def run_debug(self, x):
"""Compute the trained solution (debug version)."""
n = len(x)
H = len(self.v)
w = self.w
u = self.u
v = self.v
z = np.zeros((n, H))
for i in range(n):
for k in range(H):
z[i, k] = w[k]*x[i] + u[k]
s = np.zeros((n, H))
for i in range(n):
for k in range(H):
s[i, k] = sigma.s(z[i, k])
N = np.zeros(n)
for i in range(n):
for k in range(H):
N[i] += s[i, k]*v[k]
Yt = np.zeros(n)
for i in range(n):
Yt[i] = self.__Ytf(x[i], N[i])
return Yt
def __compute_error_debug(self, p, x):
"""Compute the error function using the current parameter values (debug version)."""
# Fetch the number of training points.
n = len(x)
# Unpack the network parameters.
H = len(self.v)
(w, u, v) = np.hsplit(p, 3)
# Compute the forward pass through the network.
z = np.zeros((n, H))
for i in range(n):
for k in range(H):
z[i, k] = x[i]*w[k] + u[k]
s = np.zeros((n, H))
for i in range(n):
for k in range(H):
s[i, k] = sigma.s(z[i, k])
s1 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s1[i, k] = sigma.s1(s[i, k])
s2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s2[i, k] = sigma.s2(s[i, k])
N = np.zeros(n)
for i in range(n):
for k in range(H):
N[i] += v[k]*s[i, k]
dN_dx = np.zeros(n)
for i in range(n):
for k in range(H):
dN_dx[i] += s1[i, k]*v[k]*w[k]
d2N_dx2 = np.zeros(n)
for i in range(n):
for k in range(H):
d2N_dx2[i] += v[k]*s2[i, k]*w[k]**2
Yt = np.zeros(n)
for i in range(n):
Yt[i] = self.__Ytf(x[i], N[i])
dYt_dx = np.zeros(n)
for i in range(n):
dYt_dx[i] = self.__dYt_dxf(x[i], N[i], dN_dx[i])
d2Yt_dx2 = np.zeros(n)
for i in range(n):
d2Yt_dx2[i] = self.__d2Yt_dx2f(x[i], N[i], dN_dx[i], d2N_dx2[i])
G = np.zeros(n)
for i in range(n):
G[i] = self.eq.Gf(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
E = 0
for i in range(n):
E += G[i]**2
return E
def run_derivative2_debug(self, x):
"""Compute the trained 2nd derivative (debug version)."""
n = len(x)
H = len(self.v)
w = self.w
u = self.u
v = self.v
z = np.zeros((n, H))
for i in range(n):
for k in range(H):
z[i, k] = w[k]*x[i] + u[k]
s = np.zeros((n, H))
for i in range(n):
for k in range(H):
s[i, k] = sigma.s(z[i, k])
s1 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s1[i, k] = sigma.s1(s[i, k])
s2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s2[i, k] = sigma.s2(s[i, k])
N = np.zeros(n)
for i in range(n):
for k in range(H):
N[i] += s[i, k]*v[k]
dN_dx = np.zeros(n)
for i in range(n):
for k in range(H):
dN_dx[i] += v[k]*s1[i, k]*w[k]
d2N_dx2 = np.zeros(n)
for i in range(n):
for k in range(H):
d2N_dx2[i] += v[k]*s2[i, k]*w[k]**2
d2Yt_dx2 = np.zeros(n)
for i in range(n):
d2Yt_dx2[i] = self.__d2Yt_dx2f(x[i], N[i], dN_dx[i], d2N_dx2[i])
return d2Yt_dx2
def __compute_error_gradient_debug(self, p, x):
"""Compute the gradient of the error function wrt network
parameters (debug version)."""
# Fetch the number of training points.
n = len(x)
# Unpack the network parameters (hsplit() returns views, so no copies made).
H = len(self.v)
(w, u, v) = np.hsplit(p, 3)
# Compute the forward pass through the network.
z = np.zeros((n, H))
for i in range(n):
for k in range(H):
z[i, k] = w[k]*x[i] + u[k]
s = np.zeros((n, H))
for i in range(n):
for k in range(H):
s[i, k] = sigma.s(z[i, k])
s1 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s1[i, k] = sigma.s1(s[i, k])
s2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s2[i, k] = sigma.s2(s[i, k])
s3 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s3[i, k] = sigma.s3(s[i, k])
N = np.zeros(n)
for i in range(n):
for k in range(H):
N[i] += v[k]*s[i, k]
dN_dx = np.zeros(n)
for i in range(n):
for k in range(H):
dN_dx[i] += v[k]*s1[i, k]*w[k]
d2N_dx2 = np.zeros(n)
for i in range(n):
for k in range(H):
d2N_dx2[i] += v[k]*s2[i, k]*w[k]**2
dN_dw = np.zeros((n, H))
for i in range(n):
for k in range(H):
dN_dw[i, k] = v[k]*s1[i, k]*x[i]
dN_du = np.zeros((n, H))
for i in range(n):
for k in range(H):
dN_du[i, k] = v[k]*s1[i, k]
dN_dv = np.zeros((n, H))
for i in range(n):
for k in range(H):
dN_dv[i, k] = s[i, k]
d2N_dwdx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2N_dwdx[i, k] = v[k]*(s1[i, k] + s2[i, k]*w[k]*x[i])
d2N_dudx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2N_dudx[i, k] = v[k]*s2[i, k]*w[k]
d2N_dvdx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2N_dvdx[i, k] = s1[i, k]*w[k]
d3N_dwdx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3N_dwdx2[i, k] = v[k]*(2*s2[i, k]*w[k] + s3[i, k]*w[k]**2*x[i])
d3N_dudx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3N_dudx2[i, k] = v[k]*s3[i, k]*w[k]**2
d3N_dvdx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3N_dvdx2[i, k] = s2[i, k]*w[k]**2
Yt = np.zeros(n)
for i in range(n):
Yt[i] = self.__Ytf(x[i], N[i])
dYt_dx = np.zeros(n)
for i in range(n):
dYt_dx[i] = self.__dYt_dxf(x[i], N[i], dN_dx[i])
d2Yt_dx2 = np.zeros(n)
for i in range(n):
d2Yt_dx2[i] = self.__d2Yt_dx2f(x[i], N[i], dN_dx[i], d2N_dx2[i])
dYt_dw = np.zeros((n, H))
for i in range(n):
for k in range(H):
dYt_dw[i, k] = x[i]*(1 - x[i])*dN_dw[i, k]
dYt_du = np.zeros((n, H))
for i in range(n):
for k in range(H):
dYt_du[i, k] = x[i]*(1 - x[i])*dN_du[i, k]
dYt_dv = np.zeros((n, H))
for i in range(n):
for k in range(H):
dYt_dv[i, k] = x[i]*(1 - x[i])*dN_dv[i, k]
d2Yt_dwdx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2Yt_dwdx[i, k] = x[i]*(1 - x[i])*d2N_dwdx[i, k] + (1 - 2*x[i])*dN_dw[i, k]
d2Yt_dudx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2Yt_dudx[i, k] = x[i]*(1 - x[i])*d2N_dudx[i, k] + (1 - 2*x[i])*dN_du[i, k]
d2Yt_dvdx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2Yt_dvdx[i, k] = x[i]*(1 - x[i])*d2N_dvdx[i, k] + (1 - 2*x[i])*dN_dv[i, k]
d3Yt_dwdx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3Yt_dwdx2[i, k] = x[i]*(1 - x[i])*d3N_dwdx2[i, k] + 2*(1 - 2*x[i])*d2N_dwdx[i, k] - 2*dN_dw[i, k]
d3Yt_dudx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3Yt_dudx2[i, k] = x[i]*(1 - x[i])*d3N_dudx2[i, k] + 2*(1 - 2*x[i])*d2N_dudx[i, k] - 2*dN_du[i, k]
d3Yt_dvdx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3Yt_dvdx2[i, k] = x[i]*(1 - x[i])*d3N_dvdx2[i, k] + 2*(1 - 2*x[i])*d2N_dvdx[i, k] - 2*dN_dv[i, k]
G = np.zeros(n)
for i in range(n):
G[i] = self.eq.Gf(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
dG_dYt = np.zeros(n)
for i in range(n):
dG_dYt[i] = self.eq.dG_dYf(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
dG_ddYtdx = np.zeros(n)
for i in range(n):
dG_ddYtdx[i] = self.eq.dG_ddYdxf(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
dG_dd2Ytdx2 = np.zeros(n)
for i in range(n):
dG_dd2Ytdx2[i] = self.eq.dG_dd2Ydx2f(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
dG_dw = np.zeros((n, H))
for i in range(n):
for k in range(H):
dG_dw[i, k] = dG_dYt[i]*dYt_dw[i, k] + dG_ddYtdx[i]*d2Yt_dwdx[i, k] + dG_dd2Ytdx2[i]*d3Yt_dwdx2[i, k]
dG_du = np.zeros((n, H))
for i in range(n):
for k in range(H):
dG_du[i, k] = dG_dYt[i]*dYt_du[i, k] + dG_ddYtdx[i]*d2Yt_dudx[i, k] + dG_dd2Ytdx2[i]*d3Yt_dudx2[i, k]
dG_dv = np.zeros((n, H))
for i in range(n):
for k in range(H):
dG_dv[i, k] = dG_dYt[i]*dYt_dv[i, k] + dG_ddYtdx[i]*d2Yt_dvdx[i, k] + dG_dd2Ytdx2[i]*d3Yt_dvdx2[i, k]
dE_dw = np.zeros(H)
for k in range(H):
for i in range(n):
dE_dw[k] += 2*G[i]*dG_dw[i, k]
dE_du = np.zeros(H)
for k in range(H):
for i in range(n):
dE_du[k] += 2*G[i]*dG_du[i, k]
dE_dv = np.zeros(H)
for k in range(H):
for i in range(n):
dE_dv[k] += 2*G[i]*dG_dv[i, k]
jac = np.zeros(3*H)
for j in range(H):
jac[j] = dE_dw[j]
for j in range(H):
jac[H + j] = dE_du[j]
for j in range(H):
jac[2*H + j] = dE_dv[j]
return jac
def __train_delta_debug(self, x, opts=DEFAULT_OPTS):
"""Train using the delta method (debug version). """
my_opts = dict(DEFAULT_OPTS)
my_opts.update(opts)
# Sanity-check arguments.
assert len(x) > 0
assert opts['maxepochs'] > 0
assert opts['eta'] > 0
assert opts['vmin'] < opts['vmax']
assert opts['wmin'] < opts['wmax']
assert opts['umin'] < opts['umax']
# Determine the number of training points, and change notation for
# convenience.
n = len(x) # Number of training points
H = len(self.v)
debug = my_opts['debug']
verbose = my_opts['verbose']
eta = my_opts['eta'] # Learning rate
maxepochs = my_opts['maxepochs'] # Number of training epochs
wmin = my_opts['wmin'] # Network parameter limits
wmax = my_opts['wmax']
umin = my_opts['umin']
umax = my_opts['umax']
vmin = my_opts['vmin']
vmax = my_opts['vmax']
# Create the hidden node weights, biases, and output node weights.
w = np.random.uniform(wmin, wmax, H)
u = np.random.uniform(umin, umax, H)
v = np.random.uniform(vmin, vmax, H)
# Initial parameter deltas are 0.
dE_dw = np.zeros(H)
dE_du = np.zeros(H)
dE_dv = np.zeros(H)
# Train the network.
for epoch in range(maxepochs):
if verbose:
print('Starting epoch %d.' % epoch)
# Compute the new values of the network parameters.
for k in range(H):
w[k] -= eta*dE_dw[k]
for k in range(H):
u[k] -= eta*dE_du[k]
for k in range(H):
v[k] -= eta*dE_dv[k]
# Compute the input, the sigmoid function, and its derivatives, for
# each hidden node and each training point.
z = np.zeros((n, H))
for i in range(n):
for k in range(H):
z[i, k] = w[k]*x[i] + u[k]
s = np.zeros((n, H))
for i in range(n):
for k in range(H):
s[i, k] = sigma.s(z[i, k])
s1 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s1[i, k] = sigma.s1(s[i, k])
s2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s2[i, k] = sigma.s2(s[i, k])
s3 = np.zeros((n, H))
for i in range(n):
for k in range(H):
s3[i, k] = sigma.s3(s[i, k])
# Compute the network output and its derivatives, for each
# training point.
N = np.zeros(n)
for i in range(n):
for k in range(H):
N[i] += v[k]*s[i, k]
dN_dx = np.zeros(n)
for i in range(n):
for k in range(H):
dN_dx[i] += v[k]*s1[i, k]*w[k]
d2N_dx2 = np.zeros(n)
for i in range(n):
for k in range(H):
d2N_dx2[i] += v[k]*s2[i, k]*w[k]**2
dN_dw = np.zeros((n, H))
for i in range(n):
for k in range(H):
dN_dw[i, k] = v[k]*s1[i, k]*x[i]
dN_du = np.zeros((n, H))
for i in range(n):
for k in range(H):
dN_du[i, k] = v[k]*s1[i, k]
dN_dv = np.zeros((n, H))
for i in range(n):
for k in range(H):
dN_dv[i, k] = s[i, k]
d2N_dwdx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2N_dwdx[i, k] = v[k]*(s1[i, k] + s2[i, k]*w[k]*x[i])
d2N_dudx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2N_dudx[i, k] = v[k]*s2[i, k]*w[k]
d2N_dvdx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2N_dvdx[i, k] = s1[i, k]*w[k]
d3N_dwdx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3N_dwdx2[i, k] = v[k]*(2*s2[i, k]*w[k] + s3[i, k]*w[k]**2*x[i])
d3N_dudx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3N_dudx2[i, k] = v[k]*s3[i, k]*w[k]**2
d3N_dvdx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3N_dvdx2[i, k] = s2[i, k]*w[k]**2
# Compute the value of the trial solution and its derivatives,
# for each training point.
Yt = np.zeros(n)
for i in range(n):
Yt[i] = self.__Ytf(x[i], N[i])
dYt_dx = np.zeros(n)
for i in range(n):
dYt_dx[i] = self.__dYt_dxf(x[i], N[i], dN_dx[i])
d2Yt_dx2 = np.zeros(n)
for i in range(n):
d2Yt_dx2[i] = self.__d2Yt_dx2f(x[i], N[i], dN_dx[i], d2N_dx2[i])
dYt_dw = np.zeros((n, H))
for i in range(n):
for k in range(H):
dYt_dw[i, k] = x[i]*(1 - x[i])*dN_dw[i, k]
dYt_du = np.zeros((n, H))
for i in range(n):
for k in range(H):
dYt_du[i, k] = x[i]*(1 - x[i])*dN_du[i, k]
dYt_dv = np.zeros((n, H))
for i in range(n):
for k in range(H):
dYt_dv[i, k] = x[i]*(1 - x[i])*dN_dv[i, k]
d2Yt_dwdx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2Yt_dwdx[i, k] = x[i]*(1 - x[i])*d2N_dwdx[i, k] + (1 - 2*x[i])*dN_dw[i, k]
d2Yt_dudx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2Yt_dudx[i, k] = x[i]*(1 - x[i])*d2N_dudx[i, k] + (1 - 2*x[i])*dN_du[i, k]
d2Yt_dvdx = np.zeros((n, H))
for i in range(n):
for k in range(H):
d2Yt_dvdx[i, k] = x[i]*(1 - x[i])*d2N_dvdx[i, k] + (1 - 2*x[i])*dN_dv[i, k]
d3Yt_dwdx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3Yt_dwdx2[i, k] = x[i]*(1 - x[i])*d3N_dwdx2[i, k] + 2*(1 - 2*x[i])*d2N_dwdx[i, k] - 2*dN_dw[i, k]
d3Yt_dudx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3Yt_dudx2[i, k] = x[i]*(1 - x[i])*d3N_dudx2[i, k] + 2*(1 - 2*x[i])*d2N_dudx[i, k] - 2*dN_du[i, k]
d3Yt_dvdx2 = np.zeros((n, H))
for i in range(n):
for k in range(H):
d3Yt_dvdx2[i, k] = x[i]*(1 - x[i])*d3N_dvdx2[i, k] + 2*(1 - 2*x[i])*d2N_dvdx[i, k] - 2*dN_dv[i, k]
# Compute the value of the original differential equation for
# each training point, and its derivatives.
G = np.zeros(n)
for i in range(n):
G[i] = self.eq.Gf(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
dG_dYt = np.zeros(n)
for i in range(n):
dG_dYt[i] = self.eq.dG_dYf(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
dG_ddYtdx = np.zeros(n)
for i in range(n):
dG_ddYtdx[i] = self.eq.dG_ddYdxf(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
dG_dd2Ytdx2 = np.zeros(n)
for i in range(n):
dG_dd2Ytdx2[i] = self.eq.dG_dd2Ydx2f(x[i], Yt[i], dYt_dx[i], d2Yt_dx2[i])
dG_dw = np.zeros((n, H))
for i in range(n):
for k in range(H):
dG_dw[i, k] = dG_dYt[i]*dYt_dw[i, k] + dG_ddYtdx[i]*d2Yt_dwdx[i, k] + dG_dd2Ytdx2[i]*d3Yt_dwdx2[i, k]
dG_du = np.zeros((n, H))
for i in range(n):
for k in range(H):
dG_du[i, k] = dG_dYt[i]*dYt_du[i, k] + dG_ddYtdx[i]*d2Yt_dudx[i, k] + dG_dd2Ytdx2[i]*d3Yt_dudx2[i, k]
dG_dv = np.zeros((n, H))
for i in range(n):
for k in range(H):
dG_dv[i, k] = dG_dYt[i]*dYt_dv[i, k] + dG_ddYtdx[i]*d2Yt_dvdx[i, k] + dG_dd2Ytdx2[i]*d3Yt_dvdx2[i, k]
# Compute the error function for this epoch.
E = 0
for i in range(n):
E += G[i]**2
# Compute the partial derivatives of the error with respect to the
# network parameters.
dE_dw = np.zeros(H)
for k in range(H):
for i in range(n):
dE_dw[k] += 2*G[i]*dG_dw[i, k]
dE_du = np.zeros(H)
for k in range(H):
for i in range(n):
dE_du[k] += 2*G[i]*dG_du[i, k]
dE_dv = np.zeros(H)
for k in range(H):
for i in range(n):
dE_dv[k] += 2*G[i]*dG_dv[i, k]
# Compute the RMS error for this epoch.
rmse = sqrt(E/n)
if opts['verbose']:
print(epoch, rmse)
# Save the optimized parameters.
self.w = w
self.u = u
self.v = v
