# Network details
# ------------------------------------------------
network_type = nn        # Perform a fit using a neural network
hidden=  20              # 1 hidden layer of 20 nodes
input= structure         # Use the atomic configuration as input
output = energy          # Use total energy as output
transfer = tanh          # Use a tanh transfer funtion for all nodes


# Specify the geometry mapping functions
# ------------------------------------------------
Nradial=5
Nangular=4
Rcut=4


# Training details
# -------------------------------------------------
algorithm=rprop              # Use the default Rprop training algorithm
Niterations = 300            # Stop training after 300 iterations
threshold = 0                # Don't stop on the basis of the gradient
Nprint = 10                  # Print fitting information every 10 steps
checkpoint_in = potential    # We ARE using a previously trained potential
checkpoint_out = potential   # Save the potential using the base name "potential"
Ncheckpoint = 100            # Save the potential every 100 steps

# specify the data
# --------------------------------------------------
include predict_data        # Data are specified in the "training_data" file




# FOR TUTORIAL PURPOSES ONLY
# --------------------------------------------------
seed = 135492                # We set the random number seed here for purposes
                             # of the tutorial only. It ensures we all
                             # get the same numbers.  IT IS NOT GENERALLY 
                             # NECESSARY TO SET THIS. 
