def main():
# MLP to be fit
net_mlp = None
with open("/Users/alange/programming/MNIST/store/classic_32.json",
"r") as f:
net_mlp = Network.read_from_json(f)
# Initial particle network
net = ParticleNetwork(cost="categorical_cross_entropy",
particle_input=ParticleInput(784, s=2.0))
net.append(Particle(784, 32, activation="tanh", zeta=1.0, s=2.0))
net.append(Particle(32, 10, activation="softmax", zeta=1.0, s=2.0))
compute_matrices(net)
error = compute_error(net_mlp, net)
de_db, de_dq, de_dt, de_drx, de_dry, de_drz = compute_grad_w(net_mlp, net)
fd_b, fd_q, fd_t, fd_x, fd_y, fd_z = compute_fd_grad(net_mlp, net)
for l, layer in enumerate(net.layers):
diff_b = np.mean(de_db[l] - fd_b[l])
diff_q = np.mean(de_dq[l] - fd_q[l])
diff_t = np.mean(de_dt[l] - fd_t[l])
diff_x = np.mean(de_drx[l] - fd_x[l])
print("b", diff_b)
print("q", diff_q)
print("t", diff_t)
print("x", diff_x)
def main2():
sgd = ParticleSGD(alpha=0.2, n_epochs=1, mini_batch_size=1, verbosity=2, weight_update="momentum", beta=0.5)
# sgd = ParticleSGD(alpha=0.2, n_epochs=1, mini_batch_size=1, verbosity=2)
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
net = ParticleNetwork(cost="mse", particle_input=ParticleInput(2))
net.append(Particle(2, 5, activation="sigmoid"))
net.append(Particle(5, 3, activation="sigmoid"))
sgd.optimize(net, train_X, train_Y)
def main4():
rprop = ParticleRPROP(n_epochs=1, verbosity=0, cost_freq=25, init_delta=0.01, eta_minus=0.5, eta_plus=1.2,
delta_max=0.5, delta_min=1e-6, manhattan=False, n_threads=2)
train_X = np.asarray([[0.2, -0.3], [0.2, -0.4], [0.1, 0.1], [0.9, 1.1], [2.2, 4.4]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
phase = False
net = ParticleNetwork(cost="categorical_cross_entropy", particle_input=ParticleInput(2, phase_enabled=phase))
net.append(Particle(2, 5, activation="sigmoid", phase_enabled=phase))
net.append(Particle(5, 7, activation="sigmoid", phase_enabled=phase))
net.append(Particle(7, 3, activation="softmax", phase_enabled=phase))
print(net.cost(train_X, train_Y))
def main3():
train_X = np.asarray([[0.2, -0.3], [0.1, -0.9]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
net = ParticleNetwork(cost="mse", particle_input=ParticleInput(2))
net.append(Particle(2, 5, activation="sigmoid"))
net.append(Particle(5, 3, activation="sigmoid"))
print(net.predict(train_X))
with open("/Users/adrianlange/network.json", "w") as f:
net.write_to_json(f)
with open("/Users/adrianlange/network.json", "r") as f:
new_net = ParticleNetwork.read_from_json(f)
print(new_net.predict(train_X))
def main():
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
# train_X = np.asarray([{0: 0.45, 1: 3.33}, {1: 2.22}])
train_X = np.asarray([[0.45, 3.33], [0.0, 2.22]])
# train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
train_Y = np.asarray([[1.0], [0.0]])
net = ParticleNetwork(cost="mse", particle_input=ParticleInput(2, phase_enabled=True))
net.append(Particle(2, 3, activation="sigmoid", phase_enabled=True))
net.append(Particle(3, 1, activation="sigmoid", phase_enabled=True))
print(net.particle_input.get_rxyz())
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
255.0) # scaled values in range [0-1]
# length ten categorical vector
Y = []
for val in raw_data_train.ix[:, 0]:
y = np.zeros(10)
y[val] = 1.0
Y.append(y)
Y = np.asarray(Y)
# Data subset
n_sub = len(X)
X_sub = X[:n_sub, :]
Y_sub = Y[:n_sub, :]
net = ParticleNetwork(cost="categorical_cross_entropy",
particle_input=ParticleInput(784))
net.append(Particle(784, 10, activation="sigmoid"))
net.append(Particle(10, 10, activation="softmax"))
start = time.time()
rprop = ParticleSGD(n_epochs=1,
verbosity=2,
cost_freq=25,
n_threads=4,
chunk_size=500,
mini_batch_size=n_sub)
rprop.optimize(net, X_sub, Y_sub)
print(time.time() - start)
def fd():
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
train_X = np.asarray([[0.2, -0.3], [0.1, -0.9], [0.1, 0.05]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
phase = True
p = "gwell"
net = ParticleNetwork(cost="categorical_cross_entropy", particle_input=ParticleInput(2, phase_enabled=phase))
# net = ParticleNetwork(cost="categorical_cross_entropy", particle_input=ParticleInput(2), regularizer=ParticleRegularize(1.0))
# net = ParticleNetwork(cost="categorical_cross_entropy", particle_input=ParticleInput(2), regularizer=ParticleRegularizeL2Charge(0.3))
net.append(Particle(2, 5, activation="sigmoid", potential=p, phase_enabled=phase))
net.append(Particle(5, 4, activation="sigmoid", potential=p, phase_enabled=phase))
net.append(Particle(4, 3, activation="softmax", potential=p, phase_enabled=phase))
# Finite difference checking
net.cost(train_X, train_Y)
db, dq, dr, dt, dt_in = net.cost_gradient(train_X, train_Y)
# db, dq, dr, dt = net.cost_gradient(train_X, train_Y)
h = 0.001
print("analytic b")
print(db)
fd_b = []
for l in range(len(net.layers)):
lb = []
for c in range(len(net.layers[l].b)):
for b in range(len(net.layers[l].b[c])):
orig = net.layers[l].b[c][b]
net.layers[l].b[c][b] += h
fp = net.cost(train_X, train_Y)
net.layers[l].b[c][b] -= 2*h
fm = net.cost(train_X, train_Y)
lb.append((fp - fm) / (2*h))
net.layers[l].b[c][b] = orig
fd_b.append(lb)
print("numerical b")
print(fd_b)
print("analytic q")
for x in dq:
print(x)
fd_q = []
for l in range(len(net.layers)):
lq = []
for i in range(len(net.layers[l].q)):
orig = net.layers[l].q[i]
net.layers[l].q[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].q[i] -= 2*h
fm = net.cost(train_X, train_Y)
lq.append((fp - fm) / (2*h))
net.layers[l].q[i] = orig
fd_q.append(lq)
print("numerical q")
for x in fd_q:
print(x)
print("analytic theta")
for x in dt:
print(x)
# input layer
fd_t = []
layer = net.particle_input
lt = []
for i in range(len(layer.theta)):
orig = layer.theta[i]
layer.theta[i] += h
fp = net.cost(train_X, train_Y)
layer.theta[i] -= 2*h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2*h))
layer.theta[i] = orig
fd_t.append(lt)
# layers
for l in range(len(net.layers)):
lt = []
for i in range(len(net.layers[l].theta)):
orig = net.layers[l].theta[i]
net.layers[l].theta[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].theta[i] -= 2*h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2*h))
net.layers[l].theta[i] = orig
fd_t.append(lt)
print("numerical theta")
for x in fd_t:
print(x)
print("analytic theta_in")
for x in dt_in:
print(x)
# input layer
fd_t = []
layer = net.particle_input
lt = []
for i in range(len(layer.theta_in)):
lt.append(0.0)
fd_t.append(lt)
# layers
for l in range(len(net.layers)):
lt = []
for i in range(len(net.layers[l].theta_in)):
orig = net.layers[l].theta_in[i]
net.layers[l].theta_in[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].theta_in[i] -= 2*h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2*h))
net.layers[l].theta_in[i] = orig
fd_t.append(lt)
print("numerical theta_in")
for x in fd_t:
print(x)
# print("analytic zeta")
# for x in dzeta:
# print(x)
#
# # input layer
# fd_zeta = []
# layer = net.particle_input
# lt = []
# for i in range(len(layer.zeta)):
# orig = layer.zeta[i]
# layer.zeta[i] += h
# fp = net.cost(train_X, train_Y)
# layer.zeta[i] -= 2*h
# fm = net.cost(train_X, train_Y)
# lt.append((fp - fm) / (2*h))
# layer.zeta[i] = orig
# fd_zeta.append(lt)
#
# # layers
# for l in range(len(net.layers)):
# lt = []
# for i in range(len(net.layers[l].zeta)):
# orig = net.layers[l].zeta[i]
# net.layers[l].zeta[i] += h
# fp = net.cost(train_X, train_Y)
# net.layers[l].zeta[i] -= 2*h
# fm = net.cost(train_X, train_Y)
# lt.append((fp - fm) / (2*h))
# net.layers[l].zeta[i] = orig
# fd_zeta.append(lt)
#
# print("numerical zeta")
# for x in fd_zeta:
# print(x)
fd_r_x = []
fd_r_y = []
fd_r_z = []
# input first
layer = net.particle_input
lr_x = []
lr_y = []
lr_z = []
for i in range(layer.output_size):
# x
orig = layer.rx[i]
layer.rx[i] += h
fp = net.cost(train_X, train_Y)
layer.rx[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_x.append((fp - fm) / (2*h))
layer.rx[i] = orig
# y
orig = layer.ry[i]
layer.ry[i] += h
fp = net.cost(train_X, train_Y)
layer.ry[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_y.append((fp - fm) / (2*h))
layer.ry[i] = orig
# z
orig = layer.rz[i]
layer.rz[i] += h
fp = net.cost(train_X, train_Y)
layer.rz[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_z.append((fp - fm) / (2*h))
layer.rz[i] = orig
fd_r_x.append(lr_x)
fd_r_y.append(lr_y)
fd_r_z.append(lr_z)
# layers
for layer in net.layers:
lr_x = []
lr_y = []
lr_z = []
for i in range(layer.output_size):
# x
orig = layer.rx[i]
layer.rx[i] += h
fp = net.cost(train_X, train_Y)
layer.rx[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_x.append((fp - fm) / (2*h))
layer.rx[i] = orig
# y
orig = layer.ry[i]
layer.ry[i] += h
fp = net.cost(train_X, train_Y)
layer.ry[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_y.append((fp - fm) / (2*h))
layer.ry[i] = orig
# z
orig = layer.rz[i]
layer.rz[i] += h
fp = net.cost(train_X, train_Y)
layer.rz[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_z.append((fp - fm) / (2*h))
layer.rz[i] = orig
fd_r_x.append(lr_x)
fd_r_y.append(lr_y)
fd_r_z.append(lr_z)
print("analytic x")
for layer in dr[0]:
print(layer)
print("numerical r x")
for f in fd_r_x:
print(f)
print("analytic y")
for layer in dr[1]:
print(layer)
print("numerical r y")
for f in fd_r_y:
print(f)
print("analytic z")
for layer in dr[2]:
print(layer)
print("numerical r z")
for f in fd_r_z:
print(f)
n_seed = 100
np.random.seed(n_seed)
state = np.random.get_state()
# MNIST data
raw_data_train = pd.read_csv("/Users/alange/programming/MNIST/data/mnist_train.csv", header=None)
print("data loaded")
# Prepare data
X = np.asarray(raw_data_train.ix[:, 1:] / 255.0) # scaled values in range [0-1]
# length ten categorical vector
Y = []
for val in raw_data_train.ix[:, 0]:
y = np.zeros(10)
y[val] = 1.0
Y.append(y)
Y = np.asarray(Y)
# Data subset
n_sub = 4000
X_sub = X[:n_sub, :]
Y_sub = Y[:n_sub, :]
net = ParticleNetwork(cost="categorical_cross_entropy", particle_input=ParticleInput(784))
net.append(Particle(784, 128, activation="sigmoid"))
net.append(Particle(128, 10, activation="softmax"))
rprop = ParticleRPROP(n_epochs=4, verbosity=2, cost_freq=25, n_threads=4, chunk_size=500)
rprop.optimize(net, X_sub, Y_sub)