def __init__(self,
samplefile,
output_file,
use_event=0,
afternbatches=-1,
on_epoch_end=False):
self.callback = PredictCallback(
samplefile=samplefile,
function_to_apply=self.make_plot, #needs to be function(counter,[model_input], [predict_output], [truth])
after_n_batches=afternbatches,
on_epoch_end=on_epoch_end,
use_event=-1,
decay_function=None)
def __init__(self,
samplefile,
output_file,
use_event=0,
x_index=5,
y_index=6,
z_index=7,
e_index=0,
cut_z=None,
plotter=None,
plotfunc=None,
afternbatches=-1,
on_epoch_end=True,
decay_function=None):
self.x_index = x_index
self.y_index = y_index
self.z_index = z_index
self.e_index = e_index
self.cut_z = cut_z
if self.cut_z is not None:
if 'pos' in self.cut_z:
self.cut_z = 1.
elif 'neg' in self.cut_z:
self.cut_z = -1.
self.decay_function = decay_function
self.callback = PredictCallback(
samplefile=samplefile,
function_to_apply=self.
make_plot, #needs to be function(counter,[model_input], [predict_output], [truth])
after_n_batches=afternbatches,
on_epoch_end=on_epoch_end,
use_event=use_event,
decay_function=self.decay_function)
self.output_file = output_file
if plotter is not None:
self.plotter = plotter
else:
self.plotter = plotter_fraction_colors(output_file=output_file)
self.plotter.gray_noise = False
if plotfunc is not None:
self.plotfunc = plotfunc
else:
self.plotfunc = None
train.compileModel(learningrate=0.001,
loss=['mean_squared_error'],)
#metrics=usemetrics)
print(train.keras_model.summary())
#exit()
from tools import offset_plotter
pltr = offset_plotter(train,relative=True)
from DeepJetCore.training.DeepJet_callbacks import PredictCallback
predcb=PredictCallback(samplefile=train.val_data.getSamplePath(train.val_data.samples[0]),
function_to_apply=pltr.make_plot,
after_n_batches=-1, #1000,
batchsize=10000,
on_epoch_end=True,
use_event=-1)
from training_scheduler import scheduled_training, Learning_sched
learn=[]
learn.append(Learning_sched(lr=1e-4,
nepochs=1,
batchsize=256,#128
#loss=[binned_global_correction_loss_random]))
loss=[huber_loss_calo]))
learn.append(Learning_sched(lr=1e-4,
nepochs=19,