def main():
""" load training data"""
inputs = np.loadtxt("../handwriting/X2_100samples.dat")
targets = np.loadtxt("../handwriting/y2_100samples.dat")
""" define network topology """
conec = mlgraph((inputs.shape[1], 10, 1))
# reg = 0.1
reg = False
net = ffnet(conec)
system = NNSystem(net, inputs, targets, reg=reg)
database = system.create_database(
# db="/home/ab2111/machine_learning_landscapes/neural_net/db_ffnet_100samples_reg"+str(reg) +".sqlite"
db="../db/db_ffnet_100samples.sqlite")
run_gui(system, database)
def main():
""" load training data"""
inputs = np.loadtxt("../handwriting/X2_100samples.dat")
targets = np.loadtxt("../handwriting/y2_100samples.dat")
""" define network topology """
conec = mlgraph((inputs.shape[1],10,1))
# reg = 0.1
reg=False
net = ffnet(conec)
system = NNSystem(net, inputs, targets, reg=reg)
database = system.create_database(
# db="/home/ab2111/machine_learning_landscapes/neural_net/db_ffnet_100samples_reg"+str(reg) +".sqlite"
db="../db/db_ffnet_100samples.sqlite"
)
run_gui(system, database)
dg.plot()
dg.label_minima(labels)
print labels
plt.show()
# dg.savefig("/home/ab2111/machine_learning_landscapes/neural_net/dg.png")
from NNSystem import NNSystem
""" load training data"""
inputs = np.loadtxt("../handwriting/X2_100samples.dat")
targets = np.loadtxt("../handwriting/y2_100samples.dat")
from ffnet_validation import get_validation_data
vinputs, vtargets = get_validation_data()
""" define network topology """
conec = mlgraph((inputs.shape[1],10,1))
print inputs.shape
# exit()
net = ffnet(conec)
system = NNSystem(net, inputs, targets)
database = system.create_database(db="../db/db_ffnet_100samples.sqlite")
# make_disconnectivity_graph(system, database, vinputs, vtargets)
# plt.plot(ts.coords,'x')
# plt.plot(ts.eigenvec,'o')
# plt.show()
make_validation_disconnectivity_graph(system, database)
