def main():
directory = "..\\Data\\Training\\2categories\\treshold_4\\"
# Lernkurve:
eval_trainlogfile(directory + "trainphase.log", plot=True)
# DatenSammeln
data, label = generate_classification()
val_data = data[:623]
val_lbl = label[:623]
# NetzLaden
netname = "categorical_crossentropy_hidden-softmax_output-softmax_above"
net, offset = load_last_net(netname, _dir=directory)
assert net is not None
eval_validation_set(val_data, val_lbl, net)
plt.show()
def main():
# Lernkurve:
eval_trainlogfile(
"..\\Data\\Training\\activationHidden-tanh_activationOutput-softmax\\trainphase.log",
plot=True)
# DatenSammeln
data, label = generate_classification()
val_data = data[:623]
val_lbl = label[:623]
# NetzLaden
netname = "categorical_crossentropy_hidden-tanh_output-softmax_above"
net, offset = load_last_net(
netname,
_dir=
"..\\Data\\Training\\activationHidden-tanh_activationOutput-softmax")
assert net is not None
eval_validation_set(val_data, val_lbl, net)
plt.show()
"sum: " + str(int(np.sum(suml[:, :, i]))),
color="white")
plt.imshow(suml[:, :, i], vmax=vmax, vmin=0)
plt.figure("Baseline " + str(i * 5 + 5) + " min")
plt.text(46,
63,
"sum: " + str(int(np.sum(sumb[:, :, i]))),
color="white")
plt.imshow(sumb[:, :, i], vmax=vmax, vmin=0)
return
if __name__ == '__main__':
#Lernkurve:
eval_trainlogfile("./trainphase_MSE.log", plot=True)
#DatenSammeln
s1, s2 = generate_Data_5_7(
"..\\Data\\samplebundles\\{}_5in_7out_64x64_without_border",
only_2004=True)
all_data = None
for bundle in s1:
data, label = bundle.get_all_data_label(channels_Last=True,
flatten_output=True)
if all_data is None:
all_data = data
all_label = label
else:
all_data = np.concatenate((all_data, data), axis=0)
import Data.evaluate_Network_on_realData as eN eN.eval_trainlogfile(logfilepath="./trainphase.log", plot=True)