default_params.yaml

# Default configuration

# Network architecture
num_inputs: 1  # size of net input
num_outputs: 1  # size of net output
num_units: [2, 4, 4]  # (hidden only) layer-wise number of neurons
num_sto: [0, 2, 2]  # (hidden only) layer-wise number of stochastic neurons
variance: .05 # 'in' (fed as input), 'out' (as network outputs), or float (isotropic value)
no_bias: False  # no bias terms
num_mems: [0, 0, 0]  # RNN only
rnn_steps: 1  # RNN only

# training parameters
n_epochs: 10
opt_method: adam  # adam, rmsprop
step_size: .01

# training logistics
size_sample: 30  # #times to sample the network per data pair
size_batch: 1  # #data pairs for each gradient estimate
weight_decay: 0.  # weight decay (0 means none)
init_theta:
  distr: XavierNormal  # initialization
  params:
    scale: 1.
#snapshot: "DYNAMICALLY SET"  # load from previously trained model
#dump_path: "DYNAMICALLY SET"  # path to dump snapshot for restore

# debugging
debug: True
dbg_out_full: True
dbg_plot_charts: True
dbg_plot_samples:
  plot: True
  batch: 100
  x_dim: 0
  y_dim: 0