def logpxz(self, w, x, z):
x, z = self.xz_to_theano(x, z)
w, z, x = ndict.ordereddicts((w, z, x))
A = self.get_A(x)
allvars = w.values() + x.values() + z.values() + [A]
logpx, logpz = self.f_logpxz(*allvars)
if np.isnan(logpx).any() or np.isnan(logpz).any():
print 'v: ', logpx, logpz
print 'Values:'
ndict.p(w)
ndict.p(z)
raise Exception("dlogpxz_dwz(): NaN found in gradients")
return logpx, logpz
def logpxz(self, w, x, z):
x, z = self.xz_to_theano(x, z)
w, z, x = ndict.ordereddicts((w, z, x))
A = self.get_A(x)
allvars = list(w.values()) + list(x.values()) + list(z.values()) + [A]
logpx, logpz = self.f_logpxz(*allvars)
if np.isnan(logpx).any() or np.isnan(logpz).any():
print('v: ', logpx, logpz)
print('Values:')
ndict.p(w)
ndict.p(z)
raise Exception("dlogpxz_dwz(): NaN found in gradients")
return logpx, logpz
def checknan(self, v, w, gv, gw):
if ndict.hasNaN(gv) or ndict.hasNaN(gw):
raise Exception("dL_dw(): NaN found in gradients")
print('v:')
ndict.p(v)
print('w:')
ndict.p(w)
print('gv:')
ndict.p(gv)
print('gw:')
ndict.p(gw)
raise Exception("dL_dw(): NaN found in gradients")
def dfd_dw(self, w, x, z, gz2):
x, z = self.xz_to_theano(x, z)
w, z, x, gz2 = ndict.ordereddicts((w, z, x, gz2))
A = self.get_A(x)
r = self.f_dfd_dw(*(list(w.values()) + list(x.values()) +
list(z.values()) + [A] + list(gz2.values())))
logpx, logpz, fd, gw = r[0], r[1], r[2], dict(
list(zip(list(w.keys()), r[3:3 + len(w)])))
if ndict.hasNaN(gw):
if True:
print('NaN detected in gradients')
raise Exception()
for i in gw:
gw[i][np.isnan(gw[i])] = 0
else:
print('fd: ', fd)
print('Values:')
ndict.p(w)
ndict.p(z)
print('Gradients:')
ndict.p(gw)
raise Exception("dfd_dw(): NaN found in gradients")
gw, _ = self.gwgz_to_numpy(gw, {})
return logpx, logpz, fd, gw
def checknan(self, v, w, gv, gw):
if ndict.hasNaN(gv) or ndict.hasNaN(gw):
raise Exception("dL_dw(): NaN found in gradients")
#print 'logpx: ', logpx
#print 'logpz: ', logpz
#print 'logqz: ', logqz
print 'v:'
ndict.p(v)
print 'w:'
ndict.p(w)
print 'gv:'
ndict.p(gv)
print 'gw:'
ndict.p(gw)
raise Exception("dL_dw(): NaN found in gradients")
def checknan(self, v, w, gv, gw):
if ndict.hasNaN(gv) or ndict.hasNaN(gw):
raise Exception("dL_dw(): NaN found in gradients")
#print 'logpx: ', logpx
#print 'logpz: ', logpz
#print 'logqz: ', logqz
print 'v:'
ndict.p(v)
print 'w:'
ndict.p(w)
print 'gv:'
ndict.p(gv)
print 'gw:'
ndict.p(gw)
raise Exception("dL_dw(): NaN found in gradients")
def L(self, v, w, x, z):
x, z = self.xz_to_theano(x, z)
v, w, z, x = ndict.ordereddicts((v, w, z, x))
A = self.get_A(x)
allvars = list(v.values()) + list(w.values()) + list(x.values()) + list(z.values()) + [A]
logpx, logpz, logqz = self.f_L(*allvars)
if np.isnan(logpx).any() or np.isnan(logpz).any() or np.isnan(logqz).any():
print('logp: ', logpx, logpz, logqz)
print('Values:')
ndict.p(v)
ndict.p(w)
ndict.p(x)
ndict.p(z)
raise Exception("delbo_dwz(): NaN found in gradients")
return logpx, logpz, logqz
def L(self, v, w, x, z):
x, z = self.xz_to_theano(x, z)
v, w, z, x = ndict.ordereddicts((v, w, z, x))
A = self.get_A(x)
allvars = v.values() + w.values() + x.values() + z.values() + [A]
logpx, logpz, logqz = self.f_L(*allvars)
if np.isnan(logpx).any() or np.isnan(logpz).any() or np.isnan(logqz).any():
print 'logp: ', logpx, logpz, logqz
print 'Values:'
ndict.p(v)
ndict.p(w)
ndict.p(x)
ndict.p(z)
raise Exception("delbo_dwz(): NaN found in gradients")
return logpx, logpz, logqz
def dlogpxz_dwz(self, w, x, z):
x, z = self.xz_to_theano(x, z)
w, z, x = ndict.ordereddicts((w, z, x))
A = self.get_A(x)
allvars = list(w.values()) + list(x.values()) + list(z.values()) + [A]
# Check if keys are correct
keys = list(w.keys()) + list(x.keys()) + list(z.keys()) + ['A']
for i in range(len(keys)):
if keys[i] != self.allvars_keys[i]:
"Input values are incorrect!"
print('Input:', keys)
print('Should be:', self.allvars_keys)
raise Exception()
r = self.f_dlogpxz_dwz(*allvars)
logpx, logpz, gw, gz = r[0], r[1], dict(
list(zip(list(w.keys()), r[2:2 + len(w)]))), dict(
list(zip(list(z.keys()), r[2 + len(w):])))
if ndict.hasNaN(gw) or ndict.hasNaN(gz):
if True:
print('NaN detected in gradients')
raise Exception()
for i in gw:
gw[i][np.isnan(gw[i])] = 0
for i in gz:
gz[i][np.isnan(gz[i])] = 0
else:
print('logpx: ', logpx)
print('logpz: ', logpz)
print('Values:')
ndict.p(w)
ndict.p(z)
print('Gradients:')
ndict.p(gw)
ndict.p(gz)
raise Exception("dlogpxz_dwz(): NaN found in gradients")
gw, gz = self.gwgz_to_numpy(gw, gz)
return logpx, logpz, gw, gz
def dlogpxz_dwz(self, w, x, z):
x, z = self.xz_to_theano(x, z)
w, z, x = ndict.ordereddicts((w, z, x))
A = self.get_A(x)
allvars = w.values() + x.values() + z.values() + [A]
# Check if keys are correct
keys = w.keys() + x.keys() + z.keys() + ['A']
for i in range(len(keys)):
if keys[i] != self.allvars_keys[i]:
"Input values are incorrect!"
print 'Input:', keys
print 'Should be:', self.allvars_keys
raise Exception()
r = self.f_dlogpxz_dwz(*allvars)
logpx, logpz, gw, gz = r[0], r[1], dict(zip(w.keys(), r[2:2+len(w)])), dict(zip(z.keys(), r[2+len(w):]))
if ndict.hasNaN(gw) or ndict.hasNaN(gz):
if True:
print 'NaN detected in gradients'
raise Exception()
for i in gw: gw[i][np.isnan(gw[i])] = 0
for i in gz: gz[i][np.isnan(gz[i])] = 0
else:
print 'logpx: ', logpx
print 'logpz: ', logpz
print 'Values:'
ndict.p(w)
ndict.p(z)
print 'Gradients:'
ndict.p(gw)
ndict.p(gz)
raise Exception("dlogpxz_dwz(): NaN found in gradients")
gw, gz = self.gwgz_to_numpy(gw, gz)
return logpx, logpz, gw, gz
def dfd_dw(self, w, x, z, gz2):
x, z = self.xz_to_theano(x, z)
w, z, x, gz2 = ndict.ordereddicts((w, z, x, gz2))
A = self.get_A(x)
r = self.f_dfd_dw(*(w.values() + x.values() + z.values() + [A] + gz2.values()))
logpx, logpz, fd, gw = r[0], r[1], r[2], dict(zip(w.keys(), r[3:3+len(w)]))
if ndict.hasNaN(gw):
if True:
print 'NaN detected in gradients'
raise Exception()
for i in gw: gw[i][np.isnan(gw[i])] = 0
else:
print 'fd: ', fd
print 'Values:'
ndict.p(w)
ndict.p(z)
print 'Gradients:'
ndict.p(gw)
raise Exception("dfd_dw(): NaN found in gradients")
gw, _ = self.gwgz_to_numpy(gw, {})
return logpx, logpz, fd, gw
