def compute_output(self, network, h_vw, x_vw):
batch_axis = network.find_hyperparameter(["batch_axis"])
if batch_axis is None:
# NOTE: this code path is not tested!
jacobian = T.jacobian(h_vw.variable.ravel(), x_vw.variable)
res = (jacobian ** 2).mean()
res_shape = ()
else:
batch_size = h_vw.symbolic_shape()[batch_axis]
# sum across batch to avoid disconnected input error
# ravel to be a vector
h_var = h_vw.variable.sum(axis=batch_axis).ravel()
x_var = x_vw.variable
# shape of result = h_var.shape + x_var.shape
jacobian = T.jacobian(h_var, x_var)
# put batch axis as first dimension
# adding 1 to batch axis, because len(h_var.shape) == 1
swapped_jacobian = jacobian.swapaxes(0, batch_axis + 1)
# convert to a matrix and mean over elements in a batch
reshaped_jacobian = swapped_jacobian.reshape((batch_size, -1))
res = (reshaped_jacobian ** 2).mean(axis=1)
res_shape = (h_vw.shape[batch_axis],)
network.create_variable(
"default",
variable=res,
shape=res_shape,
tags={"output"},
)
def test_dot_not_output(self):
"""
Test the case where the vector input to the dot is not already an
output of the inner function.
"""
v = T.vector()
m = T.matrix()
output = T.dot(v, m)
# Compile the function twice, once with the optimization and once
# without
opt_mode = mode.including("scan")
f_opt = theano.function([v, m], T.jacobian(output, v), mode=opt_mode)
no_opt_mode = mode.excluding("scanOp_pushout_output")
f_no_opt = theano.function([v, m], T.jacobian(output, v), mode=no_opt_mode)
# Ensure that the optimization was performed correctly in f_opt
# The inner function of scan should have only one output and it should
# not be the result of a Dot
scan_node = [node for node in f_opt.maker.fgraph.toposort()
if isinstance(node.op, Scan)][0]
assert len(scan_node.op.outputs) == 1
assert not isinstance(scan_node.op.outputs[0], T.Dot)
# Ensure that the function compiled with the optimization produces
# the same results as the function compiled without
v_value = numpy.random.random((4)).astype(config.floatX)
m_value = numpy.random.random((4, 5)).astype(config.floatX)
output_opt = f_opt(v_value, m_value)
output_no_opt = f_no_opt(v_value, m_value)
utt.assert_allclose(output_opt, output_no_opt)
def _gen_deriv_functions(self):
'''
_gen_deriv_functions
To be called by the derived class to compile all the required functions.
'''
##################################
# Define some Theano derivatives
# Derivative w.r.t. the hyperparameters
self.th_dhyp, uhyp = theano.scan(
lambda i, y, x: T.jacobian(y[i], x),
sequences=T.arange(self.th_K.shape[0]),
non_sequences=[self.th_K, self.th_hyp])
# Derivative w.r.t. the inputs
self.th_dX, ux = theano.scan(lambda i, y, x: T.jacobian(y[i], x),
sequences=T.arange(self.th_K.shape[0]),
non_sequences=[self.th_K, self.th_X])
##################################
# Compilation
# Kxx: self covariance matrix
self.K = theano.function([self.th_X, self.th_hyp], self.th_K)
# Kxy: cross covariance matrix
self.Kc = theano.function([self.th_X, self.th_Xc, self.th_hyp],
self.th_Kc)
self.dK_dhyp = theano.function([self.th_X, self.th_hyp],
self.th_dhyp,
updates=uhyp)
self.dK_dX = theano.function([self.th_X, self.th_hyp],
self.th_dX,
updates=ux)
def compute_output(self, network, h_vw, x_vw):
batch_axis = network.find_hyperparameter(["batch_axis"])
if batch_axis is None:
# NOTE: this code path is not tested!
jacobian = T.jacobian(h_vw.variable.ravel(), x_vw.variable)
res = (jacobian**2).mean()
res_shape = ()
else:
batch_size = h_vw.symbolic_shape()[batch_axis]
# sum across batch to avoid disconnected input error
# ravel to be a vector
h_var = h_vw.variable.sum(axis=batch_axis).ravel()
x_var = x_vw.variable
# shape of result = h_var.shape + x_var.shape
jacobian = T.jacobian(h_var, x_var)
# put batch axis as first dimension
# adding 1 to batch axis, because len(h_var.shape) == 1
swapped_jacobian = jacobian.swapaxes(0, batch_axis + 1)
# convert to a matrix and mean over elements in a batch
reshaped_jacobian = swapped_jacobian.reshape((batch_size, -1))
res = (reshaped_jacobian**2).mean(axis=1)
res_shape = (h_vw.shape[batch_axis], )
network.create_vw(
"default",
variable=res,
shape=res_shape,
tags={"output"},
)
def test_dot_not_output(self):
# Test the case where the vector input to the dot is not already an
# output of the inner function.
v = tt.vector()
m = tt.matrix()
output = tt.dot(v, m)
# Compile the function twice, once with the optimization and once
# without
opt_mode = mode.including("scan")
f_opt = theano.function([v, m], tt.jacobian(output, v), mode=opt_mode)
no_opt_mode = mode.excluding("scanOp_pushout_output")
f_no_opt = theano.function([v, m], tt.jacobian(output, v), mode=no_opt_mode)
# Ensure that the optimization was performed correctly in f_opt
# The inner function of scan should have only one output and it should
# not be the result of a Dot
scan_node = [
node for node in f_opt.maker.fgraph.toposort() if isinstance(node.op, Scan)
][0]
assert len(scan_node.op.outputs) == 1
assert not isinstance(scan_node.op.outputs[0], tt.Dot)
# Ensure that the function compiled with the optimization produces
# the same results as the function compiled without
v_value = np.random.random(4).astype(config.floatX)
m_value = np.random.random((4, 5)).astype(config.floatX)
output_opt = f_opt(v_value, m_value)
output_no_opt = f_no_opt(v_value, m_value)
utt.assert_allclose(output_opt, output_no_opt)
def augment_system(ode_func,t_n, t_m):
'''Function to create augmented system.
Take a function which specifies a set of differential equations and return
a compiled function which allows for computation of gradients of the
differential equation's solition with repsect to the parameters.
Args:
ode_func (function): Differential equation. Returns array-like
Returns:
system (function): Augemted system of differential equations.
'''
#Shapes for the dydp dmatrix
#TODO: Should this be int64 or other dtype?
# t_n = tt.scalar('n', dtype = 'int64')
# t_m = tt.scalar('m', dtype = 'int64')
#Present state of the system
t_y = tt.vector('y', dtype=theano.config.floatX)
#Parameter(s). Should be vector to allow for generaliztion to multiparameter
#systems of ODEs
t_p = tt.vector('p', dtype=theano.config.floatX)
#Time. Allow for non-automonous systems of ODEs to be analyzed
t_t = tt.scalar('t', dtype=theano.config.floatX)
#Present state of the gradients:
#Will always be 0 unless the parameter is the inital condition
#Entry i,j is partial of y[i] wrt to p[j]
dydp_vec = tt.vector('dydp', dtype=theano.config.floatX)
dydp = dydp_vec.reshape((t_n,t_m))
#Stack the results of the ode_func
#TODO: Does this behave the same of ODE is scalar?
f_tensor = tt.stack(ode_func(t_y, t_t, t_p))
#Now compute gradients
J = tt.jacobian(f_tensor,t_y)
Jdfdy = tt.dot(J, dydp)
grad_f = tt.jacobian(f_tensor, t_p)
#This is the time derivative of dydp
ddt_dydp = (Jdfdy + grad_f).flatten()
system = theano.function(
inputs=[t_y, t_t, t_p, dydp_vec],
outputs=[f_tensor, ddt_dydp],
on_unused_input='ignore')
return system
def _grad_single(self, ct, s, lnC2, GAMMI2):
lnC = lnC2
GAMMI = GAMMI2
v = self.v#T.as_tensor(self.v)[:,ct:]
v0 = T.as_tensor(v[v[:,0]==0, :])
v1 = T.as_tensor(v[v[:,0]==1, :])
cnp = v.shape[0]
# Gradient of fE wrt the priors over final state
[ofE, oxS], upd_fE_single = th.scan(fn=self._free_energy,
sequences=v,
non_sequences=[s,self.h,lnC,self.b])
ofE0 = ofE[v0].sum()
ofE1 = ofE[v1].sum()
dFE0dlnC = T.jacobian(ofE0, lnC)
dFE1dlnC = T.jacobian(ofE1, lnC)
dFEdlnC = T.jacobian(ofE, lnC)
ofE_ = T.vector()
ofE_.tag.test_value = ofE.tag.test_value
# Gradient of Gamma with respect to its initial condition:
GAMMA, upd_GAMMA = th.scan(fn=self._upd_gamma,
outputs_info=[GAMMI],
non_sequences=[ofE, self.lambd, self.alpha, self.beta, cnp],
n_steps=4)
dGdg = T.grad(GAMMA[-1], GAMMI)
dGdfE = T.jacobian(GAMMA[-1], ofE)
dGdlnC = dGdfE.dot(dFEdlnC)
out1 = ofE0
out2 = ofE1
maxout = T.max([out1, out2])
exp_out1 = T.exp(GAMMA[-1]*(out1 - maxout))
exp_out2 = T.exp(GAMMA[-1]*(out2 - maxout))
norm_const = exp_out1 + exp_out2
# Derivative wrt the second output (gammi):
Jac1_gammi = (-(out1-out2)*dGdg*
T.exp(GAMMA[-1]*(out1+out2 - 2*maxout))/(norm_const**2))
Jac2_gammi = -Jac1_gammi
# dfd1_tZ = Jac1_gammi*dCdf[1][0]+ Jac2_gammi*dCdf[1][1]
# Derivative wrt first input (lnc)
Jac1_lnC = (T.exp(GAMMA[-1]*(out1 + out2 - 2*maxout))/(norm_const**2)*
(-dGdlnC*(out1 - out2) - GAMMA[-1]*(dFE0dlnC - dFE1dlnC)))
Jac2_lnC = -Jac1_lnC
Jac1 = T.concatenate([T.stack(Jac1_gammi), Jac1_lnC])
Jac2 = T.concatenate([T.stack(Jac2_gammi), Jac2_lnC])
self.debug = [Jac1_lnC, Jac2_lnC, Jac2_gammi, Jac1_gammi, dFE0dlnC,
dFE1dlnC, dGdg, out1, out2, v0, v1, v, ct]
return Jac1, Jac2
def compute_jacobian(errors, parameters):
"""
Compute jacobian.
Parameters
----------
errors : Theano variable
Computed MSE for each sample separetly.
parameters : list of Theano variable
Neural network parameters (e.g. weights, biases).
Returns
-------
Theano variable
"""
n_samples = errors.shape[0]
J = T.jacobian(errors, wrt=parameters)
jacobians = []
for jacobian, parameter in zip(J, parameters):
jacobian = jacobian.reshape((n_samples, parameter.size))
jacobians.append(jacobian)
return T.concatenate(jacobians, axis=1)
def hessian(objective, argument):
"""
Compute the directional derivative of the gradient
(which is equal to the hessian multiplied by direction).
"""
g = T.grad(objective, argument)
# Create a new tensor A, which has the same type (i.e. same dimensionality)
# as argument.
A = argument.type()
try:
# First attempt efficient 'R-op', this directly calculates the
# directional derivative of the gradient, rather than explicitly
# calculating the hessian and then multiplying.
R = T.Rop(g, argument, A)
except NotImplementedError:
shp = T.shape(argument)
H = T.jacobian(g.flatten(),
argument).reshape(T.concatenate([shp, shp]), 2 * A.ndim)
R = T.tensordot(H, A, A.ndim)
try:
hess = theano.function([argument, A], R, on_unused_input='raise')
except theano.compile.UnusedInputError:
warn('Theano detected unused input - suggests hessian may be zero or '
'constant.')
hess = theano.function([argument, A], R, on_unused_input='ignore')
return hess
def fixedPointIteration(*args): fpiIns = dict(zip(self.getFPIArgNames(), args)) gOpFreeIns = [fpiIns[k] for k in self.getGOpFreeArgNames()] gOpClampedIns = [fpiIns[k] for k in self.getGOpClampedArgNames()] gOpFreeRets = self.g(*gOpFreeIns) gOpClampedRets = self.g(*gOpClampedIns) gOpDiffs = [c-f for c,f in zip(gOpClampedRets, gOpFreeRets)] for i, (s, fi, fr, ci, cr) in enumerate(zip(self.getStateIter(), gOpFreeIns, gOpFreeRets, gOpClampedIns, gOpClampedRets)): if i>0: fpiIns["free_" +s.name] = self.rho(fi+fr) fpiIns["clamped_"+s.name] = self.rho(ci+cr) allStates = TT.concatenate([s.flatten() for s in gOpFreeRets]) allDiffs = TT.concatenate([d.flatten() for d in gOpDiffs]) for t, tName in [(fpiIns[t.name], t.name) for t in self.getThetaIter()]: J = TT.jacobian(allStates, t.flatten(), disconnected_inputs="ignore") dt = J.T.dot(allDiffs).reshape(t.shape) fpiIns[tName] += fpiIns["lr"]*dt fpiIns["i"] += 1 fpiRets = [fpiIns[k] for k in self.getFPIRetNames()] return fpiRets
def _get_updates(self):
n = self.params['batch_size']
N = self.params['train_size']
prec_lik = self.params['prec_lik']
prec_prior = self.params['prec_prior']
gc_norm = self.params['gc_norm']
gamma = float(n + N) / n
# compute log-likelihood
error = self.model_outputs - self.true_outputs
logliks = log_normal(error, prec_lik)
sumloglik = logliks.sum()
# compute gradient of likelihood wrt each data point
grads = tensor.jacobian(expression=logliks, wrt=self.weights)
grads = tensor.concatenate([g.flatten(ndim=2) for g in grads], axis=1)
avg_grads = grads.mean(axis=0)
dist_grads = grads - avg_grads
# compute variance of gradient
var_grads = (1. / (n - 1)) * tensor.dot(dist_grads.T, dist_grads)
logprior = log_prior_normal(self.weights, prec_prior)
grads_prior = tensor.grad(cost=logprior, wrt=self.weights)
grads_prior = tensor.concatenate([g.flatten() for g in grads_prior])
# update Fisher information
I_t_next = (1 - 1 / self.it) * self.I_t + 1 / self.it * var_grads
# compute noise
if 'B' in self.params:
B = self.params['B']
else:
B = gamma * I_t_next * N
# B += np.eye(self.n_weights) * (10 ** -9)
B_ch = slinalg.cholesky(B)
noise = tensor.dot(((2. / tensor.sqrt(self.lr)) * B_ch),
trng.normal((self.n_weights, 1)))
# expensive inversion
inv_cond_mat = gamma * N * I_t_next + (4. / self.lr) * B
cond_mat = nlinalg.matrix_inverse(inv_cond_mat)
updates = []
updates.append((self.I_t, I_t_next))
updates.append((self.it, self.it + 1))
# update the parameters
updated_params = 2 * tensor.dot(
cond_mat, grads_prior + N * avg_grads + noise.flatten())
updated_params = updated_params.flatten()
last_row = 0
for p in self.weights:
sub_index = np.prod(p.get_value().shape)
up = updated_params[last_row:last_row + sub_index]
up = up.reshape(p.shape)
updates.append((p, up))
last_row += sub_index
return updates, sumloglik
def logdet_dinv_num(self, y):
# return debug(tt.log(sT.det(debug(tt.jacobian(self.inv(y), y), 'jacobian_inv'))), 'automatic_logdet_dinv')
return tt.sum(
debug(
tt.log(
tt.diag(debug(tt.jacobian(self.inv(y), y),
'jacobian_inv'))), 'automatic_logdet_dinv'))
def grad_wrt_input(self, inputf):
fx = theano.function([self.model.layers[0].input],
T.jacobian(self.model.layers[-1].output.flatten(),
self.model.layers[0].input),
allow_input_downcast=True)
grad = fx(inputf)
return grad
def compute_jacobian(errors, parameters):
"""
Compute jacobian.
Parameters
----------
errors : Theano variable
Computed MSE for each sample separetly.
parameters : list of Theano variable
Neural network parameters (e.g. weights, biases).
Returns
-------
Theano variable
"""
n_samples = errors.shape[0]
J = T.jacobian(errors, wrt=parameters)
jacobians = []
for jacobian, parameter in zip(J, parameters):
jacobian = jacobian.reshape((n_samples, parameter.size))
jacobians.append(jacobian)
return T.concatenate(jacobians, axis=1)
def dM2_f_i(mx, beta, hyp, X):
hyps = (hyp[:idims + 1], hyp[idims + 1])
kernel_func = partial(cov.Sum, hyps, self.covs)
k = kernel_func(mx[None, :], X).flatten()
mean = k.dot(beta)
dmean = tt.jacobian(mean.flatten(), mx)
return tt.square(dmean.flatten())
def hessian(objective, argument):
"""
Compute the directional derivative of the gradient
(which is equal to the hessian multiplied by direction).
"""
g = T.grad(objective, argument)
# Create a new tensor A, which has the same type (i.e. same dimensionality)
# as argument.
A = argument.type()
try:
# First attempt efficient 'R-op', this directly calculates the
# directional derivative of the gradient, rather than explicitly
# calculating the hessian and then multiplying.
R = T.Rop(g, argument, A)
except NotImplementedError:
shp = T.shape(argument)
H = T.jacobian(g.flatten(), argument).reshape(
T.concatenate([shp, shp]), 2*A.ndim)
R = T.tensordot(H, A, A.ndim)
try:
hess = theano.function([argument, A], R, on_unused_input='raise')
except theano.compile.UnusedInputError:
warn('Theano detected unused input - suggests hessian may be zero or '
'constant.')
hess = theano.function([argument, A], R, on_unused_input='ignore')
return hess
def auto4check2(input, dataset):
a = theano.shared(value=dataset[0], name="a")
b = theano.shared(value=dataset[1], name="b")
c = theano.shared(value=dataset[2], name="c")
x = T.vector('x')
u = x[0] - 0.8
v = x[1] - (a[0] + a[1] * u ** 2 * (1 - u) ** 0.5 - a[2] * u)
alpha = -b[0] + b[1] * u ** 2 * (1 + u) ** 0.5 + b[2] * u
beta = c[0] * v ** 2 * (1 - c[1] * v) / (1 + c[2] * u ** 2)
fx = alpha * np.e ** (-beta)
g_f_x = T.jacobian(fx, x)
grad = theano.function([x], g_f_x)
Hessian = theano.function([x], T.hessian(fx, x))
H_alpha_x = theano.function([x], T.hessian(alpha, x))
H_beta_x = theano.function([x], T.hessian(beta, x))
J_f_alpha = theano.function([x], T.grad(fx, alpha))
J_f_beta = theano.function([x], T.grad(fx, beta))
J_alpha_x = theano.function([x], T.grad(alpha, x))
J_beta_x = theano.function([x], T.grad(beta, x))
J_f_y = [J_f_alpha(input), J_f_beta(input)]
J_y_x = [J_alpha_x(input), J_beta_x(input)]
# print "H_alpha_x"
# print H_alpha_x(input)
# print "H_beta_x"
# print H_beta_x(input)
# print "J_f_y"
# print J_f_y
# print "J_y_x"
# print J_y_x
# print grad(input)
return Hessian(input)
def compile_tan_force(self, u_np, s_np, *args, **kargs):
grid = u_np.grid
grid_math = grid._math
grid._math = T
tensor_dim = u_np.ndim + 2
input_data = T.TensorType('float64', (False,) * tensor_dim)()
tensor_dim = s_np.ndim
param = T.TensorType('float64', (False,) * tensor_dim)()
#param = T.dvector('s')
u_theano = grid.array(input_data.copy(), u_np.shape)
s_theano = np.array(param.copy(), s_np.shape)
ret = self._function(u_theano, s_theano, *args, **kargs)
out_tan = T.jacobian(ret._data, param)
if _VERBOSE_: print('tangent derived in theano mode, compiling')
f = theano.function([input_data, param], [out_tan])
if _VERBOSE_: print('tangent sucessfully compiled')
grid._math = grid_math
return f
def hypernet_elbo(X, y, loglik_primary_f, logprior_f, hypernet_f, z_noise, N,
log_det_dtheta_dz_f=None):
assert(X.ndim == 2 and y.ndim == 2)
assert(z_noise.ndim == 1)
B = X.shape[0]
rescale = float(N) / B # Ensure not integer division
theta = hypernet_f(z_noise)
loglik = loglik_primary_f(X, y, theta)
assert(loglik.ndim == 1)
loglik_total = T.sum(loglik)
assert(loglik_total.ndim == 0)
logprior_theta = logprior_f(theta)
assert(logprior_theta.ndim == 0)
if log_det_dtheta_dz_f is None: # This is slower, but good for testing
assert(theta.ndim == 1) # Use vector theta for this mode
J = T.jacobian(theta, z_noise)
penalty = log_abs_det_T(J)
else:
penalty = log_det_dtheta_dz_f(z_noise)
assert(penalty.ndim == 0)
logprior_z = 0.5 * T.dot(z_noise, z_noise)
assert(logprior_z.ndim == 0)
elbo = rescale * loglik_total + logprior_theta + penalty + logprior_z
return elbo
def test_flow_det(flow_spec):
z0 = tt.arange(0, 20).astype('float32')
flow = flow_spec(dim=20, z0=z0.dimshuffle('x', 0))
with change_flags(compute_test_value='off'):
z1 = flow.forward.flatten()
J = tt.jacobian(z1, z0)
logJdet = tt.log(tt.abs_(tt.nlinalg.det(J)))
det = flow.logdet[0]
np.testing.assert_allclose(logJdet.eval(), det.eval(), atol=0.0001)
def test_flow_det(flow_spec):
z0 = tt.arange(0, 20).astype('float32')
flow = flow_spec(dim=20, z0=z0.dimshuffle('x', 0))
with change_flags(compute_test_value='off'):
z1 = flow.forward.flatten()
J = tt.jacobian(z1, z0)
logJdet = tt.log(tt.abs_(tt.nlinalg.det(J)))
det = flow.logdet[0]
np.testing.assert_allclose(logJdet.eval(), det.eval(), atol=0.0001)
def test002_jacobian_matrix():
x = tensor.matrix()
y = 2 * x.sum(axis=0)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
ev = numpy.zeros((10, 10, 10))
for dx in xrange(10):
ev[dx, :, dx] = 2.
# test when the jacobian is called with a tensor as wrt
Jx = tensor.jacobian(y, x)
f = theano.function([x], Jx)
vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
assert numpy.allclose(f(vx), ev)
# test when the jacobian is called with a tuple as wrt
Jx = tensor.jacobian(y, (x,))
assert isinstance(Jx, tuple)
f = theano.function([x], Jx[0])
vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
assert numpy.allclose(f(vx), ev)
# test when the jacobian is called with a list as wrt
Jx = tensor.jacobian(y, [x])
assert isinstance(Jx, list)
f = theano.function([x], Jx[0])
vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
assert numpy.allclose(f(vx), ev)
# test when the jacobian is called with a list of two elements
z = tensor.matrix()
y = (x * z).sum(axis=1)
Js = tensor.jacobian(y, [x, z])
f = theano.function([x, z], Js)
vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
vz = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
vJs = f(vx, vz)
evx = numpy.zeros((10, 10, 10))
evz = numpy.zeros((10, 10, 10))
for dx in xrange(10):
evx[dx, dx, :] = vx[dx, :]
evz[dx, dx, :] = vz[dx, :]
assert numpy.allclose(vJs[0], evz)
assert numpy.allclose(vJs[1], evx)
def get_gradients(self, model, data, ** kwargs):
space, sources = self.get_data_specs(model)
space.validate(data)
X, Y = data
theano_rng = RandomStreams(seed = model.rng.randint(2 ** 15))
noise = theano_rng.random_integers(size = (X.shape[0] * model.k,), low=0, high = model.dict_size - 1)
delta = model.delta(data)
p = model.score(X, Y)
params = model.get_params()
pos_ = T.jacobian(model.score(X, Y), params, disconnected_inputs='ignore')
pos_coeff = 1 - T.nnet.sigmoid(model.delta(data))
pos = []
for param in pos_:
axes = [0]
axes.extend(['x' for item in range(param.ndim - 1)])
pos.append(pos_coeff.dimshuffle(axes) * param)
del pos_, pos_coeff
noise_x = T.tile(X, (model.k, 1))
neg_ = T.jacobian(model.score(noise_x, noise), params, disconnected_inputs='ignore')
neg_coeff = T.nnet.sigmoid(model.delta((noise_x, noise)))
neg = []
for param in neg_:
axes = [0]
axes.extend(['x' for item in range(param.ndim - 1)])
tmp = neg_coeff.dimshuffle(axes) * param
new_shape = [X.shape[0], model.k]
new_shape.extend([tmp.shape[i] for i in range(1, tmp.ndim)])
neg.append(tmp.reshape(new_shape).sum(axis=1))
del neg_, neg_coeff
grads = [(pos_ - neg_).mean(axis=0) for pos_, neg_ in zip(pos, neg)]
gradients = OrderedDict(izip(params, grads))
updates = OrderedDict()
return gradients, updates
def get_stat(f, thetahat):
fhat = theano.function([theta], f)(thetahat)
dfhat = theano.function([theta], T.jacobian(f, [theta])[0])(thetahat)
fhatcov = np.dot(np.dot(dfhat, covhat), dfhat.transpose())
try:
fse = np.sqrt(np.diag(fhatcov))
except:
fse = np.sqrt(fhatcov)
ftstat = fhat/fse
return fhat, fse, ftstat
def test_jacobian_matrix():
x = tensor.matrix()
y = 2 * x.sum(axis=0)
rng = np.random.RandomState(seed=utt.fetch_seed())
ev = np.zeros((10, 10, 10))
for dx in range(10):
ev[dx, :, dx] = 2.0
# test when the jacobian is called with a tensor as wrt
Jx = tensor.jacobian(y, x)
f = theano.function([x], Jx)
vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
assert np.allclose(f(vx), ev)
# test when the jacobian is called with a tuple as wrt
Jx = tensor.jacobian(y, (x,))
assert isinstance(Jx, tuple)
f = theano.function([x], Jx[0])
vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
assert np.allclose(f(vx), ev)
# test when the jacobian is called with a list as wrt
Jx = tensor.jacobian(y, [x])
assert isinstance(Jx, list)
f = theano.function([x], Jx[0])
vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
assert np.allclose(f(vx), ev)
# test when the jacobian is called with a list of two elements
z = tensor.matrix()
y = (x * z).sum(axis=1)
Js = tensor.jacobian(y, [x, z])
f = theano.function([x, z], Js)
vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
vz = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
vJs = f(vx, vz)
evx = np.zeros((10, 10, 10))
evz = np.zeros((10, 10, 10))
for dx in range(10):
evx[dx, dx, :] = vx[dx, :]
evz[dx, dx, :] = vz[dx, :]
assert np.allclose(vJs[0], evz)
assert np.allclose(vJs[1], evx)
def test_vectors(self):
try:
import theano.tensor as T
from theano import function
except:
return
for MT in [False, True]:
# Set up variables and function
vals = [np.random.randn(20) for i in range(5)]
f = lambda a, b, c, d, e: a + (b * c) - d**e
# Set up our objects
Cs = [ch.Ch(v) for v in vals]
C_result = f(*Cs)
C_result.MT = MT
# Set up Theano equivalents
Ts = T.dvectors('T1', 'T2', 'T3', 'T4', 'T5')
TF = f(*Ts)
T_result = function(Ts, TF)
if False:
import theano.gradient
which = 1
theano_sse = (TF**2.).sum()
theano_grad = theano.gradient.grad(theano_sse, Ts[which])
theano_fn = function(Ts, theano_grad)
print(theano_fn(*vals))
C_result_grad = ch.SumOfSquares(C_result).dr_wrt(Cs[which])
print(C_result_grad)
# if True:
# aaa = np.linalg.solve(C_result_grad.T.dot(C_result_grad), C_result_grad.dot(np.zeros(C_result_grad.shape[1])))
# theano_hes = theano.R_obbb = theano.R_op()
import pdb
pdb.set_trace()
# Make sure values and derivatives are equal
np.testing.assert_array_equal(C_result.r, T_result(*vals))
for k in range(len(vals)):
theano_derivative = function(Ts, T.jacobian(TF, Ts[k]))(*vals)
our_derivative = np.array(C_result.dr_wrt(Cs[k]).todense())
#print(theano_derivative, our_derivative)
# Theano produces has more nans than we do during exponentiation.
# So we test only on entries where Theano is without NaN's
without_nans = np.nonzero(
np.logical_not(np.isnan(theano_derivative.flatten())))[0]
np.testing.assert_array_equal(
theano_derivative.flatten()[without_nans],
our_derivative.flatten()[without_nans])
def estimate_fisher(outputs, n_outputs, parameters):
# shape (sample_size, n_outputs, #parameters)
grads = T.stack(*[util.batched_flatcat(
T.jacobian(outputs[:, j], parameters))
for j in xrange(n_outputs)])
# ravel the batch and output axes so that the product will sum
# over the outputs *and* over the batch. divide by the batch
# size to get the batch mean.
grads = grads.reshape((grads.shape[0] * grads.shape[1], grads.shape[2]))
fisher = T.dot(grads.T, grads) / grads.shape[0]
return fisher
def Hessian(objective, *Vars, **kwargs):
"""block structure matrix of Jacobian of gradients, symmetric"""
return T.concatenate([
T.concatenate([
T.jacobian(T.grad(objective, var1,
disconnected_inputs='ignore').reshape((-1, )),
var2,
disconnected_inputs='ignore').reshape(
(var1.size, var2.size)) for var2 in Vars
],
axis=1) for var1 in Vars
],
axis=0)
def Hessian(objective, *Vars, **kwargs):
return T.concatenate([
T.concatenate([
T.jacobian(
T.grad(objective, var1, disconnected_inputs='ignore').reshape(
(T.prod(var1.shape), )),
var2,
disconnected_inputs='ignore').reshape(
(T.prod(var1.shape), T.prod(var2.shape))) for var2 in Vars
],
axis=1) for var1 in Vars
],
axis=0)
def test_jacobian_vector():
x = tensor.vector()
y = x * 2
rng = np.random.RandomState(seed=utt.fetch_seed())
# test when the jacobian is called with a tensor as wrt
Jx = tensor.jacobian(y, x)
f = theano.function([x], Jx)
vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
assert np.allclose(f(vx), np.eye(10) * 2)
# test when the jacobian is called with a tuple as wrt
Jx = tensor.jacobian(y, (x,))
assert isinstance(Jx, tuple)
f = theano.function([x], Jx[0])
vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
assert np.allclose(f(vx), np.eye(10) * 2)
# test when the jacobian is called with a list as wrt
Jx = tensor.jacobian(y, [x])
assert isinstance(Jx, list)
f = theano.function([x], Jx[0])
vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
assert np.allclose(f(vx), np.eye(10) * 2)
# test when the jacobian is called with a list of two elements
z = tensor.vector()
y = x * z
Js = tensor.jacobian(y, [x, z])
f = theano.function([x, z], Js)
vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
vz = rng.uniform(size=(10,)).astype(theano.config.floatX)
vJs = f(vx, vz)
evx = np.zeros((10, 10))
evz = np.zeros((10, 10))
np.fill_diagonal(evx, vx)
np.fill_diagonal(evz, vz)
assert np.allclose(vJs[0], evz)
assert np.allclose(vJs[1], evx)
def test001_jacobian_vector():
x = tensor.vector()
y = x * 2
rng = numpy.random.RandomState(seed=utt.fetch_seed())
# test when the jacobian is called with a tensor as wrt
Jx = tensor.jacobian(y, x)
f = theano.function([x], Jx)
vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
assert numpy.allclose(f(vx), numpy.eye(10) * 2)
# test when the jacobian is called with a tuple as wrt
Jx = tensor.jacobian(y, (x,))
assert isinstance(Jx, tuple)
f = theano.function([x], Jx[0])
vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
assert numpy.allclose(f(vx), numpy.eye(10) * 2)
# test when the jacobian is called with a list as wrt
Jx = tensor.jacobian(y, [x])
assert isinstance(Jx, list)
f = theano.function([x], Jx[0])
vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
assert numpy.allclose(f(vx), numpy.eye(10) * 2)
# test when the jacobian is called with a list of two elements
z = tensor.vector()
y = x * z
Js = tensor.jacobian(y, [x, z])
f = theano.function([x, z], Js)
vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
vz = rng.uniform(size=(10,)).astype(theano.config.floatX)
vJs = f(vx, vz)
evx = numpy.zeros((10, 10))
evz = numpy.zeros((10, 10))
numpy.fill_diagonal(evx, vx)
numpy.fill_diagonal(evz, vz)
assert numpy.allclose(vJs[0], evz)
assert numpy.allclose(vJs[1], evx)
def test_vectors(self):
try:
import theano.tensor as T
from theano import function
except:
return
for MT in [False, True]:
# Set up variables and function
vals = [np.random.randn(20) for i in range(5)]
f = lambda a, b, c, d, e : a + (b * c) - d ** e
# Set up our objects
Cs = [ch.Ch(v) for v in vals]
C_result = f(*Cs)
C_result.MT = MT
# Set up Theano equivalents
Ts = T.dvectors('T1', 'T2', 'T3', 'T4', 'T5')
TF = f(*Ts)
T_result = function(Ts, TF)
if False:
import theano.gradient
which = 1
theano_sse = (TF**2.).sum()
theano_grad = theano.gradient.grad(theano_sse, Ts[which])
theano_fn = function(Ts, theano_grad)
print theano_fn(*vals)
C_result_grad = ch.SumOfSquares(C_result).dr_wrt(Cs[which])
print C_result_grad
# if True:
# aaa = np.linalg.solve(C_result_grad.T.dot(C_result_grad), C_result_grad.dot(np.zeros(C_result_grad.shape[1])))
# theano_hes = theano.R_obbb = theano.R_op()
import pdb; pdb.set_trace()
# Make sure values and derivatives are equal
np.testing.assert_array_equal(C_result.r, T_result(*vals))
for k in range(len(vals)):
theano_derivative = function(Ts, T.jacobian(TF, Ts[k]))(*vals)
our_derivative = np.array(C_result.dr_wrt(Cs[k]).todense())
#print theano_derivative, our_derivative
# Theano produces has more nans than we do during exponentiation.
# So we test only on entries where Theano is without NaN's
without_nans = np.nonzero(np.logical_not(np.isnan(theano_derivative.flatten())))[0]
np.testing.assert_array_equal(theano_derivative.flatten()[without_nans], our_derivative.flatten()[without_nans])
def test_flow_det_local(flow_spec):
z0 = tt.arange(0, 12).astype('float32')
spec = flow_spec.cls.get_param_spec_for(d=12)
params = dict()
for k, shp in spec.items():
params[k] = np.random.randn(1, *shp).astype('float32')
flow = flow_spec(dim=12, z0=z0.reshape((1, 1, 12)), **params)
assert flow.batched
with change_flags(compute_test_value='off'):
z1 = flow.forward.flatten()
J = tt.jacobian(z1, z0)
logJdet = tt.log(tt.abs_(tt.nlinalg.det(J)))
det = flow.logdet[0]
np.testing.assert_allclose(logJdet.eval(), det.eval(), atol=0.0001)
def get_fisher_mat():
grad2d = []
for p in self.model.params:
grad2d += [T.jacobian(self.f_loss_samples, p)]
if grad2d[-1].ndim == 2:
grad2d[-1] = grad2d[-1].dimshuffle(0, 1, 'x')
grad2d_vec = T.concatenate([g.flatten(2).T for g in grad2d]).T
# tensor wise: F_p,i,j = sum_k grad2d[p,i,k]*grad2d[p,k,j]
# just a slow reference implementation of what is below
# F = T.mean(T.batched_dot(grad2d_vec.dimshuffle(0, 1, 'x'), grad2d_vec.dimshuffle(0, 'x', 1)), 0)/self.over_sampling
F = T.dot(grad2d_vec.T, grad2d_vec)/T.cast(grad2d_vec.shape[0], theano.config.floatX)/self.over_sampling
return F
def test_flow_det_local(flow_spec):
z0 = tt.arange(0, 12).astype('float32')
spec = flow_spec.cls.get_param_spec_for(d=12)
params = dict()
for k, shp in spec.items():
params[k] = np.random.randn(1, *shp).astype('float32')
flow = flow_spec(dim=12, z0=z0.reshape((1, 1, 12)), **params)
assert flow.batched
with change_flags(compute_test_value='off'):
z1 = flow.forward.flatten()
J = tt.jacobian(z1, z0)
logJdet = tt.log(tt.abs_(tt.nlinalg.det(J)))
det = flow.logdet[0]
np.testing.assert_allclose(logJdet.eval(), det.eval(), atol=0.0001)
def jacobian_vector(expr, wrt):
"""Computes the Jacobian of a vector expression with respect to varaibles.
Args:
expr: Vector Theano tensor expression.
wrt: List of Theano variables.
Returns:
Theano tensor.
"""
try:
return _tensor_map(lambda f: jacobian_scalar(f, wrt), expr)
except ValueError:
# Fallback for wider support.
return T.stack([T.jacobian(expr, wrt, disconnected_inputs="ignore")])
def test003_jacobian_scalar():
x = tensor.scalar()
y = x * 2
rng = numpy.random.RandomState(seed=utt.fetch_seed())
# test when the jacobian is called with a tensor as wrt
Jx = tensor.jacobian(y, x)
f = theano.function([x], Jx)
vx = numpy.cast[theano.config.floatX](rng.uniform())
assert numpy.allclose(f(vx), 2)
# test when the jacobian is called with a tuple as wrt
Jx = tensor.jacobian(y, (x,))
assert isinstance(Jx, tuple)
f = theano.function([x], Jx[0])
vx = numpy.cast[theano.config.floatX](rng.uniform())
assert numpy.allclose(f(vx), 2)
# test when the jacobian is called with a list as wrt
Jx = tensor.jacobian(y, [x])
assert isinstance(Jx, list)
f = theano.function([x], Jx[0])
vx = numpy.cast[theano.config.floatX](rng.uniform())
assert numpy.allclose(f(vx), 2)
# test when the jacobian is called with a list of two elements
z = tensor.scalar()
y = x * z
Jx = tensor.jacobian(y, [x, z])
f = theano.function([x, z], Jx)
vx = numpy.cast[theano.config.floatX](rng.uniform())
vz = numpy.cast[theano.config.floatX](rng.uniform())
vJx = f(vx, vz)
assert numpy.allclose(vJx[0], vz)
assert numpy.allclose(vJx[1], vx)
def test_jacobian_scalar():
x = tensor.scalar()
y = x * 2
rng = np.random.RandomState(seed=utt.fetch_seed())
# test when the jacobian is called with a tensor as wrt
Jx = tensor.jacobian(y, x)
f = theano.function([x], Jx)
vx = np.cast[theano.config.floatX](rng.uniform())
assert np.allclose(f(vx), 2)
# test when the jacobian is called with a tuple as wrt
Jx = tensor.jacobian(y, (x,))
assert isinstance(Jx, tuple)
f = theano.function([x], Jx[0])
vx = np.cast[theano.config.floatX](rng.uniform())
assert np.allclose(f(vx), 2)
# test when the jacobian is called with a list as wrt
Jx = tensor.jacobian(y, [x])
assert isinstance(Jx, list)
f = theano.function([x], Jx[0])
vx = np.cast[theano.config.floatX](rng.uniform())
assert np.allclose(f(vx), 2)
# test when the jacobian is called with a list of two elements
z = tensor.scalar()
y = x * z
Jx = tensor.jacobian(y, [x, z])
f = theano.function([x, z], Jx)
vx = np.cast[theano.config.floatX](rng.uniform())
vz = np.cast[theano.config.floatX](rng.uniform())
vJx = f(vx, vz)
assert np.allclose(vJx[0], vz)
assert np.allclose(vJx[1], vx)
def initialize_calc_ll_gmm_hist_fun(self):
meansvec = T.dvector('means')
covarsvec = T.dvector('covars')
weights = T.dvector('weights')
gm_num = weights.shape[0]
means = T.reshape(meansvec, (gm_num, meansvec.shape[0] / gm_num))
covars = T.reshape(covarsvec, (gm_num, meansvec.shape[0] / gm_num))
Yp = T.dmatrix('Yp')
Yn = T.dmatrix('Yn')
p_p,r_p,p_p_m = self.calc_ll_gmm(Yp, means, covars, weights)
p_n,r_n,p_n_m = self.calc_ll_gmm(Yn, means, covars, weights)
L, hmax, hmin, hn, hp = self.calc_hist_loss_vector(p_n, p_p)
dL = T.jacobian(L, [meansvec, covarsvec, weights, Yp, Yn])
self.gmmhist_df = function([meansvec, covarsvec, weights, Yp, Yn], dL, allow_input_downcast=True)
self.gmmhist_f = function([meansvec, covarsvec, weights, Yp, Yn], [L, hmax, hmin, hn, hp], allow_input_downcast=True)
def grad(self, inputs, dCdf):
""" Gradient MTF
"""
MU = inputs[0][0]
SD = inputs[0][1]
# Y = self._normal(just_return = True, MU=MU, SD=SD)
Y, Y_upd = th.scan(fn=self.norm_fun,
sequences=self.counter, non_sequences=[MU, SD])
dYdMIn = T.jacobian(Y.sum(axis=0), inputs[0])
# dYdSD = T.jacobian(Y, SD)
# return dYdMIn[0]*dCdf[0][0] + dYdMIn[1]*dCdf[0][1],
# return T.as_tensor([dCdf[0][0]*dYdMIn[0][0] + dCdf[0][1]*dYdMIn[1][0],
# dCdf[0][0]*dYdMIn[0][1] + dCdf[0][1]*dYdMIn[1][1]]),
return T.as_tensor([dCdf[0].dot(dYdMIn[:,0]), dCdf[0].dot(dYdMIn[:,1])]),
def L_op(self, inputs, output, grads):
# from IPython import embed; embed()
if not hasattr(self, 'precomputed_grads'):
grad_integrators = T.jacobian(self._expr, self._extra_vars)
self.precomputed_grads = [
IntegrateVectorizedGeneralized(gi, self._var, self.bins,
*self._extra_vars)
for gi in grad_integrators
]
out, = grads
dargs = []
for integrate in self.precomputed_grads:
darg = T.dot(out, integrate(*inputs))
# print(darg)
dargs.append(darg)
return dargs
def _get_updates(self):
n = self.params['batch_size']
N = self.params['train_size']
prec_lik = self.params['prec_lik']
prec_prior = self.params['prec_prior']
gc_norm = self.params['gc_norm']
alpha = self.params['alpha']
mu = self.params['mu']
use_gamma = self.params['use_gamma']
# compute log-likelihood
error = self.model_outputs - self.true_outputs
logliks = log_normal(error, prec_lik)
sumloglik = logliks.sum()
meanloglik = sumloglik / n
# compute gradients
grads = tensor.grad(cost=meanloglik, wrt=self.weights)
# update preconditioning matrix
V_t_next = [
alpha * v + (1 - alpha) * g * g for g, v in zip(grads, self.V_t)
]
G_t = [1. / (mu + tensor.sqrt(v)) for v in V_t_next]
logprior = log_prior_normal(self.weights, prec_prior)
grads_prior = tensor.grad(cost=logprior, wrt=self.weights)
updates = []
[updates.append((v, v_n)) for v, v_n in zip(self.V_t, V_t_next)]
for p, g, gp, gt in zip(self.weights, grads, grads_prior, G_t):
# inject noise
noise = tensor.sqrt(self.lr * gt) * trng.normal(p.shape)
if use_gamma:
# compute gamma
gamma = nlinalg.extract_diag(
tensor.jacobian(gt.flatten(), p).flatten(ndim=2))
gamma = gamma.reshape(p.shape)
updates.append((p, p + 0.5 * self.lr *
((gt * (gp + N * g)) + gamma) + noise))
else:
updates.append(
(p, p + 0.5 * self.lr * (gt * (gp + N * g)) + noise))
return updates, sumloglik
def grad(self, inputs, g_outputs):
[gz] = g_outputs
[A] = inputs
v = self(A)
dexp = T.jacobian(self.exp(v).flatten(), v)
invdexp = T.nlinalg.matrix_inverse(
dexp.reshape((
A.shape[0] * A.shape[1],
v.shape[0] * v.shape[1],
))).reshape((
A.shape[0],
A.shape[1],
v.shape[0],
v.shape[1],
))
return [T.tensordot(gz, invdexp, ((0, 1), (0, 1)))]
def __init__(self, mode='matrix', exp=None, LAtoV=None, VtoLA=None):
assert mode in ['matrix', 'zeroest', 'nearest']
self.mode = mode
if exp is None:
exp = T.slinalg.Expm()
self.exp = exp
self.LAtoVf = None
self.VtoLAf = None
self.lossf = None
self.dlossf = None
if mode != 'matrix':
g = T.matrix()
hatxi = T.vector()
xi = T.matrix()
self.LAtoVf = theano.function([xi], LAtoV(xi))
self.VtoLAf = theano.function([hatxi], VtoLA(hatxi))
loss = lambda hatxi, g: T.sum((exp(VtoLA(hatxi)) - g)**2)
dloss = lambda hatxi, g: T.jacobian(loss(hatxi, g), hatxi)
self.lossf = theano.function([hatxi, g], loss(hatxi, g))
self.dlossf = theano.function([hatxi, g], dloss(hatxi, g))
def get_order_n_pole(order):
"""Generate function to calculate the Fourier transform `order`-order pole.
The Fourier transform of :math:`{(z-ϵ)}^{n}` is calculate, where `ϵ` is the
position of the pole and `n` the order of the pole.
Parameters
----------
order : int
The order of the pole
Returns
-------
order_n_pole : Callable
The function (tau, pole, beta)->gf_tau calculating the Fourier transform.
"""
import theano
import theano.tensor as T
from theano.ifelse import ifelse
from math import factorial
pole = T.dscalar('pole')
beta = T.dscalar('beta')
# tau = T.dscalar('tau')
tau = T.dscalar('tau')
fermi_fct = (1 + T.tanh(-beta*pole/2))/2
gf_tau = ifelse(
pole > 0, # avoid overflows asserting negative exponent
-(1 - fermi_fct)*T.exp(-pole*tau),
-fermi_fct*T.exp(pole*(beta-tau)),
)
n_gf_tau = gf_tau
for __ in range(order-1):
# n_gf_tau = T.grad(n_gf_tau, pole)
n_gf_tau = T.jacobian(n_gf_tau, pole)
n_gf_tau = n_gf_tau / factorial(order-1)
# resuts, __ = theano.scan(n_gf_tau.)
func = theano.function([tau, pole, beta], n_gf_tau)
return np.vectorize(func, otypes=[np.float])
def compute_hessian(self, objective, argument):
"""
Computes the directional derivative of the gradient (which is equal to
the Hessian multiplied by direction).
"""
g = T.grad(objective, argument)
# Create a new tensor A, which has the same type (i.e. same
# dimensionality) as argument.
try:
A = argument.type()
except AttributeError:
# Assume we are on the product manifold
A = [arg.type() for arg in argument]
try:
# First attempt efficient 'R-op', this directly calculates the
# directional derivative of the gradient, rather than explicitly
# calculating the Hessian and then multiplying.
R = T.Rop(g, argument, A)
except NotImplementedError:
# TODO: fix this fallback for the product manifold.
shp = T.shape(argument)
H = T.jacobian(g.flatten(),
argument).reshape(T.concatenate([shp, shp]),
2 * A.ndim)
R = T.tensordot(H, A, A.ndim)
try:
hess = theano.function([argument, A], R, on_unused_input="warn")
except TypeError:
hess_prod = theano.function(argument + A,
R,
on_unused_input="warn")
def hess(x, a):
return hess_prod(*(x + a))
return hess
def compute_hessian(self, objective, argument):
"""
Computes the directional derivative of the gradient (which is equal to
the Hessian multiplied by direction).
"""
g = T.grad(objective, argument)
# Create a new tensor A, which has the same type (i.e. same
# dimensionality) as argument.
try:
A = argument.type()
except AttributeError:
# Assume we are on the product manifold
A = [arg.type() for arg in argument]
try:
# First attempt efficient 'R-op', this directly calculates the
# directional derivative of the gradient, rather than explicitly
# calculating the Hessian and then multiplying.
R = T.Rop(g, argument, A)
except NotImplementedError:
# TODO: fix this fallback for the product manifold.
shp = T.shape(argument)
H = T.jacobian(g.flatten(), argument).reshape(
T.concatenate([shp, shp]), 2 * A.ndim)
R = T.tensordot(H, A, A.ndim)
try:
hess = theano.function([argument, A], R, on_unused_input="warn")
except TypeError:
hess_prod = theano.function(argument + A, R,
on_unused_input="warn")
def hess(x, a):
return hess_prod(*(x + a))
return hess
def auto4check(dataset, x, tol=1e-9, maxiter=1000):
t0 = theano.shared(value=dataset[0], name="t0")
a0 = theano.shared(value=dataset[1], name="a0")
b0 = theano.shared(value=dataset[2], name="b0")
c0 = theano.shared(value=dataset[3], name="c0")
k = T.vector('k')
a_t = np.e ** (-(k[0] + k[1]) * t0)
b_t = k[0] / (k[0] + k[1]) * (1 - a_t)
c_t = k[1] / (k[0] + k[1]) * (1 - a_t)
f = T.sum((a0 - a_t) ** 2 + (b0 - b_t) ** 2 + (c0 - c_t) ** 2)
F = theano.function([k], f)
g_f_k = T.jacobian(f, k)
j_f_k = theano.function([k], g_f_k)
H_f_k = T.hessian(f, k)
Hessian = theano.function([k], H_f_k)
track, f_val = [], []
track.append(array(x))
f_val.append(F(x))
g = j_f_k(x)
i = 0
print "Step =", i, "g=", g, "x=", x, "loss=", F(x)
while norm(g) > tol:
i += 1
if i > maxiter:
break
G = Hessian(x)
s = -np.linalg.solve(G, g)
x += s
track.append(array(x))
f_val.append(F(x))
g = j_f_k(x)
print "step =", i, "g=", g, "x=", x, "loss=", F(x), "G=", G
return x, F(x), track, f_val
def grad_hess(objective, argument):
"""
Compute both the gradient and the directional derivative of the gradient
(which is equal to the hessian multiplied by direction).
"""
# TODO: Check that the hessian calculation is correct!
# TODO: Make this compatible with non-matrix manifolds.
g = T.grad(objective, argument)
grad = compile(g, argument)
# Create a new tensor A, which has the same type (i.e. same dimensionality)
# as argument.
A = argument.type()
try:
# First attempt efficient 'R-op', this directly calculates the
# directional derivative of the gradient, rather than explicitly
# calculating the hessian and then multiplying.
print("begins")
sys.stdout.flush()
R = T.Rop(g, argument, A)
print("ends")
sys.stdout.flush()
except NotImplementedError:
# This will break if the manifold is not a matrix.
n, p = T.shape(argument)
H = T.jacobian(g.flatten(), argument).reshape([n, p, n, p], 4)
R = T.tensordot(H, A)
try:
hess = theano.function([argument, A], R)
except theano.compile.UnusedInputError:
warn('Theano detected unused input - suggests hessian may be zero or '
'constant.')
hess = theano.function([argument, A], R, on_unused_input='ignore')
return grad, hess
input_duration = T.scalar('input_duration')
input_intensity = T.scalar('input_intensity')
P = input_intensity * ((T.sgn(input_duration - t) + 1) / 2)
corrected_sigmoid = \
1 / (1 + T.exp(-T.mul(a,(T.dot(c,activation) + P) - theta))) \
- 1 / (1 + T.exp(np.multiply(a, theta)))
#corrected_sigmoid = theano.function([t, activation, input_duration, input_intensity], corrected_s)
#d_a = T.true_div(-activation + T.mul( 1 - T.mul(r, activation), corrected_s), tau)
d_a = T.true_div(-activation + T.mul( k - T.mul(r, activation), corrected_sigmoid), tau)
d_activation = theano.function(inputs=[activation, t, input_duration, input_intensity],
outputs=d_a, on_unused_input='warn')
J = theano.function(inputs=[activation, t, input_duration, input_intensity],
outputs=T.jacobian(d_a, activation), on_unused_input='warn')
activation_0 = np.array([0, 0])
t_0 = 0
t_1 = .125
dt = .0001
times = np.arange(t_0, t_1, dt)
intens = 1
duration = .125
params = (duration, intens)
#r = ode(d_activation).set_integrator('vode')
#r.set_initial_value(activation_0, t_0).set_f_params(*params)
timeseries = odeint(d_activation, activation_0, times, params, Dfun=J)
def __init__(self, params, sx2 = 1, linear_model = False, samples = 20, use_hat = False):
ker, self.samples, self.params, self.KmmInv = kernel(), samples, params, {}
self.use_hat = use_hat
model_file_name = 'model' + ('_hat' if use_hat else '') + ('_linear' if linear_model else '') + '.save'
try:
print 'Trying to load model...'
with open(model_file_name, 'rb') as file_handle:
obj = cPickle.load(file_handle)
self.f, self.g, self.f_Kmm, self.f_KmmInv, self.dKmm_d = obj
self.update_KmmInv_cache()
print 'Loaded!'
return
except:
print 'Failed. Creating a new model...'
Y, Z, m, ls, mu, lL, eps_MK, eps_NQ, eps_NK, KmmInv = T.dmatrices('Y', 'Z', 'm', 'ls', 'mu',
'lL', 'eps_MK', 'eps_NQ', 'eps_NK', 'KmmInv')
lhyp = T.dvector('lhyp')
(M, K), N, Q = mu.shape, m.shape[0], Z.shape[1]
s, sl2, sf2, l = T.exp(ls), T.exp(lhyp[0]), T.exp(lhyp[1]), T.exp(lhyp[2:2+Q])
L = T.tril(lL - T.diag(T.diag(lL)) + T.diag(T.exp(T.diag(lL))))
print 'Setting up cache...'
Kmm = ker.RBF(sf2, l, Z) if not linear_model else ker.LIN(sl2, Z)
KmmInv_cache = sT.matrix_inverse(Kmm)
self.f_Kmm = theano.function([Z, lhyp], Kmm, name='Kmm')
self.f_KmmInv = theano.function([Z, lhyp], KmmInv_cache, name='KmmInv_cache')
self.update_KmmInv_cache()
self.dKmm_d = {'Z': theano.function([Z, lhyp], T.jacobian(Kmm.flatten(), Z), name='dKmm_dZ'),
'lhyp': theano.function([Z, lhyp], T.jacobian(Kmm.flatten(), lhyp), name='dKmm_dlhyp')}
print 'Setting up model...'
if not self.use_hat:
mu_scaled, L_scaled = sf2**0.5 * mu, sf2**0.5 * L
X = m + s * eps_NQ
U = mu_scaled + L_scaled.dot(eps_MK)
Kmn = ker.RBF(sf2, l, Z, X) if not linear_model else ker.LIN(sl2, Z, X)
Knn = ker.RBFnn(sf2, l, X) if not linear_model else ker.LINnn(sl2, X)
A = KmmInv.dot(Kmn)
B = Knn - T.sum(Kmn * KmmInv.dot(Kmn), 0)
F = A.T.dot(U) + T.maximum(B, 1e-16)[:,None]**0.5 * eps_NK
F = T.concatenate((T.zeros((N,1)), F), axis=1)
S = T.nnet.softmax(F)
LS = T.sum(T.log(T.maximum(T.sum(Y * S, 1), 1e-16)))
if not linear_model:
KL_U = -0.5 * (T.sum(KmmInv.T * T.sum(mu_scaled[:,None,:]*mu_scaled[None,:,:], 2))
+ K * (T.sum(KmmInv.T * L_scaled.dot(L_scaled.T)) - M - 2.0*T.sum(T.log(T.diag(L_scaled)))
+ 2.0*T.sum(T.log(T.diag(sT.cholesky(Kmm))))))
else:
KL_U = 0
#KL_U = -0.5 * T.sum(T.sum(mu_scaled * KmmInv.dot(mu_scaled), 0) + T.sum(KmmInv * L_scaled.dot(L_scaled.T)) - M
# - 2.0*T.sum(T.log(T.diag(L_scaled))) + 2.0*T.sum(T.log(T.diag(sT.cholesky(Kmm))))) if not linear_model else 0
else:
# mu_scaled, L_scaled = mu / sf2**0.5, L / sf2**0.5
mu_scaled, L_scaled = mu / sf2, L / sf2
X = m + s * eps_NQ
U = mu_scaled + L_scaled.dot(eps_MK)
Kmn = ker.RBF(sf2, l, Z, X) if not linear_model else ker.LIN(sl2, Z, X)
Knn = ker.RBFnn(sf2, l, X) if not linear_model else ker.LINnn(sl2, X)
B = Knn - T.sum(Kmn * KmmInv.dot(Kmn), 0)
F = Kmn.T.dot(U) + T.maximum(B, 1e-16)[:,None]**0.5 * eps_NK
F = T.concatenate((T.zeros((N,1)), F), axis=1)
S = T.nnet.softmax(F)
LS = T.sum(T.log(T.maximum(T.sum(Y * S, 1), 1e-16)))
if not linear_model:
KL_U = -0.5 * (T.sum(Kmm.T * T.sum(mu_scaled[:,None,:]*mu_scaled[None,:,:], 2))
+ K * (T.sum(Kmm.T * L_scaled.dot(L_scaled.T)) - M - 2.0*T.sum(T.log(T.diag(L_scaled)))
- 2.0*T.sum(T.log(T.diag(sT.cholesky(Kmm))))))
else:
KL_U = 0
KL_X_all = -0.5 * T.sum((m**2.0 + s**2.0)/sx2 - 1.0 - 2.0*ls + T.log(sx2), 1)
KL_X = T.sum(KL_X_all)
print 'Compiling...'
inputs = {'Y': Y, 'Z': Z, 'm': m, 'ls': ls, 'mu': mu, 'lL': lL, 'lhyp': lhyp, 'KmmInv': KmmInv,
'eps_MK': eps_MK, 'eps_NQ': eps_NQ, 'eps_NK': eps_NK}
z = 0.0*sum([T.sum(v) for v in inputs.values()]) # solve a bug with derivative wrt inputs not in the graph
f = zip(['X', 'U', 'S', 'LS', 'KL_U', 'KL_X', 'KL_X_all'], [X, U, S, LS, KL_U, KL_X, KL_X_all])
self.f = {n: theano.function(inputs.values(), f+z, name=n, on_unused_input='ignore') for n,f in f}
g = zip(['LS', 'KL_U', 'KL_X'], [LS, KL_U, KL_X])
wrt = {'Z': Z, 'm': m, 'ls': ls, 'mu': mu, 'lL': lL, 'lhyp': lhyp, 'KmmInv': KmmInv}
self.g = {vn: {gn: theano.function(inputs.values(), T.grad(gv+z, vv), name='d'+gn+'_d'+vn,
on_unused_input='ignore') for gn,gv in g} for vn, vv in wrt.iteritems()}
with open(model_file_name, 'wb') as file_handle:
print 'Saving model...'
sys.setrecursionlimit(2000)
cPickle.dump([self.f, self.g, self.f_Kmm, self.f_KmmInv, self.dKmm_d], file_handle, protocol=cPickle.HIGHEST_PROTOCOL)
drawsallbase = (np.tile(np.arange(ndraws), (nobs,nchoice,1)).transpose() + 0.5)/ndraws
draws1allbase = norminv(drawsallbase*p0allbase)
p1allbase = normcdf(-(Vallbase[:,1,:] + c10[:,groupid]*draws1allbase)/c11[:,groupid]).mean(axis=0)
pallbase = p0allbase*p1allbase
if use_fe and use_share_moments:
pstation = T.stack([pallbase[1:,np.where(stationid==i)[0]].mean(axis=1) for i in range(nstation)]).transpose().flatten()[(~nuisancexi).flatten().nonzero()[0]]
pstationtrue = np.stack([dv_choice[1:,stationid==i].mean(axis=1) for i in range(nstation)]).transpose().flatten()[~nuisancexi.flatten()]
obj_multiplier = T.dscalar('obj_multiplier')
lagrange_multiplier = T.dvector('lagrange_multiplier')
lagrange = obj_multiplier*obj + (lagrange_multiplier*pstation).sum()
constr = theano.function([theta], pstation)
jab = theano.function([theta], T.jacobian(pstation, [theta]))
hess_constr = theano.function([theta, lagrange_multiplier, obj_multiplier],
outputs=theano.gradient.hessian(lagrange, [theta]))
ntheta1 = nalpha + nbeta + nallsigma
nxifull = (nchoice-1)*nstation
mask00 = np.ones((ntheta1, ntheta1), dtype = bool)
mask01 = np.ones((ntheta1, nxi), dtype = bool)
mask10 = np.ones((nxi, ntheta1), dtype = bool)
mask11 = np.tile(np.eye(nstation, dtype = bool), (nchoice-1, nchoice-1))[~nuisancexi.flatten(),:][:,~nuisancexi.flatten()]
maskj = np.hstack((mask10, mask11))
maskh = np.hstack((np.vstack((mask00, mask10)), np.vstack((mask01, mask11))))
def solve_constr(theta0, use_hess = False):
pyipopt.set_loglevel(1)
def compute_reproj_err_d_wrapper(curr_w,o,feat):
curr_cam = cams[o[0]]
curr_X = X[o[1]]
return T.jacobian(compute_reproj_err(curr_cam,curr_X,curr_w,feat),
[curr_cam,curr_X,curr_w])
def __init__(self,fname,constants={},sparse=False):
# parse model specification
with open(fname,'r') as fid:
mod = json.load(fid,object_pairs_hook=OrderedDict)
self.mod = mod
# constants
self.con_dict = OrderedDict()
for name in mod['constants']:
value = constants[name]
self.con_dict[name] = np.array(value) if type(value) is list else value
# arguments
self.arg_info = OrderedDict()
self.arg_dict = OrderedDict()
for (name,spec) in mod['arguments'].items():
asize = spec['size']
(amin,amax) = spec['range']
agrid = np.linspace(amin,amax,asize)
info = OrderedDict()
info['size'] = asize
info['grid'] = agrid
self.arg_info[name] = info
self.arg_dict[name] = agrid
# parameters
self.par_info = OrderedDict()
self.par_sizes = []
for (name,spec) in mod['parameters'].items():
ptype = spec.get('type','scalar')
psize = 1 if ptype == 'scalar' else spec['size']
info = OrderedDict()
info['type'] = ptype
info['size'] = psize
self.par_info[name] = info
self.par_sizes.append(psize)
# variables
self.var_info = OrderedDict()
self.var_sizes = []
for (name,spec) in mod['variables'].items():
vtype = spec['type']
info = OrderedDict()
info['type'] = vtype
if vtype == 'scalar':
vsize = 1
self.var_sizes.append(vsize)
elif vtype == 'vector':
vsize = spec['size']
self.var_sizes.append(vsize)
elif vtype == 'function':
vder = spec.get('derivs',[])
nder = len(vder)
args = spec['args']
ainfo = [self.arg_info[arg] for arg in args]
vsize = np.prod([ai['size'] for ai in ainfo])
info['vder'] = vder
info['nder'] = nder
info['args'] = args
info['shape'] = [self.arg_info[a]['size'] for a in args]
info['grid'] = map(lambda v: v.transpose().flatten(),np.meshgrid(*[self.arg_info[a]['grid'] for a in args])) if len(args) > 1 else [self.arg_info[args[0]]['grid']]
self.var_sizes.append(vsize)
self.var_sizes += sum(map(len,vder))*[vsize]
info['size'] = vsize
self.var_info[name] = info
# totals
self.n_pars = len(self.par_info)
self.n_vars = len(self.var_info)
self.sz_pars = np.sum(self.par_sizes)
self.sz_vars = np.sum(self.var_sizes)
# input vectors
self.par_vec = T.dvector('parvec')
self.var_vec = T.dvector('varvec')
# unpack and map out variables
self.par_dict = OrderedDict()
piter = iter(split(self.par_vec,self.par_sizes))
for (name,info) in self.par_info.items():
ptype = info['type']
par = next(piter)
if ptype == 'scalar':
par = par[0]
par.name = name
self.par_dict[name] = par
else:
par.name = name
self.par_dict[name] = par
self.var_dict = OrderedDict()
self.der_dict = OrderedDict()
viter = iter(split(self.var_vec,self.var_sizes))
for (name,info) in self.var_info.items():
var = next(viter)
vtype = info['type']
if vtype == 'scalar':
var = var[0]
var.name = name
self.var_dict[name] = var
elif vtype == 'vector':
var.name = name
self.var_dict[name] = var
elif vtype == 'function':
var.name = name
self.var_dict[name] = var
vder = info.get('vder',[])
nder = len(vder)
self.der_dict[var] = {'': var}
for der in vder:
for s in prefixes(der):
dvar = viter.next()
dvar.name = name+'_'+s
self.der_dict[var][s] = dvar
# define operators
def diff(var,*args):
name = ''.join([getkey(self.arg_dict,v) for v in args])
return self.der_dict[var][name]
def vslice(var,arg,point):
var_name = var.name
arg_name = getkey(self.arg_dict,arg)
var_info = self.var_info[var_name]
args = var_info['args']
(idx, _) = filter(lambda ia: ia[1]==arg_name, enumerate(args))[0]
shape = var_info['shape']
idx_list = slice_dim([point],idx,shape)
return var[idx_list]
def grid(var,arg):
var_name = var.name
arg_name = getkey(self.arg_dict,arg)
var_info = self.var_info[var_name]
args = var_info['args']
(idx, _) = filter(lambda ia: ia[1]==arg_name, enumerate(args))[0]
return var_info['grid'][idx]
def interp(var,arg,x):
i = icut(arg,x)
t = np.clip((arg[i+1]-x)/(arg[i+1]-arg[i]),0.0,1.0)
return t*vslice(var,arg,i) + (1.0-t)*vslice(var,arg,i+1)
self.func_dict = {'diff': diff, 'slice': vslice, 'grid': grid, 'interp': interp}
# combine them all
self.sym_dict = merge(op_dict,self.con_dict,self.par_dict,self.var_dict,self.func_dict,self.arg_dict)
# evaluate
self.equations = []
# regular equations
for eq in mod['equations']:
self.equations.append(eval(eq,{},self.sym_dict))
# derivative relations
for (name,info) in self.var_info.items():
if info['type'] == 'function':
var = self.var_dict[name]
size = info['size']
# derivative relations - symmetric except at 0
vder = info.get('vder','')
args = info['args']
shape = info['shape']
for der in vder:
v0 = '' # function value
for v1 in prefixes(der):
# collect argument info
arg = v1[-1]
(adx, _) = filter(lambda ia: ia[1]==arg, enumerate(args))[0]
s = shape[adx]
grid = info['grid'][adx]
# generate accessors
zer_idx = slice_dim([0],adx,shape)
one_idx = slice_dim([1],adx,shape)
beg_idx = slice_dim(range(s-2),adx,shape)
mid_idx = slice_dim(range(1,s-1),adx,shape)
end_idx = slice_dim(range(2,s),adx,shape)
# calculate derivatives
d0 = self.der_dict[var][v0]
d1 = self.der_dict[var][v1]
self.equations.append(d0[one_idx]-d0[zer_idx]-(grid[one_idx]-grid[zer_idx])*d1[zer_idx])
self.equations.append((d0[end_idx]-d0[beg_idx])-(grid[end_idx]-grid[beg_idx])*d1[mid_idx])
# to next level
v0 = v1
# repack
self.eqn_vec = T.join(0,*map(ensure_vector,self.equations))
# jacobians
self.par_jac = T.jacobian(self.eqn_vec,self.par_vec)
self.var_jac = T.jacobian(self.eqn_vec,self.var_vec)
# sparse?
if sparse:
self.par_jac = S.csc_from_dense(self.par_jac)
self.var_jac = S.csc_from_dense(self.var_jac)
self.linsolve = spsolve
else:
self.linsolve = np.linalg.solve
# compile
print('Compiling...')
self.eqn_fun = theano.function([self.par_vec,self.var_vec],self.eqn_vec)
self.parjac_fun = theano.function([self.par_vec,self.var_vec],self.par_jac)
self.varjac_fun = theano.function([self.par_vec,self.var_vec],self.var_jac)
# newtonian path
t = T.dscalar('t')
start = T.dvector('start')
finish = T.dvector('finish')
path = (1.0-t)*start + t*finish
dpath = T.jacobian(path,t)
self.path_fun = theano.function([start,finish,t],path)
self.dpath_fun = theano.function([start,finish,t],dpath)
def CompileTrainingFunctions(self, RPROP_penalty=0.35, RPORP_gain=0.2, SGD_LR_=5e-5,
SGD_momentum_=0.9, b_Override_only_SGD=False, bOverride_OnlyGPROP=False,
bOverride_OnlyRPORP=False, b_Override_only_RMSPROP=False, bWeightDecay=False,
bHighActivationPenalty=False, b_layerwise_LR= False, b_external_top_error=False,
b_use_clipped_gradients = False, f_clip_at = 5e-3):
""" creates the functions for the last layer of <self.layers>
trains all parameters included in <self.params>, i.e. ignoring the layer structure
rmsprop and sgd share <last_grads>, so switching between them may behave a bit strangely
"""
print "Called: CompileTrainingFunctions. You don't have to call this function, you may use .training_step() directly!"
if len(self.params)==0:
print "call CompileOutputFunctions() before calling CompileTrainingFunctions()!"
return -1
# create a list of gradients for all model parameters
if b_external_top_error==False:
if b_use_clipped_gradients==False:
output_layer_Gradients = T.grad( self.output_layer_Loss, self.params, disconnected_inputs="warn")
else:
print "\nBE WARNED: Feature activated: use_clipped_gradients (f_clip_at =",f_clip_at,")"
output_layer_Gradients_tmp = T.jacobian( self.layers[-1].negative_log_likelihood_array(self.y), self.params, disconnected_inputs="warn")
#each element has shape: (batchsize, rest...)
output_layer_Gradients = [T.mean(T.clip(x,-np.float32(np.abs(f_clip_at)),np.float32(np.abs(f_clip_at))),axis=0) for x in output_layer_Gradients_tmp]
else:
self.known_top_err = T.TensorType('float32',(False,)*5,name='known_top_err')('known_top_err')
print "predictions are last_layer.output, which is (hopefully) sigmoid!"
print "top error is specified externally: <self.known_top_err> (batchsize,x,n_classes,y,z)"
output_layer_Gradients = theano.gradient.grad( T.sum(self.layers[-1].output*self.known_top_err) , self.params ,disconnected_inputs="warn")#.subgraph_grad()
if b_Override_only_SGD==False:
self.RPROP_LRs=[] # one for each parameter -> many
self.last_grads=[]
self.gprop_grad_variance=[]
for i,para in enumerate(self.params):
if para in self.params[:i]:
print "Detected RNN or shared param @index =",i
continue
if b_Override_only_SGD==False:
# print "warning: was 4e-5"
self.RPROP_LRs.append(theano.shared( 1e-4*np.ones(para.get_value().shape,dtype=theano.config.floatX) , name=para.name+str('_RPORP') , borrow=0))
self.gprop_grad_variance.append(theano.shared( 1e-2*np.ones(para.get_value().shape,dtype=theano.config.floatX) , name=para.name+str('_GPROP') , borrow=0))
# print "WARNING change this if you want to use sgd/rmsprop"
self.last_grads.append(theano.shared( np.zeros(para.get_value().shape,dtype=theano.config.floatX) , name=para.name+str('_LG') , borrow=0))
#self.SGD_EigHessian_perturbed_grads.append(theano.shared( zeros(para.get_value().shape,dtype=theano.config.floatX) , name=para.name+str('_pLG') , borrow=True))
n = len(self.last_grads)
for i,lay in enumerate(self.layers):
low = (i*2)%n
lay.last_grads = self.last_grads[low:low+2]
SGD_updatesa=[]
SGD_updatesb=[]
if b_Override_only_SGD==False:
RPROP_updates = []
RMSPROP_updates = []
self.SGD_global_LR.set_value(np.float32(SGD_LR_))
if bWeightDecay:
print "CNN::using Weight decay! Change via this.SGD_global_weightdecay.set_value()"
self.SGD_global_weightdecay = theano.shared(np.asarray(0.0005).astype("float32"))
self.SGD_momentum.set_value(np.float32(SGD_momentum_))
if b_Override_only_SGD==False:
assert len(self.params)==len(self.last_grads),"rnn/shared params not yet implemented in rprop/gprop"
# print "Trading memory usage for more speed (SGD_updates_a), change it if it gets too big (removes momentum, too)."
for param_i, grad_i, last_grad_i, pLR_i, gprop_var_i in zip(self.params, output_layer_Gradients, self.last_grads, self.RPROP_LRs, self.gprop_grad_variance):
# capping RPROP-LR inside [1e-7,1e-2]
print "RPROP: missing backtracking handling "
RPROP_updates.append((pLR_i, T.minimum( T.maximum( pLR_i * ( 1 - np.float32(RPROP_penalty)* ((last_grad_i*grad_i) < -1e-9) + np.float32(RPORP_gain)* ((last_grad_i*grad_i) > 1e-11) ) , 1e-7*T.ones_like(pLR_i) ),2e-3 * T.ones_like(pLR_i)) ))
RPROP_updates.append((param_i, param_i - pLR_i * grad_i/(T.abs_(grad_i) + 1e-6) - (0 if bWeightDecay==False else self.SGD_global_weightdecay*param_i) ))
RPROP_updates.append((last_grad_i, grad_i ))#RPROP_updates.append((last_grad_i, (grad_i + 0.5*last_grad_i)/1.5)) #trailing exp-mean over last gradients: smoothing. check if useful...
if b_layerwise_LR:
print "Using layerwise LR multiplier. Speed penalty ~ 10%. Access it via this.SGD_local_LRs (default is 1. == no modification of the global LR)."
self.SGD_local_LRs = [theano.shared(np.float32(1.)) for x in self.params] #one LR modifier per param group
else:
self.SGD_local_LRs = [1. for x in self.params]
for param_i, grad_i, last_grad_i, local_lr_modifier in zip(self.params, output_layer_Gradients, self.last_grads, self.SGD_local_LRs):
if len(self.params)>len(self.last_grads):
grad_i = None
print "grad_param::",param_i
for i in range(len(self.params)):
if param_i == self.params[i]:
print ">>",i
grad_i = output_layer_Gradients[i] if grad_i==None else grad_i + output_layer_Gradients[i]
SGD_updatesa.append((last_grad_i, grad_i + last_grad_i * self.SGD_momentum))#use this if you want to use the gradient magnitude
for i, param_i, grad_i, last_grad_i, local_lr_modifier in zip(range(len(self.params)), self.params, output_layer_Gradients, self.last_grads, self.SGD_local_LRs):
if bWeightDecay and (i < len(self.params)-2): #no WeightDecay in last layer
SGD_updatesb.append((param_i, param_i - (self.SGD_global_LR * local_lr_modifier) * last_grad_i - self.SGD_global_LR *self.SGD_global_weightdecay*param_i ))
else:
SGD_updatesb.append((param_i, param_i - (self.SGD_global_LR * local_lr_modifier) * last_grad_i ))
RMSPROP_updates.append((last_grad_i, 0.95*last_grad_i + 0.05* (grad_i)**2 ))
RMSPROP_updates.append((param_i, param_i - self.SGD_global_LR * grad_i/( T.sqrt(last_grad_i+0.000001) ) ))
print "RMSPROP: advice: a good LR is 2e-4 (value for <self.SGD_global_LR>)"
if bHighActivationPenalty:
self.HighActivationPenalty_coeff = theano.shared(np.float32(1e-4))
print "Applying high-activation-penalty..."
print "todo: test..."
for lay in self.layers:
type_ = lay.ActivationFunction
ok=1
if type_=="tanh":
grads = T.grad( T.mean((lay.output)**2), lay.params)
elif type_=="sigmoid":
grads = T.grad( 2*T.mean((lay.output-0.5)**2), lay.params)
elif type_=="relu":
print "relu...todo:test"
grads = T.grad( -T.mean((lay.output)**2), lay.params)
else:
print "UNSUPPORTED ActivationFunction!"
ok=0
if ok:
for param_i,grad_i in zip(lay.params,grads):
for i,u in enumerate(SGD_updatesb):
if u[0]==param_i:
SGD_updatesb[i] = (param_i,u[1] - (self.SGD_global_LR * self.HighActivationPenalty_coeff) * grad_i)
break
try:
for i,u in enumerate(RMSPROP_updates):
if u[0]==param_i:
RMSPROP_updates[i] = (param_i,u[1] - (self.SGD_global_LR * self.HighActivationPenalty_coeff) * grad_i)
break
for i,u in enumerate(RPROP_updates):
if u[0]==param_i:
RPROP_updates[i] = (param_i,u[1] - (self.SGD_global_LR * self.HighActivationPenalty_coeff) * grad_i)
break
except:
print "only sgd..."
addthis = [self.z,] if self.bUseModulatedNLL else []
if b_external_top_error:
addthis = addthis + [self.known_top_err]
if bOverride_OnlyRPORP or (b_Override_only_SGD==False and bOverride_OnlyGPROP==False and b_Override_only_RMSPROP==0):
print "compiling RPROP..."
self.train_model_RPROP = theano.function([self.x] + ([] if b_external_top_error else [self.y])+addthis, None if b_external_top_error else self.output_layer_Loss, updates=RPROP_updates, on_unused_input='warn')
if b_Override_only_SGD==False and bOverride_OnlyGPROP==False and bOverride_OnlyRPORP==False:
print "compiling RMSPROP..."
self.train_model_RMSPROP = theano.function([self.x] + ([] if b_external_top_error else [self.y])+addthis, None if b_external_top_error else self.output_layer_Loss, updates=RMSPROP_updates, on_unused_input='warn')
if bOverride_OnlyGPROP==0 and b_Override_only_RMSPROP==0 and bOverride_OnlyRPORP==False:
print "compiling SGD..."
# a only updates last_grads, it DOES NOT change any parameters
#you could call it 10 times and would get the same nll every time... but if momentum is != 0 then this changes the search direction
assert len(SGD_updatesa)==len(SGD_updatesb),str(len(SGD_updatesa))+" != "+str(len(SGD_updatesb))
self.train_model_SGD_a = theano.function([self.x] + ([] if b_external_top_error else [self.y])+addthis, None if b_external_top_error else self.output_layer_Loss, updates=SGD_updatesa, on_unused_input='warn')#the output is the value you get BEFORE updates....
try:
self.train_model_SGD_a_ext = theano.function([self.x,self.y]+addthis, [self.output_layer_Loss, self.layers[-1].class_probabilities_realshape], updates=SGD_updatesa, on_unused_input='warn')
except:
print "NNet.train_model_SGD_a_ext unavailable"
# b ONLY changes the parameters
self.train_model_SGD_b = theano.function([], None, updates=SGD_updatesb)
return 0
def __init__(self, fileEmbeddings, wordEmbeddings, weights=None, contextSize=None, negative=None):
filesCount, fileEmbeddingSize = fileEmbeddings.shape
wordsCount, wordEmbeddingSize = wordEmbeddings.shape
trainWeights = weights is None
if trainWeights:
weights = rnd2(fileEmbeddingSize + contextSize * wordEmbeddingSize, wordsCount)
else:
featuresCount, activationsCount = weights.shape
contextSize = (featuresCount - fileEmbeddingSize) / wordEmbeddingSize
negative = activationsCount - 1
self.fileEmbeddings = theano.shared(asfx(fileEmbeddings), 'fileEmbeddings', borrow=False)
self.wordEmbeddings = theano.shared(asfx(wordEmbeddings), 'wordEmbeddings', borrow=False)
self.weights = theano.shared(asfx(weights), 'weights', borrow=False)
fileIndexOffset = 0
wordIndicesOffset = fileIndexOffset + 1
indicesOffset = wordIndicesOffset + contextSize
contexts = T.imatrix('contexts')
fileIndices = contexts[:,fileIndexOffset:wordIndicesOffset]
wordIndices = contexts[:,wordIndicesOffset:indicesOffset]
indices = contexts[:,indicesOffset:indicesOffset + negative]
files = self.fileEmbeddings[fileIndices]
fileFeatures = T.flatten(files, outdim=2)
words = self.wordEmbeddings[wordIndices]
wordFeatures = T.flatten(words, outdim=2)
features = T.concatenate([fileFeatures, wordFeatures], axis=1)
subWeights = self.weights[:,indices].dimshuffle(1, 0, 2)
probabilities = T.batched_dot(features, subWeights)
parameters = [self.fileEmbeddings]
subParameters = [files]
consider_constant = [self.wordEmbeddings]
if trainWeights:
parameters.append(self.weights)
subParameters.append(subWeights)
else:
consider_constant.append(self.weights)
# cost = -T.mean(T.log(T.nnet.sigmoid(probabilities[:,0])) + T.sum(T.log(T.nnet.sigmoid(-probabilities[:,1:])), dtype=floatX, acc_dtype=floatX), dtype=floatX, acc_dtype=floatX)
cost = -T.log(T.nnet.sigmoid(probabilities[:,0])) - T.sum(T.log(T.nnet.sigmoid(-probabilities[:,1:])), dtype=floatX, acc_dtype=floatX)
learningRate = T.scalar('learningRate', dtype=floatX)
updates = []
for p, subP in zip(parameters, subParameters):
if subP is not None:
gradient = T.jacobian(cost, wrt=subP)
update = (p, T.inc_subtensor(subP, -learningRate * gradient))
else:
gradient = T.jacobian(cost, wrt=p)
update = (p, p - learningRate * gradient)
updates.append(update)
contextIndex = T.iscalar('contextIndex')
self.trainingContexts = theano.shared(empty(1,1,1), 'trainingContexts', borrow=False)
self.trainModel = theano.function(
inputs=[contextIndex, learningRate],
outputs=cost,
updates=updates,
givens={
contexts: self.trainingContexts[:,contextIndex]
}
)
def compile(self, optimizer, metrics=[]):
metrics += [mean_q]
if hasattr(optimizer, '__len__'):
if len(optimizer) != 2:
raise ValueError('More than two optimizers provided. Please only provide a maximum of two optimizers, the first one for the actor and the second one for the critic.')
actor_optimizer, critic_optimizer = optimizer
else:
actor_optimizer = optimizer
critic_optimizer = clone_optimizer(optimizer)
assert actor_optimizer != critic_optimizer
if len(metrics) == 2 and hasattr(metrics[0], '__len__') and hasattr(metrics[1], '__len__'):
actor_metrics, critic_metrics = metrics
else:
actor_metrics = critic_metrics = metrics
def clipped_mse(y_true, y_pred):
delta = K.clip(y_true - y_pred, self.delta_range[0], self.delta_range[1])
return K.mean(K.square(delta), axis=-1)
# Compile target networks. We only use them in feed-forward mode, hence we can pass any
# optimizer and loss since we never use it anyway.
self.target_actor = clone_model(self.actor, self.custom_model_objects)
self.target_actor.compile(optimizer='sgd', loss='mse')
self.target_critic = clone_model(self.critic, self.custom_model_objects)
self.target_critic.compile(optimizer='sgd', loss='mse')
# We also compile the actor. We never optimize the actor using Keras but instead compute
# the policy gradient ourselves. However, we need the actor in feed-forward mode, hence
# we also compile it with any optimzer and
self.actor.compile(optimizer='sgd', loss='mse')
# Compile the critic.
if self.target_model_update < 1.:
# We use the `AdditionalUpdatesOptimizer` to efficiently soft-update the target model.
critic_updates = get_soft_target_model_updates(self.target_critic, self.critic, self.target_model_update)
critic_optimizer = AdditionalUpdatesOptimizer(critic_optimizer, critic_updates)
self.critic.compile(optimizer=critic_optimizer, loss=clipped_mse, metrics=critic_metrics)
# Combine actor and critic so that we can get the policy gradient.
combined_inputs = []
critic_inputs = []
for i in self.critic.input:
if i == self.critic_action_input:
combined_inputs.append(self.actor.output)
else:
combined_inputs.append(i)
critic_inputs.append(i)
combined_output = self.critic(combined_inputs)
if K._BACKEND == 'tensorflow':
grads = K.gradients(combined_output, self.actor.trainable_weights)
grads = [g / float(self.batch_size) for g in grads] # since TF sums over the batch
elif K._BACKEND == 'theano':
import theano.tensor as T
grads = T.jacobian(combined_output.flatten(), self.actor.trainable_weights)
grads = [K.mean(g, axis=0) for g in grads]
else:
raise RuntimeError('Unknown Keras backend "{}".'.format(K._BACKEND))
# We now have the gradients (`grads`) of the combined model wrt to the actor's weights and
# the output (`output`). Compute the necessary updates using a clone of the actor's optimizer.
clipnorm = getattr(actor_optimizer, 'clipnorm', 0.)
clipvalue = getattr(actor_optimizer, 'clipvalue', 0.)
def get_gradients(loss, params):
# We want to follow the gradient, but the optimizer goes in the opposite direction to
# minimize loss. Hence the double inversion.
assert len(grads) == len(params)
modified_grads = [-g for g in grads]
if clipnorm > 0.:
norm = K.sqrt(sum([K.sum(K.square(g)) for g in modified_grads]))
modified_grads = [optimizers.clip_norm(g, clipnorm, norm) for g in modified_grads]
if clipvalue > 0.:
modified_grads = [K.clip(g, -clipvalue, clipvalue) for g in modified_grads]
return modified_grads
actor_optimizer.get_gradients = get_gradients
updates = actor_optimizer.get_updates(self.actor.trainable_weights, self.actor.constraints, None)
if self.target_model_update < 1.:
# Include soft target model updates.
updates += get_soft_target_model_updates(self.target_actor, self.actor, self.target_model_update)
updates += self.actor.updates # include other updates of the actor, e.g. for BN
# Finally, combine it all into a callable function.
actor_inputs = None
if not hasattr(self.actor.input, '__len__'):
actor_inputs = [self.actor.input]
else:
actor_inputs = self.actor.input
inputs = actor_inputs + critic_inputs
if self.uses_learning_phase:
inputs += [K.learning_phase()]
self.actor_train_fn = K.function(inputs, [self.actor.output], updates=updates)
self.actor_optimizer = actor_optimizer
self.compiled = True
compile_mode = 'FAST_COMPILE'
#compile_mode = 'FAST_RUN'
th.config.linker='cvm'
start = t.time()
err_ = hand_objective(params_,nbones_,base_relatives_,parents_,inverse_base_absolutes_,base_positions_,
weights_,mirror_factor_,points_,correspondences_)
f = th.function([params_,nbones_,base_relatives_,parents_,inverse_base_absolutes_,base_positions_,
weights_,mirror_factor_,points_,correspondences_], err_, mode=compile_mode)
end = t.time()
tf_compile = (end - start)
print("tf_compile: %f" % tf_compile)
start = t.time()
jac = T.jacobian(T.flatten(err_),[params_])
fjac = th.function([params_,nbones_,base_relatives_,parents_,inverse_base_absolutes_,base_positions_,
weights_,mirror_factor_,points_,correspondences_], jac, mode=compile_mode)
end = t.time()
tJ_compile = (end - start)
print("tJ_compile: %f" % tJ_compile)
ntasks = (len(sys.argv)-1)//5
for task_id in range(ntasks):
print("task_id: %i" % task_id)
argv_idx = task_id*5 + 1
dir_in = sys.argv[argv_idx]
dir_out = sys.argv[argv_idx+1]
fn = sys.argv[argv_idx+2]
nruns_f = int(sys.argv[argv_idx+3])
