def on_plot_save(self, epoch, iter, logs={}):
# import neuron sandbox
# has to be here, can't be at the top, due to cyclical imports (??)
# TODO: should just pass the function to compute the figures given the model and generator
import neuron.sandbox as nrn_sandbox
reload(nrn_sandbox)
with timer.Timer('plot callback', self.verbose):
if len(self.run.grid_size) == 3:
collapse_2d = [0, 1, 2]
else:
collapse_2d = [2]
exampl = nrn_sandbox.show_example_prediction_result(self.model,
self.generator,
self.run,
self.data,
test_batch_size=1,
test_model_names=None,
test_grid_size=self.run.grid_size,
ccmap=None,
collapse_2d=collapse_2d,
slice_nr=None,
plt_width=17,
verbose=self.verbose)
# save, then close
figs = exampl[1:]
for idx, fig in enumerate(figs):
dirn = "dirn_%d" % idx
slice_nr = 0
filename = self.savefilepath.format(epoch=epoch, iter=iter, axis=dirn, slice_nr=slice_nr)
fig.savefig(filename)
plt.close()
def on_plot_save(self, epoch, iter, logs={}):
# import neuron sandbox
# has to be here, can't be at the top, due to cyclical imports (??)
# TODO: should just pass the function to compute the figures given the model and generator
import neuron.sandbox as nrn_sandbox
reload(nrn_sandbox)
with timer.Timer('plot callback', self.verbose):
if len(self.run.grid_size) == 3:
collapse_2d = [0, 1, 2]
else:
collapse_2d = [2]
exampl = nrn_sandbox.show_example_prediction_result(self.model,
self.generator,
self.run,
self.data,
test_batch_size=1,
test_model_names=None,
test_grid_size=self.run.grid_size,
ccmap=None,
collapse_2d=collapse_2d,
slice_nr=None,
plt_width=17,
verbose=self.verbose)
# save, then close
figs = exampl[1:]
for idx, fig in enumerate(figs):
dirn = "dirn_%d" % idx
slice_nr = 0
filename = self.savefilepath.format(epoch=epoch, iter=iter, axis=dirn, slice_nr=slice_nr)
fig.savefig(filename)
plt.close()