def p1b1_parameter_set():
"""Utility function to encapsulate ParameterSet definition"""
ps = prs.ParameterSet()
ps["activation"] = prs.DiscreteParameter(activation)
ps["batch_size"] = prs.NumericListParameter(batch_size)
ps["dense"] = prs.DiscreteParameter(dense)
ps["drop"] = prs.NumericParameter(0.0, 0.9)
ps["epochs"] = prs.IntegerParameter(10, 20) #100, 200)
ps["latent_dim"] = prs.NumericListParameter(latent_dim)
ps["learning_rate"] = prs.NumericParameter(0.00001, 0.1)
ps["model"] = prs.DiscreteParameter(model)
ps["optimizer"] = prs.DiscreteParameter(optimizer)
ps["residual"] = prs.DiscreteParameter(residual)
ps["reduce_lr"] = prs.DiscreteParameter(reduce_lr)
ps["warmup_lr"] = prs.DiscreteParameter(warmup_lr)
# # switching batch_size to NumericList to enforce integer validation
# ps.add(prs.DiscreteParameter("activation", activation))
# ps.add(prs.NumericListParameter("batch_size", batch_size))
# ps.add(prs.DiscreteParameter("dense", dense))
# ps.add(prs.NumericParameter("drop", 0.0, 0.9))
# ps.add(prs.IntegerParameter("epochs", 10, 20)) #100, 200))
# ps.add(prs.NumericListParameter("latent_dim", latent_dim))
# ps.add(prs.NumericParameter("learning_rate", 0.00001, 0.1))
# ps.add(prs.DiscreteParameter("model", model))
# ps.add(prs.DiscreteParameter("optimizer", optimizer))
# ps.add(prs.DiscreteParameter("residual", residual))
# ps.add(prs.DiscreteParameter("reduce_lr", reduce_lr))
# ps.add(prs.DiscreteParameter("warmup_lr", warmup_lr))
return ps
def p3b1_parameter_set():
"""Utility function to encapsulate ParameterSet definition"""
ps = prs.ParameterSet()
# switching batch_size to NumericList to enforce integer validation
#ps.add(prs.DiscreteParameter("batch_size", batch_size))
ps.add(prs.NumericListParameter("batch_size", batch_size))
ps.add(prs.IntegerParameter("epochs", 5, 50))
#ps.add(prs.DiscreteParameter("activation", activation))
#ps.add(prs.DiscreteParameter("optimizer", optimizer))
ps.add(prs.NumericParameter("dropout", 0.0, 0.9))
ps.add(prs.NumericParameter("learning_rate", 0.00001, 0.1))
ps.add(prs.DiscreteParameter("shared_nnet_spec", shared_nnet_spec))
ps.add(prs.DiscreteParameter("ind_nnet_spec", ind_nnet_spec))
return ps
def p1b1_parameter_set():
"""Utility function to encapsulate ParameterSet definition"""
ps = prs.ParameterSet()
# batch_size is NumericList to enforce integer validation
ps["activation"] = prs.DiscreteParameter(activation)
ps["batch_size"] = prs.NumericListParameter(batch_size)
ps["dense"] = prs.DiscreteParameter(dense)
ps["drop"] = prs.NumericParameter(0.0, 0.9)
# limit maximum number of epcohs for demonstration purposes
ps["epochs"] = prs.IntegerParameter(10, 20) #100, 200)
ps["latent_dim"] = prs.NumericListParameter(latent_dim)
ps["learning_rate"] = prs.NumericParameter(0.00001, 0.1)
ps["model"] = prs.DiscreteParameter(model)
ps["optimizer"] = prs.DiscreteParameter(optimizer)
ps["residual"] = prs.DiscreteParameter(residual)
ps["reduce_lr"] = prs.DiscreteParameter(reduce_lr)
ps["warmup_lr"] = prs.DiscreteParameter(warmup_lr)
return ps
dense = [[500, 100, 50], [1000, 500, 100, 50], [2000, 1000, 500, 100, 50],
[2000, 1000, 1000, 500, 100, 50],
[2000, 1000, 1000, 1000, 500, 100, 50]]
#optimizer = ["adam", "sgd", "rmsprop", "adagrad", "adadelta","adamax","nadam"]
conv = [[50, 50, 50, 50, 50, 1], [25, 25, 25, 25, 25, 1],
[64, 32, 16, 32, 64, 1], [100, 100, 100, 100, 100, 1],
[32, 20, 16, 32, 10, 1]]
ps = prs.ParameterSet()
ps.add(prs.DiscreteParameter("batch_size", batch_size))
ps.add(prs.IntegerParameter("epochs", 5, 100))
#ps.add(prs.DiscreteParameter("activation", activation))
ps.add(prs.DiscreteParameter("dense", dense))
#ps.add(prs.DiscreteParameter("optimizer", optimizer))
ps.add(prs.NumericParameter("drop", 0.0, 0.9))
ps.add(prs.NumericParameter("learning_rate", 0.00001, 0.1))
ps.add(prs.DiscreteParameter("conv", conv))
# TODO: since dense and conv will be dummy-coded, ensure that all possible
# category values are present in the parameter set
# =============================================================================
# DATA
# =============================================================================
# TODO: relocate pdtypes to nt3_run_data
# coerce data into correct types in dataframe
float64 = 'float64'
int64 = 'int64'
pdtypes = {
'batch_size': int64,
[2000, 1000, 1000, 1000, 500, 100, 50]]
optimizer = [
"adam", "sgd", "rmsprop", "adagrad", "adadelta", "adamax", "nadam"
]
conv = [[50, 50, 50, 50, 50, 1], [25, 25, 25, 25, 25, 1],
[64, 32, 16, 32, 64, 1], [100, 100, 100, 100, 100, 1],
[32, 20, 16, 32, 10, 1]]
ps = prs.ParameterSet()
ps["batch_size"] = prs.DiscreteParameter(batch_size)
ps["epochs"] = prs.IntegerParameter(5, 500)
ps["activation"] = prs.DiscreteParameter(activation)
ps["dense"] = prs.DiscreteParameter(dense)
ps["optimizer"] = prs.DiscreteParameter(optimizer)
ps["drop"] = prs.NumericParameter(0.0, 0.9)
ps["learning_rate"] = prs.NumericParameter(0.00001, 0.1)
ps["conv"] = prs.DiscreteParameter(conv)
print(ps)
# =============================================================================
# Add run_id and subdirectory of /save for logged output.
# Ensure that all parameters are populated with default values
# Any last-minute or ad hoc changes can be added here
# =============================================================================
def param_update(params, default_params, run_id, subdirectory='exp'):
run_params = default_params.copy()
run_params.update(params)
run_params['save'] = 'save/{}'.format(subdirectory)