def run_example(k):
# ----------------- Preprocessing --------------------
# data_arrays format: [vg, vd, ids]
data_arrays = parser.read_dc_iv_mdm('./HEMT_bo/Id_vs_Vd_at_Vg.mdm')
data_arrays_test = parser.read_dc_iv_mdm('./HEMT_bo/Id_vs_Vg_at_Vd.mdm')
#data_arrays = preproc.truncate(data_arrays, (-2, -1.2), 0)
vg, vd, ids = data_arrays[0], data_arrays[1], data_arrays[2]
vgtest, vdtest, idstest = data_arrays_test[0], data_arrays_test[
1], data_arrays_test[2]
# plot_iv(vg, vd, ids)
# quit()
scale, vg_shift = preproc.compute_dc_meta(*data_arrays)
preproc_param = {
'scale': scale,
'vg_shift': vg_shift,
}
np.random.seed = 42
permu = np.random.permutation(len(data_arrays[0]))
data_arrays = [e[permu] for e in data_arrays]
data_arrays_eval = [e[0:100] for e in data_arrays]
data_arrays_train = [e[100:] for e in data_arrays]
# ----------------- Train + Eval ---------------------
dc_model = DCModel(path + str(k) + 'HEMT_DC_2_L1_Weighted')
dc_model.add_data('train', data_arrays_train, preproc_param)
dc_model.add_data('eval', data_arrays_eval, preproc_param)
# plot_iv(*dc_model.preproc_data_arrays)
dc_model.build_nets(
hidden_sig_dims=[16, 1],
hidden_tanh_dims=[16, 1],
train_batch_size=461,
eval_batch_size=100,
weight_optim_method='AdaGrad',
weight_optim_param={
'alpha': k,
'epsilon': 1e-4
},
bias_optim_method='AdaGrad',
bias_optim_param={
'alpha': k,
'epsilon': 1e-4
},
max_loss_scale=7e5, # to improve subthreshold modeling accuracy
)
start = time.time()
dc_model.train_with_eval(num_epoch=int(1e5),
report_interval=1000,
eval_during_training=True)
end = time.time()
print('Elapsed time for alpha =' + str(k) + ':' + str(end - start))
# # ----------------- Inspection ---------------------
# dc_model.draw_nets()
dc_model.plot_loss_trend()
plt.savefig(path + str(k) + "trend.png")
plt.close()
# # ----------------- Deployment ---------------------
# _, pred_ids = dc_model.predict_ids(vg, vd)
# plot_iv(
# vg, vd, ids,
# vg_comp=vg,
# vd_comp=vd,
# ids_comp=pred_ids,
# save_name = 'saved_plot/'
# )
# _, pred_ids = dc_model.predict_ids(vg, vd)
# plot_iv(
# vd, vg, ids,
# vg_comp=vd,
# vd_comp=vg,
# ids_comp=pred_ids,
# )
# _, pred_ids = dc_model.predict_ids(vg, vd)
# plot_iv(
# vg, vd, ids,
# vg_comp=vg,
# vd_comp=vd,
# ids_comp=pred_ids,
# )
dc_model.save_loss_trend(path + str(k) + "trend.csv")
epoch, train_loss, eval_loss, train_l1_metric, eval_l1_metric, train_scaled_l1_metric, eval_scaled_l1_metric = dc_model.save_loss(
)
return epoch, train_loss, eval_loss, train_l1_metric, eval_l1_metric, train_scaled_l1_metric, eval_scaled_l1_metric
from dc_iv_api import DCModel, plot_iv, predict_ids
import pinn.parser as parser
import pinn.preproc as preproc
import pinn.exporter as exporter
import numpy as np
# ----------------- Preprocessing --------------------
# data_arrays format: [vg, vd, ids]
data_arrays = parser.read_dc_iv_mdm('./HEMT_bo/Id_vs_Vd_at_Vg.mdm')
data_arrays_test = parser.read_dc_iv_mdm('./HEMT_bo/Id_vs_Vg_at_Vd.mdm')
#data_arrays = preproc.truncate(data_arrays, (-2, -1.2), 0)
vg, vd, ids = data_arrays[0], data_arrays[1], data_arrays[2]
vgtest, vdtest, idstest = data_arrays_test[0], data_arrays_test[
1], data_arrays_test[2]
# plot_iv(vg, vd, ids)
# quit()
scale, vg_shift = preproc.compute_dc_meta(*data_arrays)
preproc_param = {
'scale': scale,
'vg_shift': vg_shift,
}
np.random.seed = 42
permu = np.random.permutation(len(data_arrays[0]))
data_arrays = [e[permu] for e in data_arrays]
data_arrays_eval = [e[0:100] for e in data_arrays]
data_arrays_train = [e[100:] for e in data_arrays]
# ----------------- Train + Eval ---------------------
dc_model = DCModel('HEMT_DC_2_L1_Weighted')
dc_model.add_data('train', data_arrays_train, preproc_param)
dc_model.add_data('eval', data_arrays_eval, preproc_param)