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83a9309 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # ===================================================================
# Diffusion Model Configuration for Multi-System Pooled Embeddings
# ===================================================================
# --- Execution Target ---
# Specify which system's embeddings to train the diffusion model on.
# This corresponds to the group name in the HDF5 file (e.g., 0 for 'system_0').
system_to_train: 0
# --- Data Paths ---
# Path to the HDF5 file containing the pooled embeddings from the multi-system ChebNet script.
h5_file_path: "latent_reps/pooled_embeddings.h5"
# Template for the group name within the HDF5 file. The script will replace '{}' with 'system_to_train'.
dataset_group_key_template: "system_{}"
# Name of the dataset within each group.
dataset_name_in_group: "embeddings"
# Directory to save generated embeddings and checkpoints.
output_dir: "diffusion_output"
# --- Run Mode & Grid Search ---
# 'user_defined': Runs a single experiment with the parameters in the 'parameters' section.
# 'grid_search': Runs a series of experiments defined in 'grid_search_space'.
run_mode: "grid_search"
# --- Parameters for a single run (if run_mode is 'user_defined') ---
parameters:
num_epochs: 50
batch_size: 64
learning_rate: 1e-5
num_gen: 5000
save_interval: 1000
model_type: "mlp_v2"
hidden_dim: 1024
scheduler: "linear"
diffusion_steps: 1400
beta_start: 5e-6
beta_end: 0.03
num_instances: 6 # For partitioning grid search experiments
# --- Hyperparameter space for grid search (if run_mode is 'grid_search') ---
grid_search_space:
# These parameters will be fixed across all grid search experiments
# To vary them, you would need to add them to the 'curated_experiments' in the script.
learning_rate: 1e-5
num_epochs: 50
model_type: "mlp_v2"
hidden_dim: 1024
# The script will generate a curated grid based on these values.
# This section is for reference; the actual grid is built in the python script.
example_beta_starts: [5e-6, 0.005]
example_beta_ends: [0.03, 0.1]
example_diffusion_steps: [500, 1400]
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