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experiment.yml
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experiment.yml
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# Experiment config, this overwrites the defaults.
output:
# Path for the main output directory
output_dir: ./outputs
# Add extra text to the output folder. No deactivates it
extra_text: no
# Enable logging with tensorboard
tensorboard: yes
# Enable printing
verbose: yes
# Redirects stdout to file output.txt
to_file: no
optimization:
# Input batch size for training and validation
batch_size: 100
# Number of epochs for training
epochs: 2000
# learning rate
lr: 0.0003
# Grace period for early stopping
grace_early_stopping: 50
# Number of warmup epochs
warmup: 200
experiment:
# Size of the latent space
latent_size: 50
# Type of VAE to use
# Possible options: standard, copula, diagonal, copulaV2
type_vae: standard
# Type of architecture ot be used.
# Possible options: shallow, deep, conv
architecture: shallow
# Type of marginals that should we used in the architecture. It is ignored if not required
# Possible options: gaussian, laplace, log_norm, cauchy, exp'
marginals: gaussian
# Number of samples used for approximating the log-likelihood
samples_ll: 5
dataset:
# Name of the database to load
# Possible options: binary_mnist, mnist, bedrooms, omniglot, cifar10, fashionmnist, dSprites
dataset_name: fashionmnist
# Allow dynamic binarization. Ignored if it does not make sense
dynamic_binarization: yes
# Shuffle the dataset
shuffle: no
checkpointing:
# Every how many epochs do I checkpoint
# 0 means that we checkpoint every 5% of number of epochs
# This is ignored if checkpointing is false
frequency_checkpoints: 0