def main():
train_X = np.asarray([[0.2, -0.3], [0.1, -0.9], [0.3, 0.5]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
# For comparison
net = ParticleDipoleNetwork(cost="mse", particle_input=ParticleDipoleInput(2))
net.append(ParticleDipole(2, 5, activation="sigmoid"))
net.append(ParticleDipole(5, 3, activation="sigmoid"))
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
net2 = ParticleDipoleTreeNetwork(cost="mse", particle_input=ParticleDipoleTreeInput(2))
net2.append(ParticleDipoleTree(2, 5, activation="sigmoid"))
net2.append(ParticleDipoleTree(5, 3, activation="sigmoid"))
# Make sure we have the same coordinates and charges
net2.particle_input.copy_pos_neg_positions(net.particle_input.rx_pos, net.particle_input.ry_pos, net.particle_input.rz_pos,
net.particle_input.rx_neg, net.particle_input.ry_neg, net.particle_input.rz_neg)
for l in range(len(net.layers)):
net2.layers[l].copy_pos_neg_positions(net.layers[l].q, net.layers[l].b,
net.layers[l].rx_pos, net.layers[l].ry_pos, net.layers[l].rz_pos,
net.layers[l].rx_neg, net.layers[l].ry_neg, net.layers[l].rz_neg)
print(net2.predict(train_X))
print(net2.cost(train_X, train_Y))
def main():
train_X = np.asarray([[0.2, -0.3], [0.1, -0.9], [0.3, 0.5]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
# For comparison
net = ParticleDipoleNetwork(cost="mse",
particle_input=ParticleDipoleInput(2))
net.append(ParticleDipole(2, 5, activation="sigmoid"))
net.append(ParticleDipole(5, 3, activation="sigmoid"))
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
net2 = ParticleDipoleTreeNetwork(cost="mse",
particle_input=ParticleDipoleTreeInput(2))
net2.append(ParticleDipoleTree(2, 5, activation="sigmoid"))
net2.append(ParticleDipoleTree(5, 3, activation="sigmoid"))
# Make sure we have the same coordinates and charges
net2.particle_input.copy_pos_neg_positions(net.particle_input.rx_pos,
net.particle_input.ry_pos,
net.particle_input.rz_pos,
net.particle_input.rx_neg,
net.particle_input.ry_neg,
net.particle_input.rz_neg)
for l in range(len(net.layers)):
net2.layers[l].copy_pos_neg_positions(
net.layers[l].q, net.layers[l].b, net.layers[l].rx_pos,
net.layers[l].ry_pos, net.layers[l].rz_pos, net.layers[l].rx_neg,
net.layers[l].ry_neg, net.layers[l].rz_neg)
print(net2.predict(train_X))
print(net2.cost(train_X, train_Y))
times = []
nt = 3
for _ in range(nt):
ts = time.time()
c = net.cost(X_sub, Y_sub)
# c = 1.0
# net.cost_gradient(X_sub, Y_sub)
t = time.time() - ts
print("Cost: {} time: {}".format(c, t))
times.append(t)
print("Mean: " + str(sum(times) / nt))
net2 = ParticleDipoleTreeNetwork(cost="categorical_cross_entropy",
particle_input=ParticleDipoleTreeInput(
784,
s=s,
cut=cut,
max_level=max_level,
mac=mac))
net2.append(
ParticleDipoleTree(784,
n,
activation="sigmoid",
s=s,
cut=cut,
max_level=max_level,
mac=mac,
n_particle_min=n_min))
net2.append(
ParticleDipoleTree(n,
10,
print("starting predict")
times = []
nt = 3
for _ in range(nt):
ts = time.time()
c = net.cost(X_sub, Y_sub)
# c = 1.0
# net.cost_gradient(X_sub, Y_sub)
t = time.time() - ts
print("Cost: {} time: {}".format(c, t))
times.append(t)
print("Mean: " + str(sum(times) / nt))
net2 = ParticleDipoleTreeNetwork(
cost="categorical_cross_entropy",
particle_input=ParticleDipoleTreeInput(784, s=s, cut=cut, max_level=max_level, mac=mac),
)
net2.append(
ParticleDipoleTree(784, n, activation="sigmoid", s=s, cut=cut, max_level=max_level, mac=mac, n_particle_min=n_min)
)
net2.append(
ParticleDipoleTree(n, 10, activation="softmax", s=s, cut=cut, max_level=max_level, mac=mac, n_particle_min=n_min)
)
# Make sure we have the same coordinates and charges
net2.particle_input.copy_pos_neg_positions(
net.particle_input.rx_pos,
net.particle_input.ry_pos,
net.particle_input.rz_pos,
net.particle_input.rx_neg,
net.particle_input.ry_neg,
