.3', 'dmey', 'morl', 'haar', 'cgau1'
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- type: identity
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length: 195
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scheduler:
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reps_per_version: 1
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agents_per_job: 1
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latent_projector:
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type: fc
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input_size: 195
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latent_size: 4
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layer_shapes: [20, 6]
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activations: ['Tanh', 'Tanh']
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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@ -416,51 +242,183 @@ middle_out:
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predictor:
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layer_shapes: [2]
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activations: ['Tanh']
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activations: ['ReLU']
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training:
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epochs: 1024
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epochs: 10000
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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---
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name: FOURIER_thin
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name: Smol_Feat_sym2_1
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import: $
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feature_extractor:
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input_size: 1953 # (=0.1s)
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transforms:
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- type: 'wavelet'
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wavelet_type: 'sym2'
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- type: identity
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length: 195
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scheduler:
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reps_per_version: 1
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agents_per_job: 1
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latent_projector:
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type: fourier
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input_size: 1953 # 0.1s
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type: fc
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latent_size: 4
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layer_shapes: [32, 8]
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layer_shapes: [20, 6]
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activations: ['ReLU', 'ReLU']
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pass_raw_len: 195 # 0.01s
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middle_out:
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region_latent_size: 4
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num_peers: 3
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num_peers: 2
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residual: true
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predictor:
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layer_shapes: [3]
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layer_shapes: [2]
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activations: ['ReLU']
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training:
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epochs: 1024
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epochs: 10000
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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---
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name: FOURIER_thicc
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name: Smol_Feat_coif1_1
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import: $
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feature_extractor:
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input_size: 1953 # (=0.1s)
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transforms:
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- type: 'wavelet'
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wavelet_type: 'coif1'
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- type: identity
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length: 195
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scheduler:
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reps_per_version: 1
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agents_per_job: 1
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latent_projector:
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type: fourier
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input_size: 1953 # 0.1s
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latent_size: 8
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layer_shapes: [32, 8]
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type: fc
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latent_size: 4
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layer_shapes: [20, 6]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 2
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residual: true
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predictor:
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layer_shapes: [2]
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activations: ['ReLU']
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training:
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epochs: 10000
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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---
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name: Smol_Feat_haar_1
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import: $
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feature_extractor:
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input_size: 1953 # (=0.1s)
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transforms:
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- type: 'wavelet'
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wavelet_type: 'haar'
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- type: identity
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length: 195
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scheduler:
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reps_per_version: 1
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agents_per_job: 1
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latent_projector:
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type: fc
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latent_size: 4
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layer_shapes: [20, 6]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 2
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residual: true
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predictor:
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layer_shapes: [2]
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activations: ['ReLU']
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training:
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epochs: 10000
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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---
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name: Smol_Feat_dmey_1
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import: $
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feature_extractor:
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input_size: 1953 # (=0.1s)
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transforms:
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- type: 'wavelet'
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wavelet_type: 'dmey'
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- type: identity
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length: 195
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scheduler:
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reps_per_version: 1
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agents_per_job: 1
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latent_projector:
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type: fc
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latent_size: 4
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layer_shapes: [20, 6]
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activations: ['ReLU', 'ReLU']
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middle_out:
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region_latent_size: 4
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num_peers: 2
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residual: true
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predictor:
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layer_shapes: [2]
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activations: ['ReLU']
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training:
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epochs: 10000
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batch_size: 32
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num_batches: 1
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learning_rate: 0.01
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device: cpu
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---
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name: Proto_1
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import: $
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feature_extractor:
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input_size: 1953 # (=0.1s)
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transforms:
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- type: 'wavelet'
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wavelet_type: 'dmey'
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- type: identity
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length: 195
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scheduler:
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reps_per_version: 8
|
||||
agents_per_job: 8
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
latent_size: 8
|
||||
layer_shapes: [24, 12]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
pass_raw_len: 195 # 0.01s
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 8
|
||||
@ -472,120 +430,43 @@ predictor:
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 1024
|
||||
epochs: 10000
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
device: cpu
|
||||
|
||||
evaluation:
|
||||
full_compression: true
|
||||
---
|
||||
name: FC_master3
|
||||
name: Proto_2
|
||||
import: $
|
||||
|
||||
feature_extractor:
|
||||
input_size: 1953 # (=0.1s)
|
||||
transforms:
|
||||
- type: 'wavelet'
|
||||
wavelet_type: 'dmey'
|
||||
- type: identity
|
||||
length: 195
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 8
|
||||
agents_per_job: 8
|
||||
reps_per_version: 4
|
||||
agents_per_job: 4
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
input_size: 1953
|
||||
latent_size: 4
|
||||
layer_shapes: [32, 8]
|
||||
latent_size: 8
|
||||
layer_shapes: [24, 12]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 4
|
||||
region_latent_size: 8
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [3]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 1024
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
---
|
||||
name: FC_master_single
|
||||
import: $
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 1
|
||||
agents_per_job: 1
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
input_size: 1953
|
||||
latent_size: 4
|
||||
layer_shapes: [32, 8]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 4
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [3]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 1024
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
---
|
||||
name: debug
|
||||
import: $
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 1
|
||||
agents_per_job: 1
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
input_size: 1953
|
||||
latent_size: 4
|
||||
layer_shapes: [32, 8]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 4
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [3]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 1024
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
---
|
||||
name: FOURIER_smol_master
|
||||
import: $
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 8
|
||||
agents_per_job: 8
|
||||
|
||||
latent_projector:
|
||||
type: fourier
|
||||
input_size: 195
|
||||
latent_size: 4
|
||||
layer_shapes: [20, 6]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
pass_raw_len: 20 # 0.001s
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 4
|
||||
num_peers: 2
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [2]
|
||||
layer_shapes: [4]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
@ -593,4 +474,260 @@ training:
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
device: cpu
|
||||
device: cpu
|
||||
|
||||
bitstream_encoding:
|
||||
type: rice
|
||||
|
||||
evaluation:
|
||||
full_compression: true
|
||||
---
|
||||
name: Proto_Light_0
|
||||
import: $
|
||||
|
||||
feature_extractor:
|
||||
input_size: 1953 # (=0.1s)
|
||||
transforms:
|
||||
- type: identity
|
||||
length: 195
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 8
|
||||
agents_per_job: 8
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
latent_size: 8
|
||||
layer_shapes: [24, 12]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 8
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [4]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 10000
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
device: cpu
|
||||
---
|
||||
name: Proto_3_Light_SmolInp
|
||||
import: $
|
||||
|
||||
feature_extractor:
|
||||
input_size: 1953 # (=0.1s)
|
||||
transforms:
|
||||
- type: identity
|
||||
length: 19
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 2
|
||||
agents_per_job: 2
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
latent_size: 8
|
||||
layer_shapes: [24, 12]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 8
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [4]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 10000
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
device: cpu
|
||||
|
||||
bitstream_encoding:
|
||||
type: rice
|
||||
|
||||
evaluation:
|
||||
full_compression: true
|
||||
---
|
||||
name: Proto_3_Light_HugeInp
|
||||
import: $
|
||||
|
||||
feature_extractor:
|
||||
input_size: 1953 # (=0.1s)
|
||||
transforms:
|
||||
- type: identity
|
||||
length: 1953
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 2
|
||||
agents_per_job: 2
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
latent_size: 8
|
||||
layer_shapes: [24, 12]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 8
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [4]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 10000
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
device: cpu
|
||||
|
||||
bitstream_encoding:
|
||||
type: rice
|
||||
k: 2
|
||||
|
||||
evaluation:
|
||||
full_compression: true
|
||||
---
|
||||
name: Proto_3_Smol
|
||||
import: $
|
||||
|
||||
feature_extractor:
|
||||
input_size: 195 # (=0.01s)
|
||||
transforms:
|
||||
- type: 'wavelet'
|
||||
wavelet_type: 'dmey'
|
||||
- type: identity
|
||||
length: 19
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 2
|
||||
agents_per_job: 2
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
latent_size: 8
|
||||
layer_shapes: [24, 12]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 8
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [4]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 10000
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
device: cpu
|
||||
|
||||
bitstream_encoding:
|
||||
type: rice
|
||||
k: 2
|
||||
|
||||
evaluation:
|
||||
full_compression: true
|
||||
---
|
||||
name: Proto_2_k2
|
||||
import: $
|
||||
|
||||
feature_extractor:
|
||||
input_size: 1953 # (=0.1s)
|
||||
transforms:
|
||||
- type: 'wavelet'
|
||||
wavelet_type: 'dmey'
|
||||
- type: identity
|
||||
length: 195
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 2
|
||||
agents_per_job: 2
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
latent_size: 8
|
||||
layer_shapes: [24, 12]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 8
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [4]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 10000
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
device: cpu
|
||||
|
||||
bitstream_encoding:
|
||||
type: rice
|
||||
k: 2
|
||||
|
||||
evaluation:
|
||||
full_compression: true
|
||||
---
|
||||
name: Proto_2_k4
|
||||
import: $
|
||||
|
||||
feature_extractor:
|
||||
input_size: 1953 # (=0.1s)
|
||||
transforms:
|
||||
- type: 'wavelet'
|
||||
wavelet_type: 'dmey'
|
||||
- type: identity
|
||||
length: 195
|
||||
|
||||
scheduler:
|
||||
reps_per_version: 2
|
||||
agents_per_job: 2
|
||||
|
||||
latent_projector:
|
||||
type: fc
|
||||
latent_size: 8
|
||||
layer_shapes: [24, 12]
|
||||
activations: ['ReLU', 'ReLU']
|
||||
|
||||
middle_out:
|
||||
region_latent_size: 8
|
||||
num_peers: 3
|
||||
residual: true
|
||||
|
||||
predictor:
|
||||
layer_shapes: [4]
|
||||
activations: ['ReLU']
|
||||
|
||||
training:
|
||||
epochs: 10000
|
||||
batch_size: 32
|
||||
num_batches: 1
|
||||
learning_rate: 0.01
|
||||
device: cpu
|
||||
|
||||
bitstream_encoding:
|
||||
type: rice
|
||||
k: 4
|
||||
|
||||
evaluation:
|
||||
full_compression: true
|
||||
Loading…
Reference in New Issue
Block a user