def main():
""" load training data"""
inputs = np.loadtxt("../handwriting/X2_100samples.dat")
targets = np.loadtxt("../handwriting/y2_100samples.dat")
ValInputs, ValTargets = get_validation_data()
""" define network topology """
conec = mlgraph((inputs.shape[1], 10, 1))
net = ffnet(conec)
system = NNSystem(net, inputs, targets)
pot = system.get_potential()
database = system.create_database(
db=
"/home/ab2111/machine_learning_landscapes/neural_net/db_ffnet_100samples.sqlite"
)
# database = system.create_database(db="/home/ab2111/machine_learning_landscapes/neural_net/db_ffnet_me3.sqlite")
# run_gui(system, database)
# check_its_a_minimum(system, database)
energies = np.array([])
for m in database.minima():
coords = m.coords
testenergy = pot.getValidationEnergy(coords, ValInputs,
ValTargets) / len(ValTargets)
energies = np.append(energies, testenergy)
# plt.plot(m.coords,'o')
# np.max(m.coords)
# plt.plot([m._id for m in database.minima()], np.array([m.energy for m in database.minima()])/100., 'o')
plt.plot(np.array([m.energy for m in database.minima()]) / 100)
plt.plot(energies)
plt.plot(
np.array([np.max(m.coords) for m in database.minima()]) / 1000, 'x')
plt.legend(["Etrain", "Evalidation", "max(params)"])
plt.show()
def main():
""" load training data"""
inputs = np.loadtxt("../handwriting/X2_100samples.dat")
targets = np.loadtxt("../handwriting/y2_100samples.dat")
ValInputs, ValTargets = get_validation_data()
""" define network topology """
conec = mlgraph((inputs.shape[1],10,1))
net = ffnet(conec)
system = NNSystem(net, inputs, targets)
pot = system.get_potential()
database = system.create_database(db="/home/ab2111/machine_learning_landscapes/neural_net/db_ffnet_100samples.sqlite")
# database = system.create_database(db="/home/ab2111/machine_learning_landscapes/neural_net/db_ffnet_me3.sqlite")
# run_gui(system, database)
# check_its_a_minimum(system, database)
energies = np.array([])
for m in database.minima():
coords = m.coords
testenergy = pot.getValidationEnergy(coords,ValInputs,ValTargets)/len(ValTargets)
energies = np.append(energies,testenergy)
# plt.plot(m.coords,'o')
# np.max(m.coords)
# plt.plot([m._id for m in database.minima()], np.array([m.energy for m in database.minima()])/100., 'o')
plt.plot(np.array([m.energy for m in database.minima()])/100)
plt.plot(energies)
plt.plot(np.array([np.max(m.coords) for m in database.minima()])/1000, 'x')
plt.legend(["Etrain","Evalidation","max(params)"])
plt.show()
return np.array(points)
""" load training data"""
dir="/scratch/ab2111/dellcp10/projects/BDynam2d/LONGER/TPanalysis/"
tp = np.loadtxt(dir+"tpout",usecols=(1,2))
ntp = np.loadtxt(dir+"nottpout",usecols=(1,2))
print tp.shape
inputs = np.concatenate((tp,ntp))
targets = np.concatenate(([1.0 for a in range(len(tp))],[0.0 for a in range(len(ntp))]))
""" define network topology """
conec = mlgraph((inputs.shape[1],5,1))
net = ffnet(conec)
system = NNSystem(net,inputs,targets)
database = system.create_database(db="/home/ab2111/machine_learning_landscapes/neural_net/db.2dmodel.sqlite")
# run_gui(system, database)
m = database.minima()[0]
net.weights = m.coords
predicts = test_optimized_data(100,net)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Axes3D.plot_surface(predicts[:,0],predicts[:,1],predicts[:,2])
fig = plt.figure()
ax = fig.add_subplot(111,projection='3d')
ax.plot_surface(predicts[:,0],predicts[:,1],predicts[:,2])
# plt.plot_surface(predicts[:,0],predicts[:,1],predicts[:,2])
plt.show()
# run_basinhopping(system,database,system.pot.net.weights,1000)
return np.array(points)
""" load training data"""
dir = "/scratch/ab2111/dellcp10/projects/BDynam2d/LONGER/TPanalysis/"
tp = np.loadtxt(dir + "tpout", usecols=(1, 2))
ntp = np.loadtxt(dir + "nottpout", usecols=(1, 2))
print tp.shape
inputs = np.concatenate((tp, ntp))
targets = np.concatenate(
([1.0 for a in range(len(tp))], [0.0 for a in range(len(ntp))]))
""" define network topology """
conec = mlgraph((inputs.shape[1], 5, 1))
net = ffnet(conec)
system = NNSystem(net, inputs, targets)
database = system.create_database(
db="/home/ab2111/machine_learning_landscapes/neural_net/db.2dmodel.sqlite")
# run_gui(system, database)
m = database.minima()[0]
net.weights = m.coords
predicts = test_optimized_data(100, net)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Axes3D.plot_surface(predicts[:,0],predicts[:,1],predicts[:,2])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(predicts[:, 0], predicts[:, 1], predicts[:, 2])
# plt.plot_surface(predicts[:,0],predicts[:,1],predicts[:,2])
plt.show()
