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investment_euler_defaults.yaml
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investment_euler_defaults.yaml
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trainer:
accelerator: cpu
max_epochs: 100
min_epochs: 0
max_time: 00:00:15:00
precision: 32
num_sanity_val_steps: 0
logger:
class_path: pytorch_lightning.loggers.WandbLogger
init_args:
offline: true # set to true to not upload during testing
log_model: false # set to true to save the model at the end
name: null # can set name or have it automatically generated
project: symmetry_examples
group: null # can group related runs
tags:
- basic_example
callbacks:
- class_path: pytorch_lightning.callbacks.LearningRateMonitor
init_args:
logging_interval: step
log_momentum: false
- class_path: pytorch_lightning.callbacks.ModelCheckpoint
init_args:
filename: best
monitor: val_loss
verbose: false
save_last: true
save_top_k: 1
save_weights_only: true
mode: min
auto_insert_metric_name: true
- class_path: pytorch_lightning.callbacks.EarlyStopping
init_args:
monitor: val_loss
min_delta: 0.0
patience: 50
mode: min
check_finite: true
divergence_threshold: 100000 # stops if larger
stopping_threshold: 1.0e-6 # typically the binding stopping threshold
optimizer:
class_path: torch.optim.Adam
init_args:
lr: 1.0e-3
# Scheduler currently not tuned
lr_scheduler:
class_path: torch.optim.lr_scheduler.StepLR
init_args:
step_size: 50 # number of epochs
gamma: 0.8
# lr_scheduler:
# class_path: torch.optim.lr_scheduler.ReduceLROnPlateau
# init_args:
# factor: 0.1
# mode: min
# patience: 5
model:
# Model parameters
N: 128
alpha_0: 1.0
alpha_1: 1.0
beta: 0.95
gamma: 90.0
sigma: 0.005
delta: 0.05
eta: 0.001
nu: 1.0
# Settings for output
verbose: false
hpo_objective_name: test_loss
always_log_hpo_objective: false
print_metrics: false
save_metrics: false
save_test_results: false
test_seed: 0 # set to 0 to use default RNG seed
X_0_seed: 0 # set to 0 to use default RNG seed
check_transversality: true
# if checking transversality for nu != 1, sees divergence of the test mean(X) from iteration. For small T these can be initial condition sensitive
transversality_X_mean_min: 0.1
transversality_X_mean_max: 10.0
transversality_u_rel_error: 0.2 # if nu = 1, checks relative error of test. defaults to 20% deviation
test_loss_success_threshold: 1e-4 # typically failures of overfitting are far above this
# Settings for method
omega_quadrature_nodes: 7
normalize_shock_vector: true
reset_trajectories_frequency: 0 # in epochs, 0 to never reset
train_trajectories: 16
train_subsample_trajectories: 0 # if > 0 will randomly sample from the train trajectories datapoints, otherwise uses all
val_trajectories: 8
test_trajectories: 32
batch_size: 16 # set to 0 for full dataset
shuffle_training: true
T: 63
X_0_loc: 0.9
X_0_scale: 0.05
# # Settings for deep sets neural networks
ml_model:
class_path: econ_layers.layers.DeepSet
init_args:
n_in: 1
n_out: 1
L: 4
phi_layers: 1
phi_hidden_dim: 128
phi_hidden_bias: false
phi_last_bias: true
phi_activator:
class_path: torch.nn.ReLU
rho_layers: 4
rho_hidden_dim: 256
rho_hidden_bias: true
rho_last_bias: true
rho_activator:
class_path: torch.nn.ReLU
# Can also change on CLI such as
#python investment_euler.py --model.ml_model.class_path=econ_layers.layers.DeepSetMoments --model.ml_model.L=4 --model.ml_model.n_in=1 --model.ml_model.n_out=1 --model.ml_model.rho_layers=3 --model.ml_model.rho_hidden_dim=256 --model.ml_model.rho_hidden_bias=false --model.ml_model.rho_last_bias=true