def logp_t(cls, value, transport, inputs):
#print(value.tag.test_value)
#print(mu.tag.test_value)
#print(mapping.inv(value).tag.test_value)
value = debug(value, 'value', force=False)
delta = transport.inv(inputs, value, noise=True)
det_m = transport.logdet_dinv(inputs, value)
delta = debug(delta, 'delta', force=False)
npi = np.float32(-0.5) * value.shape[0].astype(
th.config.floatX) * tt.log(np.float32(2.0 * np.pi))
dot2 = np.float32(-0.5) * delta.dot(delta.T)
npi = debug(npi, 'npi', force=False)
dot2 = debug(dot2, 'dot2', force=False)
det_m = debug(det_m, 'det_m', force=False)
r = npi + dot2 + det_m
cond1 = tt.or_(tt.any(tt.isinf_(delta)), tt.any(tt.isnan_(delta)))
cond2 = tt.or_(tt.any(tt.isinf_(det_m)), tt.any(tt.isnan_(det_m)))
return ifelse(cond1, np.float32(-1e30),
ifelse(cond2, np.float32(-1e30), r))
def get_nesterov_sgd_updates(param_list, gradients, velocities, lr, mu):
"""Do SGD updates with Nesterov momentum."""
updates = []
for p, g, v in zip(param_list, gradients, velocities):
new_v = mu * v - lr * g
new_p = p - mu * v + (1 + mu) * new_v
has_non_finite = (T.any(T.isnan(new_p) + T.isinf(new_p)) +
T.any(T.isnan(new_v) + T.isinf(new_v)))
updates.append((p, ifelse(has_non_finite, p, new_p)))
updates.append((v, ifelse(has_non_finite, v, new_v)))
return updates
def __init__(self, n_comp=10, verbose=False):
# Theano initialization
self.T_weights = shared(np.eye(n_comp, dtype=np.float32))
self.T_bias = shared(np.ones((n_comp, 1), dtype=np.float32))
T_p_x_white = T.fmatrix()
T_lrate = T.fscalar()
T_block = T.fscalar()
T_unmixed = T.dot(self.T_weights,T_p_x_white) + T.addbroadcast(self.T_bias,1)
T_logit = 1 - 2 / (1 + T.exp(-T_unmixed))
T_out = self.T_weights + T_lrate * T.dot(T_block * T.identity_like(self.T_weights) + T.dot(T_logit, T.transpose(T_unmixed)), self.T_weights)
T_bias_out = self.T_bias + T_lrate * T.reshape(T_logit.sum(axis=1), (-1,1))
T_max_w = T.max(self.T_weights)
T_isnan = T.any(T.isnan(self.T_weights))
self.w_up_fun = theano.function([T_p_x_white, T_lrate, T_block],
[T_max_w, T_isnan],
updates=[(self.T_weights, T_out),
(self.T_bias, T_bias_out)],
allow_input_downcast=True)
T_matrix = T.fmatrix()
T_cov = T.dot(T_matrix,T.transpose(T_matrix))/T_block
self.cov_fun = theano.function([T_matrix, T_block], T_cov, allow_input_downcast=True)
self.loading = None
self.sources = None
self.weights = None
self.n_comp = n_comp
self.verbose = verbose
def accurate_pixels_class(self, y):
"""
Returns number of correctly classified pixels per class
and total number of pixels per class.
(pair of numpy 1d arrays)
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if not y.dtype.startswith('int'):
raise NotImplementedError()
correct = T.zeros((self.n_classes), dtype='int32')
total = T.zeros((self.n_classes), dtype='int32')
for i in range(self.n_classes):
correct = T.set_subtensor(
correct[i],
T.switch(
T.any(T.eq(y, i)),
T.sum(T.eq(y[T.eq(y, i).nonzero()],
self.y_pred[T.eq(y, i).nonzero()])),
0)
)
total = T.set_subtensor(total[i], T.sum(T.eq(y, i)))
return correct, total
def compile_eval_function(nnet):
X = T.tensor4()
y = T.ivector()
# get prediciton by fully convolutional network
prediction = lasagne.layers.get_output(nnet.dense3_conv_layer,
deterministic=True,
inputs=X)
# get output scores on first dim
# before flattening on 2dim and then get scores on second dim
prediction = prediction.transpose((1, 0, 2, 3))\
.flatten(2).transpose((1, 0))
prediction = T.nnet.softmax(prediction)
# spatial averaging
prediction = T.mean(prediction, axis=0)
# compute top1 and top5 accuracies
sorted_pred = T.argsort(prediction)
top1_acc = T.mean(T.eq(sorted_pred[-1], y), dtype='floatX')
top5_acc = T.mean(T.any(T.eq(sorted_pred[-5:], T.shape_padright(y)),
axis=1),
dtype='floatX')
return theano.function([X, y], [top1_acc, top5_acc])
def in_transit(self, t, r=None, texp=None, light_delay=False):
"""Get a list of timestamps that are in transit
Args:
t (vector): A vector of timestamps to be evaluated.
r (Optional): The radii of the planets.
texp (Optional[float]): The exposure time.
Returns:
The indices of the timestamps that are in transit.
"""
if light_delay:
raise NotImplementedError(
"Light travel time delay is not implemented for simple orbits"
)
dt = tt.mod(tt.shape_padright(t) - self._ref_time, self.period)
dt -= self._half_period
if r is None:
tol = 0.5 * self.duration
else:
x = (r + self.r_star) ** 2 - self._b_norm ** 2
tol = tt.sqrt(x) / self.speed
if texp is not None:
tol += 0.5 * texp
mask = tt.any(tt.abs_(dt) < tol, axis=-1)
return tt.arange(t.size)[mask]
def posdef(self, x, diag):
""" Check to determine postive definiteness of the Kronecker-structured
covariance matrix. This operation is slow, and is thus not recommended
to be called repeatedly as a check during optimization. Rather, the user
should use this function as a guide to ensuring positive definiteness
of the model for varying values of the kernel parameters.
Args:
tensor x: The input coordinates.
tensor diag: The white noise variances. This should be an NxM
array where N is the length of x and M is the size of
alpha.
Returns:
isposdef: A boolean that is True if the covariance matrix
is positive definite and False otherwise. The user will
need to call ``isposdef.eval()`` to compute the returned value
from the theano tensor variable.
"""
diag = tt.as_tensor_variable(diag)
diag = tt.reshape(diag.T, (1, diag.size))[0]
x = tt.as_tensor_variable(x)
T = self.term.value(x[:, None] - x[None, :])
if 'alpha' in vars(self):
R = self.alpha[:, None] * self.alpha[None, :]
K = tt.slinalg.kron(T, R)
elif 'R' in vars(self):
K = tt.slinalg(T, self.R)
chol = tt.slinalg.Cholesky(on_error='nan')
L = chol(K + tt.diag(diag))
return tt.switch(tt.any(tt.isnan(L)), np.array(False), np.array(True))
def cost(self):
"""
:rtype: (theano.Variable | None, dict[theano.Variable,theano.Variable] | None)
:returns: cost, known_grads
"""
known_grads = None
if self.loss == 'ce' or self.loss == 'priori':
if self.attrs.get("target", "").endswith("[sparse:coo]"):
assert isinstance(self.y, tuple)
assert len(self.y) == 3
from NativeOp import crossentropy_softmax_and_gradient_z_sparse
y_mask = self.network.j[self.attrs.get("target", "").replace("[sparse:coo]", "[sparse:coo:2:0]")]
ce, grad_z = crossentropy_softmax_and_gradient_z_sparse(
self.z, self.index, self.y[0], self.y[1], self.y[2], y_mask)
return self.norm * T.sum(ce), {self.z: grad_z}
if self.y_data_flat.type == T.ivector().type:
# Use crossentropy_softmax_1hot to have a more stable and more optimized gradient calculation.
# Theano fails to use it automatically; I guess our self.i indexing is too confusing.
#idx = self.index.flatten().dimshuffle(0,'x').repeat(self.y_m.shape[1],axis=1) # faster than line below
#nll, pcx = T.nnet.crossentropy_softmax_1hot(x=self.y_m * idx, y_idx=self.y_data_flat * self.index.flatten())
nll, pcx = T.nnet.crossentropy_softmax_1hot(x=self.y_m[self.i], y_idx=self.y_data_flat[self.i])
#nll, pcx = T.nnet.crossentropy_softmax_1hot(x=self.y_m, y_idx=self.y_data_flat)
#nll = -T.log(T.nnet.softmax(self.y_m)[self.i,self.y_data_flat[self.i]])
#z_c = T.exp(self.z[:,self.y])
#nll = -T.log(z_c / T.sum(z_c,axis=2,keepdims=True))
#nll, pcx = T.nnet.crossentropy_softmax_1hot(x=self.y_m, y_idx=self.y_data_flat)
#nll = T.set_subtensor(nll[self.j], T.constant(0.0))
else:
nll = -T.dot(T.log(T.clip(self.p_y_given_x[self.i], 1.e-38, 1.e20)), self.y_data_flat[self.i].T)
return self.norm * T.sum(nll), known_grads
elif self.loss == 'entropy':
h_e = T.exp(self.y_m) #(TB)
pcx = T.clip((h_e / T.sum(h_e, axis=1, keepdims=True)).reshape((self.index.shape[0],self.index.shape[1],self.attrs['n_out'])), 1.e-6, 1.e6) # TBD
ee = -T.sum(pcx[self.i] * T.log(pcx[self.i])) # TB
#nll, pcxs = T.nnet.crossentropy_softmax_1hot(x=self.y_m[self.i], y_idx=self.y[self.i])
nll, _ = T.nnet.crossentropy_softmax_1hot(x=self.y_m, y_idx=self.y_data_flat) # TB
ce = nll.reshape(self.index.shape) * self.index # TB
y = self.y_data_flat.reshape(self.index.shape) * self.index # TB
f = T.any(T.gt(y,0), axis=0) # B
return T.sum(f * T.sum(ce, axis=0) + (1-f) * T.sum(ee, axis=0)), known_grads
#return T.sum(T.switch(T.gt(T.sum(y,axis=0),0), T.sum(ce, axis=0), -T.sum(ee, axis=0))), known_grads
#return T.switch(T.gt(T.sum(self.y_m[self.i]),0), T.sum(nll), -T.sum(pcx * T.log(pcx))), known_grads
elif self.loss == 'priori':
pcx = self.p_y_given_x[self.i, self.y_data_flat[self.i]]
pcx = T.clip(pcx, 1.e-38, 1.e20) # For pcx near zero, the gradient will likely explode.
return -T.sum(T.log(pcx)), known_grads
elif self.loss == 'sse':
if self.y_data_flat.dtype.startswith('int'):
y_f = T.cast(T.reshape(self.y_data_flat, (self.y_data_flat.shape[0] * self.y_data_flat.shape[1]), ndim=1), 'int32')
y_oh = T.eq(T.shape_padleft(T.arange(self.attrs['n_out']), y_f.ndim), T.shape_padright(y_f, 1))
return T.mean(T.sqr(self.p_y_given_x[self.i] - y_oh[self.i])), known_grads
else:
#return T.sum(T.sum(T.sqr(self.y_m - self.y.reshape(self.y_m.shape)), axis=1)[self.i]), known_grads
return T.sum(T.sqr(self.y_m[self.i] - self.y_data_flat.reshape(self.y_m.shape)[self.i])), known_grads
#return T.sum(T.sum(T.sqr(self.z - (self.y.reshape((self.index.shape[0], self.index.shape[1], self.attrs['n_out']))[:self.z.shape[0]])), axis=2).flatten()[self.i]), known_grads
#y_z = T.set_subtensor(T.zeros((self.index.shape[0],self.index.shape[1],self.attrs['n_out']), dtype='float32')[:self.z.shape[0]], self.z).flatten()
#return T.sum(T.sqr(y_z[self.i] - self.y[self.i])), known_grads
#return T.sum(T.sqr(self.y_m - self.y[:self.z.shape[0]*self.index.shape[1]]).flatten()[self.i]), known_grads
else:
assert False, "unknown loss: %s" % self.loss
def in_transit(self, t, r=0.0, texp=None):
"""Get a list of timestamps that are in transit
Args:
t (vector): A vector of timestamps to be evaluated.
r (Optional): The radii of the planets.
texp (Optional[float]): The exposure time.
Returns:
The indices of the timestamps that are in transit.
"""
z = tt.zeros_like(self.a)
r = tt.as_tensor_variable(r) + z
R = self.r_star + z
# Wrap the times into time since transit
hp = 0.5 * self.period
dt = tt.mod(self._warp_times(t) + hp, self.period) - hp
if self.ecc is None:
# Equation 14 from Winn (2010)
k = r / R
arg = tt.square(1 + k) - tt.square(self.b)
factor = R / (self.a * self.sin_incl)
hdur = hp * tt.arcsin(factor * tt.sqrt(arg)) / np.pi
t_start = -hdur
t_end = hdur
flag = z
else:
M_contact = self.contact_points_op(
self.a,
self.ecc,
self.cos_omega,
self.sin_omega,
self.cos_incl + z,
self.sin_incl + z,
R + r,
)
flag = M_contact[2]
t_start = (M_contact[0] - self.M0) / self.n
t_start = tt.mod(t_start + hp, self.period) - hp
t_end = (M_contact[1] - self.M0) / self.n
t_end = tt.mod(t_end + hp, self.period) - hp
t_start = tt.switch(tt.gt(t_start, 0.0), t_start - self.period,
t_start)
t_end = tt.switch(tt.lt(t_end, 0.0), t_end + self.period, t_end)
if texp is not None:
t_start -= 0.5 * texp
t_end += 0.5 * texp
mask = tt.any(tt.and_(dt >= t_start, dt <= t_end), axis=-1)
result = ifelse(tt.all(tt.eq(flag, 0)),
tt.arange(t.size)[mask], tt.arange(t.size))
return result
def compute_step(self, param, previous_step):
not_finite = tensor.any(
tensor.or_(tensor.isnan(previous_step),
tensor.isinf(previous_step)))
step = tensor.switch(not_finite, self.scaler * param, previous_step)
return step, []
def accurate_pixels_class(self, y):
"""
Returns number of correctly classified pixels per class
and total number of pixels per class.
(pair of numpy 1d arrays)
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError('y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type))
# check if y is of the correct datatype
if not y.dtype.startswith('int'):
raise NotImplementedError()
correct = T.zeros((self.n_classes), dtype='int32')
total = T.zeros((self.n_classes), dtype='int32')
for i in range(self.n_classes):
correct = T.set_subtensor(
correct[i],
T.switch(
T.any(T.eq(y, i)),
T.sum(
T.eq(y[T.eq(y, i).nonzero()],
self.y_pred[T.eq(y, i).nonzero()])), 0))
total = T.set_subtensor(total[i], T.sum(T.eq(y, i)))
return correct, total
def get_vanilla_sgd_updates(param_list, gradients, lr):
"""Do SGD updates with vanilla step rule."""
updates = []
for p, g in zip(param_list, gradients):
new_p = p - lr * g
has_non_finite = T.any(T.isnan(new_p) + T.isinf(new_p))
updates.append((p, ifelse(has_non_finite, p, new_p)))
return updates
def any(x, axis=None, keepdims=False):
"""Bitwise reduction (logical OR).
"""
y = T.any(x, axis=axis, keepdims=keepdims)
if isinstance(get_shape(x), (tuple, list)):
output_shape = auto_infer_shape(T.any, x, axis=axis, keepdims=keepdims)
add_shape(y, output_shape)
return y
def in_transit(self, t, r=0.0, texp=None):
"""Get a list of timestamps that are in transit
Args:
t (vector): A vector of timestamps to be evaluated.
r (Optional): The radii of the planets.
texp (Optional[float]): The exposure time.
Returns:
The indices of the timestamps that are in transit.
"""
z = tt.zeros_like(self.a)
r = tt.as_tensor_variable(r) + z
R = self.r_star + z
# Wrap the times into time since transit
hp = 0.5 * self.period
dt = tt.mod(self._warp_times(t) - self.t0 + hp, self.period) - hp
if self.ecc is None:
# Equation 14 from Winn (2010)
k = r / R
arg = tt.square(1 + k) - tt.square(self.b)
factor = R / (self.a * self.sin_incl)
hdur = hp * tt.arcsin(factor * tt.sqrt(arg)) / np.pi
t_start = -hdur
t_end = hdur
flag = z
else:
M_contact = self.contact_points_op(
self.a, self.ecc, self.cos_omega, self.sin_omega,
self.cos_incl + z, self.sin_incl + z, R + r)
flag = M_contact[2]
t_start = (M_contact[0] - self.M0) / self.n
t_start = tt.mod(t_start + hp, self.period) - hp
t_end = (M_contact[1] - self.M0) / self.n
t_end = tt.mod(t_end + hp, self.period) - hp
t_start = tt.switch(tt.gt(t_start, 0.0),
t_start - self.period, t_start)
t_end = tt.switch(tt.lt(t_end, 0.0),
t_end + self.period, t_end)
if texp is not None:
t_start -= 0.5*texp
t_end += 0.5*texp
mask = tt.any(tt.and_(dt >= t_start, dt <= t_end), axis=-1)
result = ifelse(tt.all(tt.eq(flag, 0)),
tt.arange(t.size)[mask],
tt.arange(t.size))
return result
def model_4(gamble, exclude, A_v, B_v, C_v, amb_A, amb_B, amb_C, rho, lambda_param, alpha_noloss_context,
alpha_loss_context, gamma, amb_gain_value, amb_loss_value):
# When using non-centered paramaterisation parameters can go below zero - need to stop this
rho = T.switch(T.lt(rho, 0.01), 0.01, rho)
lambda_param = T.switch(T.lt(lambda_param, 0.01), 0.01, lambda_param)
gamma = T.switch(T.lt(gamma, 0.01), 0.01, gamma)
alpha_noloss_context = T.switch(T.lt(alpha_noloss_context, 0.01), 0.01, alpha_noloss_context)
alpha_loss_context = T.switch(T.lt(alpha_loss_context, 0.01), 0.01, alpha_loss_context)
alpha = T.switch(T.any(T.stack([A_v, B_v, C_v]).squeeze() < 0, axis=0), alpha_loss_context, alpha_noloss_context)
# Calculate values for the 3 options (one of these may not be an option, in which case its value ends up being zero)
u_A = T.switch(T.gt(A_v, 0),
((1 - amb_A) * T.power(A_v, rho)) +
amb_A * (gamble[0] * (alpha * T.power(amb_gain_value, rho)) +
((1 - gamble[0]) * (alpha * T.power(amb_gain_value, rho)))),
-((1 - amb_A) * lambda_param * T.power(T.abs_(A_v), rho) +
amb_A * (gamble[0] * (alpha * T.power(amb_loss_value, rho)) +
((1 - gamble[0]) * (alpha * T.power(amb_loss_value, rho))))))
u_B = T.switch(T.gt(B_v, 0),
((1 - amb_B) * T.power(B_v, rho)) +
amb_B * (gamble[1] * (alpha * T.power(amb_gain_value, rho)) +
((1 - gamble[1]) * (alpha * T.power(amb_gain_value, rho)))),
-((1 - amb_B) * lambda_param * T.power(T.abs_(B_v), rho) +
amb_B * (gamble[1] * (alpha * T.power(amb_loss_value, rho)) +
((1 - gamble[1]) * (alpha * T.power(amb_loss_value, rho))))))
u_C = T.switch(T.gt(C_v, 0),
((1 - amb_C) * T.power(C_v, rho)) +
amb_C * (gamble[2] * (alpha * T.power(amb_gain_value, rho)) +
((1 - gamble[2]) * (alpha * T.power(amb_gain_value, rho)))),
-((1 - amb_C) * lambda_param * T.power(T.abs_(C_v), rho) +
amb_C * (gamble[2] * (alpha * T.power(amb_loss_value, rho)) +
((1 - gamble[2]) * (alpha * T.power(amb_loss_value, rho))))))
# If we have only two choices (i.e. no gamble), the ambiguous option should be labelled as a gamble
gamble = T.switch(T.eq(exclude.sum(axis=0), 1), T.stack([amb_A, amb_B, amb_C]).squeeze(), gamble)
# Get value of gamble option
gamble_weighting = gamble / gamble.sum(axis=0)
u_gamble = gamble_weighting[0] * (u_A * (1 - exclude[0])) + \
gamble_weighting[1] * (u_B * (1 - exclude[1])) + \
gamble_weighting[2] * (u_C * (1 - exclude[2]))
# Get value of sure option
sure_weighting = (1 - gamble) / ((1 - gamble).sum(axis=0) - exclude.sum(axis=0))
u_sure = sure_weighting[0] * (u_A * (1 - exclude[0])) + \
sure_weighting[1] * (u_B * (1 - exclude[1])) + \
sure_weighting[2] * (u_C * (1 - exclude[2]))
# Calculate choice probability
p = inv_logit(gamma * (u_gamble - u_sure))
return p
def logp_cho(cls, value, mu, cho, mapping):
"""
Calculates the log p of the parameters given the data
:param value: the data
:param mu: the location (obtained from the hiperparameters)
:param cho: the cholesky decomposition of the dispersion matrix
:param mapping: the mapping of the warped.
:return: it returns the value of the log p of the parameters given the data (values)
"""
#print(value.tag.test_value)
#print(mu.tag.test_value)
#print(mapping.inv(value).tag.test_value)
#mu = debug(mu, 'mu', force=True)
#value = debug(value, 'value', force=False)
delta = mapping.inv(value) - mu
#delta = debug(delta, 'delta', force=True)
#cho = debug(cho, 'cho', force=True)
lcho = tsl.solve_lower_triangular(cho, delta)
#lcho = debug(lcho, 'lcho', force=False)
lcho2 = lcho.T.dot(lcho)
#lcho2 = debug(lcho2, 'lcho2', force=True)
npi = np.float32(-0.5) * cho.shape[0].astype(
th.config.floatX) * tt.log(np.float32(2.0 * np.pi))
dot2 = np.float32(-0.5) * lcho2
#diag = debug(tnl.diag(cho), 'diag', force=True)
#_log= debug(tt.log(diag), 'log', force=True)
det_k = -tt.sum(tt.log(tnl.diag(cho)))
det_m = mapping.logdet_dinv(value)
#npi = debug(npi, 'npi', force=False)
#dot2 = debug(dot2, 'dot2', force=False)
#det_k = debug(det_k, 'det_k', force=False)
#det_m = debug(det_m, 'det_m', force=False)
r = npi + dot2 + det_k + det_m
cond1 = tt.or_(tt.any(tt.isinf_(delta)), tt.any(tt.isnan_(delta)))
cond2 = tt.or_(tt.any(tt.isinf_(det_m)), tt.any(tt.isnan_(det_m)))
cond3 = tt.or_(tt.any(tt.isinf_(cho)), tt.any(tt.isnan_(cho)))
cond4 = tt.or_(tt.any(tt.isinf_(lcho)), tt.any(tt.isnan_(lcho)))
return ifelse(
cond1, np.float32(-1e30),
ifelse(
cond2, np.float32(-1e30),
ifelse(cond3, np.float32(-1e30),
ifelse(cond4, np.float32(-1e30), r))))
def _step(input, *states):
output, new_states = step_function(input, states)
if masking:
# if all-zero input timestep, return
# all-zero output and unchanged states
switch = T.any(input, axis=-1, keepdims=True)
output = T.switch(switch, output, 0. * output)
return_states = []
for state, new_state in zip(states, new_states):
return_states.append(T.switch(switch, new_state, state))
return [output] + return_states
else:
return [output] + new_states
def _step(input, *states):
output, new_states = step_function(input, states)
if masking:
# if all-zero input timestep, return
# all-zero output and unchanged states
switch = T.any(input, axis=-1, keepdims=True)
output = T.switch(switch, output, 0. * output)
return_states = []
for state, new_state in zip(states, new_states):
return_states.append(T.switch(switch, new_state, state))
return [output] + return_states
else:
return [output] + new_states
def do_compute(self, quiet):
if quiet:
self._d, self._W, _ = ops.factor_quiet(self._a, self._U, self._V,
self._P)
self._log_det = tt.switch(tt.any(self._d < 0.0), -np.inf,
tt.sum(tt.log(self._d)))
else:
self._d, self._W, _ = ops.factor(self._a, self._U, self._V,
self._P)
self._log_det = tt.sum(tt.log(self._d))
self._norm = -0.5 * (self._log_det + self._size * np.log(2 * np.pi))
def grad(self, inputs, gradients):
"""
Cholesky decomposition reverse-mode gradient update.
Symbolic expression for reverse-mode Cholesky gradient taken from [0]_
References
----------
.. [0] I. Murray, "Differentiation of the Cholesky decomposition",
http://arxiv.org/abs/1602.07527
"""
x = inputs[0]
dz = gradients[0]
chol_x = self(x)
# Replace the cholesky decomposition with 1 if there are nans
# or solve_upper_triangular will throw a ValueError.
if self.on_error == 'nan':
ok = ~tensor.any(tensor.isnan(chol_x))
chol_x = tensor.switch(ok, chol_x, 1)
dz = tensor.switch(ok, dz, 1)
# deal with upper triangular by converting to lower triangular
if not self.lower:
chol_x = chol_x.T
dz = dz.T
def tril_and_halve_diagonal(mtx):
"""Extracts lower triangle of square matrix and halves diagonal."""
return tensor.tril(mtx) - tensor.diag(tensor.diagonal(mtx) / 2.)
def conjugate_solve_triangular(outer, inner):
"""Computes L^{-T} P L^{-1} for lower-triangular L."""
return solve_upper_triangular(
outer.T,
solve_upper_triangular(outer.T, inner.T).T)
s = conjugate_solve_triangular(
chol_x, tril_and_halve_diagonal(chol_x.T.dot(dz)))
if self.lower:
grad = tensor.tril(s + s.T) - tensor.diag(tensor.diagonal(s))
else:
grad = tensor.triu(s + s.T) - tensor.diag(tensor.diagonal(s))
if self.on_error == 'nan':
return [tensor.switch(ok, grad, np.nan)]
else:
return [grad]
def build_aligner(self):
tgt_action_seq = ndim_itensor(3, 'tgt_action_seq')
tgt_action_seq_type = ndim_itensor(3, 'tgt_action_seq_type')
tgt_node_seq = ndim_itensor(2, 'tgt_node_seq')
tgt_par_rule_seq = ndim_itensor(2, 'tgt_par_rule_seq')
tgt_par_t_seq = ndim_itensor(2, 'tgt_par_t_seq')
tgt_node_embed = self.node_embedding[tgt_node_seq]
query_tokens = ndim_itensor(2, 'query_tokens')
query_token_embed, query_token_embed_mask = self.query_embedding(
query_tokens, mask_zero=True)
batch_size = tgt_action_seq.shape[0]
max_example_action_num = tgt_action_seq.shape[1]
tgt_action_seq_embed = T.switch(
T.shape_padright(tgt_action_seq[:, :, 0] > 0),
self.rule_embedding_W[tgt_action_seq[:, :, 0]],
self.vocab_embedding_W[tgt_action_seq[:, :, 1]])
tgt_action_seq_embed_tm1 = tensor_right_shift(tgt_action_seq_embed)
tgt_par_rule_embed = T.switch(tgt_par_rule_seq[:, :, None] < 0,
T.alloc(0., 1, config.rule_embed_dim),
self.rule_embedding_W[tgt_par_rule_seq])
if not config.frontier_node_type_feed:
tgt_node_embed *= 0.
if not config.parent_action_feed:
tgt_par_rule_embed *= 0.
decoder_input = T.concatenate(
[tgt_action_seq_embed_tm1, tgt_node_embed, tgt_par_rule_embed],
axis=-1)
query_embed = self.query_encoder_lstm(query_token_embed,
mask=query_token_embed_mask,
dropout=0,
srng=self.srng)
tgt_action_seq_mask = T.any(tgt_action_seq_type, axis=-1)
alignments = self.decoder_lstm.align(
decoder_input,
context=query_embed,
context_mask=query_token_embed_mask,
mask=tgt_action_seq_mask,
parent_t_seq=tgt_par_t_seq,
srng=self.srng)
alignment_inputs = [
query_tokens, tgt_action_seq, tgt_action_seq_type, tgt_node_seq,
tgt_par_rule_seq, tgt_par_t_seq
]
self.align = theano.function(alignment_inputs, [alignments])
def grad(self, inputs, gradients):
"""
Cholesky decomposition reverse-mode gradient update.
Symbolic expression for reverse-mode Cholesky gradient taken from [0]_
References
----------
.. [0] I. Murray, "Differentiation of the Cholesky decomposition",
http://arxiv.org/abs/1602.07527
"""
x = inputs[0]
dz = gradients[0]
chol_x = self(x)
# Replace the cholesky decomposition with 1 if there are nans
# or solve_upper_triangular will throw a ValueError.
if self.on_error == 'nan':
ok = ~tensor.any(tensor.isnan(chol_x))
chol_x = tensor.switch(ok, chol_x, 1)
dz = tensor.switch(ok, dz, 1)
# deal with upper triangular by converting to lower triangular
if not self.lower:
chol_x = chol_x.T
dz = dz.T
def tril_and_halve_diagonal(mtx):
"""Extracts lower triangle of square matrix and halves diagonal."""
return tensor.tril(mtx) - tensor.diag(tensor.diagonal(mtx) / 2.)
def conjugate_solve_triangular(outer, inner):
"""Computes L^{-T} P L^{-1} for lower-triangular L."""
return solve_upper_triangular(
outer.T, solve_upper_triangular(outer.T, inner.T).T)
s = conjugate_solve_triangular(
chol_x, tril_and_halve_diagonal(chol_x.T.dot(dz)))
if self.lower:
grad = tensor.tril(s + s.T) - tensor.diag(tensor.diagonal(s))
else:
grad = tensor.triu(s + s.T) - tensor.diag(tensor.diagonal(s))
if self.on_error == 'nan':
return [tensor.switch(ok, grad, np.nan)]
else:
return [grad]
def get_idx(q_nbrs, q_mem):
"""Gets the index of sample in memory for computing loss.
We first look to see if the query label can be found in the
retrieved neighbours, and if not, look to memory for a key with
the same value.
We keep track of a boolean mask, which indicates whether or not we
were able to find a sample with a label that matches the query.
"""
# Whether a matching sample can be found in neighbours or memory
any_match_nbrs = T.any(q_nbrs, axis=1)
any_match_mem = T.any(q_mem, axis=1)
any_match = T.or_(any_match_nbrs, any_match_mem)
# Look in neighbours then memory for corresponding sample.
# If from neighbours, we need to retrieve the full mem idx.
rows = T.arange(nbrs.shape[0])
idx = T.switch(any_match_nbrs,
nbrs[rows, tensor_choose_k(q_nbrs, self.rng, k=1)],
tensor_choose_k(q_mem, self.rng, k=1, random=True))
return (idx, any_match)
def _get_accuracy(self, top_range, data_type):
return_list = isinstance(top_range, list)
if not return_list:
top_range = [top_range]
max_top_range = max(top_range)
expanded = self._correct_answers.dimshuffle(0, 'x')
expanded = expanded.repeat(max_top_range, axis=1)
eq = T.eq(expanded, self.answers[:, :max_top_range])
# Compile new function only if top range or data type has changed
if self._accuracy_config != [top_range, data_type]:
self._accuracy = theano.function(
inputs=[self._batch_index],
outputs=[
T.any(eq[:, :top], axis=1).mean() for top in top_range
],
givens={
self._input:
self.data_loader.input(self._batch_index, data_type),
self._correct_answers:
self.data_loader.output(self._batch_index, data_type)
},
)
self._accuracy_config = [top_range, data_type]
n_batches = self.data_loader.n_batches(data_type)
accuracy = np.zeros(shape=(n_batches, len(top_range)))
interval = n_batches / 10
if interval == 0:
interval = 1
for batch_index in xrange(n_batches):
self.data_loader.load_data(batch_index, data_type)
accuracy[batch_index, :] = np.asarray(self._accuracy(batch_index))
if self.verbosity >= 3 or \
(self.verbosity >= 2 and batch_index % interval == 0):
partial_accuracy = accuracy[:batch_index + 1, :].mean(axis=0)
text = ''
for a in partial_accuracy:
text += ' {:.2f}%'.format(100 * a)
overwrite('{}/{} minibatches accuracy:{}'.format(
batch_index + 1, n_batches, text))
overwrite()
accuracy = accuracy.mean(axis=0).tolist()
if not return_list:
return accuracy[0]
return accuracy
def compile_prop_f(self, signals, has_input, min_tau=0.0):
tau_in = T.scalar('min_tau', dtype=FLOATX)
inputs = [tau_in]
x = self.signal(signals)
# Get estimate of the state from layer above
estimate = self.estimate(signals)
# Feedforward originates from previous layer's state or given input
if not has_input:
feedforward = self.feedforward(signals)
has_nans = T.as_tensor_variable(0)
nans = 0.0
else:
input_t = T.matrix('input', dtype=FLOATX)
inputs += [input_t]
nans = T.isnan(input_t)
has_nans = T.any(nans)
feedforward = T.where(nans, 0.0, input_t)
self.info('Compiling propagation: [%6s] -> %4s <- [%6s]' %
(",".join([p.name for p in self.prev] if self.prev else 'u/y'),
self.name,
",".join([p.name for p in self.next] if self.next else '')))
# Apply nonlinearity to feedforward path only
if self.nonlin:
feedforward = self.nonlin(feedforward)
if self.merge_op:
assert not self.persistent, 'cannot combine with merge_op'
new_value = self.merge_op(feedforward, estimate)
elif self.persistent:
new_value = feedforward
else:
new_value = feedforward - estimate
# If predicting missing values, force them to zero in residual so
# that they don't influence learning
new_value = ifelse(has_nans, T.where(nans, 0.0, new_value), new_value)
(new_X, t, d) = lerp(x.var, new_value, tau_in)
d = T.max(d)
updates = [(x.var, ifelse(self.enabled, new_X, x.var))]
return theano.function(inputs=inputs,
outputs=d,
updates=updates)
def _step(*args):
global single_result
input = args[0]
states = args[1:]
output, new_states = step_function(input, states)
if masking:
# if all-zero input timestep, return
# all-zero output and unchanged states
switch = T.any(input)
output = T.switch(switch, output, 0. * output)
return_states = []
for state, new_state in zip(states, new_states):
return_states.append(T.switch(switch, new_state, state))
return [output] + return_states
else:
return [output] + new_states
def _step(*args):
global single_result
input = args[0]
states = args[1:]
output, new_states = step_function(input, states)
if masking:
# if all-zero input timestep, return
# all-zero output and unchanged states
switch = T.any(input)
output = T.switch(switch, output, 0. * output)
return_states = []
for state, new_state in zip(states, new_states):
return_states.append(T.switch(switch, new_state, state))
return [output] + return_states
else:
return [output] + new_states
def compile_prop_f(self, signals, has_input, min_tau=0.0):
tau_in = T.scalar('min_tau', dtype=FLOATX)
inputs = [tau_in]
x = self.signal(signals)
# Get estimate of the state from layer above
estimate = self.estimate(signals)
# Feedforward originates from previous layer's state or given input
if not has_input:
feedforward = self.feedforward(signals)
has_nans = T.as_tensor_variable(0)
nans = 0.0
else:
input_t = T.matrix('input', dtype=FLOATX)
inputs += [input_t]
nans = T.isnan(input_t)
has_nans = T.any(nans)
feedforward = T.where(nans, 0.0, input_t)
self.info(
'Compiling propagation: [%6s] -> %4s <- [%6s]' %
(",".join([p.name for p in self.prev] if self.prev else 'u/y'),
self.name, ",".join([p.name
for p in self.next] if self.next else '')))
# Apply nonlinearity to feedforward path only
if self.nonlin:
feedforward = self.nonlin(feedforward)
if self.merge_op:
assert not self.persistent, 'cannot combine with merge_op'
new_value = self.merge_op(feedforward, estimate)
elif self.persistent:
new_value = feedforward
else:
new_value = feedforward - estimate
# If predicting missing values, force them to zero in residual so
# that they don't influence learning
new_value = ifelse(has_nans, T.where(nans, 0.0, new_value), new_value)
(new_X, t, d) = lerp(x.var, new_value, tau_in)
d = T.max(d)
updates = [(x.var, ifelse(self.enabled, new_X, x.var))]
return theano.function(inputs=inputs, outputs=d, updates=updates)
def _step(input, *args):
# separate states and contexts
states = args[0:nb_states]
output, other_outputs, new_states = step_function(input, args)
if masking:
# if all-zero input timestep, return
# all-zero output and unchanged states
switch = T.any(input, axis=-1, keepdims=True)
output = T.switch(switch, output, 0. * output)
for other_output in other_outputs:
other_output = T.switch(switch, other_output, 0. * other_output)
return_states = []
for state, new_state in zip(states, new_states):
return_states.append(T.switch(switch, new_state, state))
return [output] + other_outputs + return_states
else:
return [output] + other_outputs + new_states
def categorical_acc(predictions, targets, top_k=1):
if targets.ndim == predictions.ndim:
targets = T.argmax(targets, axis=-1)
elif targets.ndim != predictions.ndim - 1:
raise TypeError('rank mismatch between targets and predictions')
if top_k == 1:
# standard categorical accuracy
top = T.argmax(predictions, axis=-1)
return T.eq(top, targets)
else:
# top-k accuracy
top = T.argsort(predictions, axis=-1)
# (Theano cannot index with [..., -top_k:], we need to simulate that)
top = top[[slice(None)
for _ in range(top.ndim - 1)] + [slice(-top_k, None)]]
targets = T.shape_padaxis(targets, axis=-1)
return T.any(T.eq(top, targets), axis=-1)
def get_net_fun(phonemeViseme, networkType, k=5, print_network= False):
outputLayer, inputs = load_model(phonemeViseme, networkType, print_network)
targets = T.ivector('targets')
all_predictions = lasagne.layers.get_output(outputLayer, deterministic=True)
get_all_prob = theano.function([inputs], all_predictions)
maxprob = T.argmax(all_predictions, axis=1)
get_first_prediction = theano.function([inputs], maxprob)
accuracy = T.eq(maxprob, targets)
avg_accuracy= T.mean(accuracy, dtype=theano.config.floatX)
get_accuracy = theano.function([inputs, targets], avg_accuracy)
# Top k accuracy
# topk_accuracy = T.mean(T.any(T.eq(T.argsort(all_predictions, axis=1)[:, -k:], targets.dimshuffle(0, 'x')), axis=1), axis=1)
topk_accuracy = T.any(T.eq(T.argsort(all_predictions, axis=1)[:, -k:], targets.dimshuffle(0, 'x')), axis=1)
avg_topk_accuracy = T.mean(topk_accuracy, dtype=theano.config.floatX)
get_topk_accuracy = theano.function([inputs, targets], avg_topk_accuracy)
val_fn = theano.function([inputs, targets], [all_predictions, maxprob, avg_accuracy, avg_topk_accuracy])
def print_topk(im_path, k):
im = prep_image(im_path)
prob = get_all_prob(im)[0]
#print(prob)
phonemeNumberMap = classToPhoneme39
pred = []
for i in range(0, len(prob)):
p = prob[i]
prob_phoneme = phonemeNumberMap[i]
pred.append([prob_phoneme, p])
# print(p, " ", prob_phoneme)
pred = sorted(pred, key=lambda t: t[1], reverse=True)
pred = pred[:k]
for p in pred:
print(p)
return get_all_prob, get_first_prediction, print_topk, get_accuracy, get_topk_accuracy, val_fn
def posdef(self, x, diag):
diag = tt.as_tensor_variable(diag)
diag = tt.reshape(diag.T, (1, diag.size))[0]
x = tt.as_tensor_variable(x)
T = self.terms[0].value(x[:, None] - x[None, :])
if self.terms[0].alpha.ndim == 1:
R = self.terms[0].alpha[:, None] * self.terms[0].alpha[None, :]
K = tt.slinalg.kron(T, R)
else:
K = tt.slinalg(T, self.terms[0].alpha)
for term in self.terms:
T = term.value(x[:, None] - x[None, :])
if term.alpha.ndim == 1:
R = term.alpha[:, None] * term.alpha[None, :]
K += tt.slinalg.kron(T, R)
else:
K += tt.slinalg(T, term.alpha)
chol = tt.slinalg.Cholesky(on_error='nan')
L = chol(K + tt.diag(diag))
return tt.switch(tt.any(tt.isnan(L)), np.array(False), np.array(True))
def logp_cho(cls, value, mu, cho, freedom, mapping):
delta = mapping.inv(value) - mu
lcho = tsl.solve_lower_triangular(cho, delta)
beta = lcho.T.dot(lcho)
n = cho.shape[0].astype(th.config.floatX)
np5 = np.float32(0.5)
np2 = np.float32(2.0)
npi = np.float32(np.pi)
r1 = -np5 * (freedom + n) * tt.log1p(beta / (freedom - np2))
r2 = ifelse(
tt.le(np.float32(1e6), freedom), -n * np5 * np.log(np2 * npi),
tt.gammaln((freedom + n) * np5) - tt.gammaln(freedom * np5) -
np5 * n * tt.log((freedom - np2) * npi))
r3 = -tt.sum(tt.log(tnl.diag(cho)))
det_m = mapping.logdet_dinv(value)
r1 = debug(r1, name='r1', force=True)
r2 = debug(r2, name='r2', force=True)
r3 = debug(r3, name='r3', force=True)
det_m = debug(det_m, name='det_m', force=True)
r = r1 + r2 + r3 + det_m
cond1 = tt.or_(tt.any(tt.isinf_(delta)), tt.any(tt.isnan_(delta)))
cond2 = tt.or_(tt.any(tt.isinf_(det_m)), tt.any(tt.isnan_(det_m)))
cond3 = tt.or_(tt.any(tt.isinf_(cho)), tt.any(tt.isnan_(cho)))
cond4 = tt.or_(tt.any(tt.isinf_(lcho)), tt.any(tt.isnan_(lcho)))
return ifelse(
cond1, np.float32(-1e30),
ifelse(
cond2, np.float32(-1e30),
ifelse(cond3, np.float32(-1e30),
ifelse(cond4, np.float32(-1e30), r))))
def __init__(self, n_comp=10, verbose=False):
# Theano initialization
self.T_weights = shared(np.eye(n_comp, dtype=np.float32))
self.T_bias = shared(np.ones((n_comp, 1), dtype=np.float32))
T_p_x_white = T.fmatrix()
T_lrate = T.fscalar()
T_block = T.fscalar()
T_unmixed = T.dot(self.T_weights, T_p_x_white) + T.addbroadcast(
self.T_bias, 1)
T_logit = 1 - 2 / (1 + T.exp(-T_unmixed))
T_out = self.T_weights + T_lrate * T.dot(
T_block * T.identity_like(self.T_weights) +
T.dot(T_logit, T.transpose(T_unmixed)), self.T_weights)
T_bias_out = self.T_bias + T_lrate * T.reshape(T_logit.sum(axis=1),
(-1, 1))
T_max_w = T.max(self.T_weights)
T_isnan = T.any(T.isnan(self.T_weights))
self.w_up_fun = theano.function([T_p_x_white, T_lrate, T_block],
[T_max_w, T_isnan],
updates=[(self.T_weights, T_out),
(self.T_bias, T_bias_out)],
allow_input_downcast=True)
T_matrix = T.fmatrix()
T_cov = T.dot(T_matrix, T.transpose(T_matrix)) / T_block
self.cov_fun = theano.function([T_matrix, T_block],
T_cov,
allow_input_downcast=True)
self.loading = None
self.sources = None
self.weights = None
self.n_comp = n_comp
self.verbose = verbose
def in_transit(self, t, r=None, texp=None):
"""Get a list of timestamps that are in transit
Args:
t (vector): A vector of timestamps to be evaluated.
r (Optional): The radii of the planets.
texp (Optional[float]): The exposure time.
Returns:
The indices of the timestamps that are in transit.
"""
dt = tt.mod(tt.shape_padright(t) - self._ref_time, self.period)
dt -= self._half_period
if self.r is None:
tol = 0.5 * self.duration
else:
x = (r + self.r_star)**2 - self._b_norm**2
tol = tt.sqrt(x) / self.speed
if texp is not None:
tol += 0.5 * texp
mask = tt.any(tt.abs_(dt) < tol, axis=-1)
return tt.arange(t.size)[mask]
def in_transit(self, t, r=0.0, texp=None):
"""Get a list of timestamps that are in transit
Args:
t (vector): A vector of timestamps to be evaluated.
r (Optional): The radii of the planets.
texp (Optional[float]): The exposure time.
Returns:
The indices of the timestamps that are in transit.
"""
z = tt.zeros_like(self.a)
r = tt.as_tensor_variable(r) + z
R = self.r_star + z
if self.ecc is None:
M_contact = self.contact_points_op(self.a, self.incl + z, r, R)
else:
M_contact = self.contact_points_op(self.a, self.ecc, self.omega,
self.incl + z, r, R)
# Wrap the times into time since transit
hp = 0.5 * self.period
t_start = (M_contact[0] - self.M0) / self.n
t_start = tt.mod(t_start + hp, self.period) - hp
t_end = (M_contact[3] - self.M0) / self.n
t_end = tt.mod(t_end + hp, self.period) - hp
dt = tt.mod(self._warp_times(t) - self.t0 + hp, self.period) - hp
if texp is not None:
t_start -= 0.5 * texp
t_end += 0.5 * texp
mask = tt.any(tt.and_(dt >= t_start, dt <= t_end), axis=-1)
return tt.arange(t.size)[mask]
def any(x, axis=None, keepdims=False):
'''Bitwise reduction (logical OR).
'''
return T.any(x, axis=axis, keepdims=keepdims)
def get_output_mask(self, train=False):
X = self.get_input(train)
return T.any(T.ones_like(X) * (1. - T.eq(X, self.mask_value)), axis=-1)
def _compile_functions(self):
self._gradnorm = T.zeros([])
for _param, _grad in zip(self._params, self._grads):
# apply rmsprop to before clipping gradients
if self.rmsprop:
avg_grad_sqr = self._avg_grad_sqrs[_param]
new_avg_grad_sqr = self.averaging_coeff * avg_grad_sqr + \
(1 - self.averaging_coeff) * T.sqr(_grad)
self._avg_grad_sqrs_updates[avg_grad_sqr] = new_avg_grad_sqr
rms_grad_t = T.sqrt(new_avg_grad_sqr)
rms_grad_t = T.maximum(rms_grad_t, self.stabilizer)
_grad = _grad / rms_grad_t
self._gradnorm += T.sum(_grad**2)
self._gradnorm = T.sqrt(self._gradnorm)
self._givens = {}
self._givens[self._inputvar] = self._inputs_theano[
self._batch_idx * self.batchsize:
(self._batch_idx + 1) * self.batchsize]
if self.is_supervised:
self._givens[self._targetvar] = self._outputs_theano[
self._batch_idx * self.batchsize:
(self._batch_idx + 1) * self.batchsize]
if self.has_masks:
self._givens[self._maskvar] = self._masks_theano[
self._batch_idx * self.batchsize:
(self._batch_idx + 1) * self.batchsize]
self.gradnorm = theano.function(
inputs=[],
outputs=self._gradnorm,
givens=self._givens)
avg_gradnorm_update = {
self._avg_gradnorm: self._avg_gradnorm * .8 + self._gradnorm * .2}
self._update_weight_norm_ratios = []
for _param, _grad in zip(self._params, self._grads):
if hasattr(self._model, 'skip_params'):
if _param.name in self._model.skip_params:
continue
_clip_grad = T.switch(
T.gt(self._gradnorm, self._gradient_clip_threshold),
_grad * self._gradient_clip_threshold / self._gradnorm, _grad)
try: # ... to apply learningrate_modifiers
# Cliphid version:
self._inc_updates[self._incs[_param]] = \
self._momentum * self._incs[_param] - \
self._learningrate * \
self._model.learningrate_modifiers[
_param.name] * _clip_grad
self._updates[_param] = _param + self._incs[_param]
self._updates_nomomentum[_param] = _param - \
self._learningrate * \
self._model.learningrate_modifiers[_param.name] * \
_clip_grad
print 'Learning rate modifier for {0}: {1}'.format(
_param.name, self._model.learningrate_modifiers[_param.name])
except (AttributeError, KeyError):
self._inc_updates[self._incs[_param]] = self._momentum * \
self._incs[_param] - self._learningrate * _clip_grad
self._updates[_param] = _param + self._incs[_param]
self._updates_nomomentum[_param] = _param - \
self._learningrate * _clip_grad
if self.monitor_update_weight_norm_ratio:
print 'building update weight norm ratio graph for ', _param.name
self._update_weight_norm_ratios.append(
self._incs[_param].norm(2) / _param.norm(2))
self.any_isnan = T.any(T.isnan(
T.concatenate([x.flatten() for x in self._grads], axis=0)))
# compute function to get update_weight_norm_ratios (returned in same
# order as params list)
print 'compiling update weight norm ratio function...'
self.get_update_weight_norm_ratios = theano.function(
[], self._update_weight_norm_ratios)
print 'done'
# first update gradient norm running avg
ordered_updates = collections.OrderedDict()
try:
ordered_updates.update(self._model.updates)
except AttributeError:
pass
ordered_updates.update(avg_gradnorm_update)
# so that it is considered in the parameter update computations
ordered_updates.update(self._inc_updates)
print 'compiling updateincs...'
self._updateincs = theano.function(
[], [self._cost, self._avg_gradnorm, self.any_isnan], updates=ordered_updates,
givens=self._givens)
print 'done'
print 'compiling trainmodel...'
self._trainmodel = theano.function(
[self._n], self._noop, updates=self._updates)
print 'done'
print 'compiling trainmodel_nomomentum...'
self._trainmodel_nomomentum = theano.function(
[self._n], self._noop, updates=self._updates_nomomentum,
givens=self._givens)
print 'done'
self._momentum_batchcounter = 0
def setup_backprop(self):
eta = T.scalar('eta_for_backprop')
x = T.lvector('x_for_backprop')
y = T.lvector('y_for_backprop')
y_in_x_inds = T.lmatrix('y_in_x_inds_for_backprop')
dec_init_state, annotations = self._symb_encoder(x)
def decoder_recurrence(y_t, cur_y_in_x_inds, h_prev, annotations, *params):
h_for_write = self.spec.decoder.get_h_for_write(h_prev)
scores = self.spec.get_attention_scores(h_for_write, annotations)
alpha = self.spec.get_alpha(scores)
c_t = self.spec.get_context(alpha, annotations)
write_dist = self.spec.f_write(h_for_write, c_t, scores)
base_p_y_t = write_dist[y_t]
if self.spec.attention_copying:
copying_p_y_t = T.dot(
write_dist[self.out_vocabulary.size():],
cur_y_in_x_inds)
p_y_t = base_p_y_t + copying_p_y_t
else:
p_y_t = base_p_y_t
h_t = self.spec.f_dec(y_t, c_t, h_prev)
return (h_t, p_y_t)
dec_results, _ = theano.scan(
fn=decoder_recurrence, sequences=[y, y_in_x_inds],
outputs_info=[dec_init_state, None],
non_sequences=[annotations] + self.spec.get_all_shared())
p_y_seq = dec_results[1]
log_p_y = T.sum(T.log(p_y_seq))
gradients = T.grad(log_p_y, self.params)
# Do the updates here
updates = []
if self.spec.step_rule in ('adagrad', 'rmsprop'):
# Adagrad updates
for p, g, c in zip(self.params, gradients, self.grad_cache):
grad_norm = g.norm(2)
clipped_grad = ifelse(grad_norm >= CLIP_THRESH,
g * CLIP_THRESH / grad_norm, g)
if self.spec.step_rule == 'adagrad':
new_c = c + clipped_grad ** 2
else: # rmsprop
decay_rate = 0.9 # Use fixed decay rate of 0.9
new_c = decay_rate * c + (1.0 - decay_rate) * clipped_grad ** 2
new_p = p + eta * clipped_grad / T.sqrt(new_c + 1e-4)
has_non_finite = T.any(T.isnan(new_p) + T.isinf(new_p))
updates.append((p, ifelse(has_non_finite, p, new_p)))
updates.append((c, ifelse(has_non_finite, c, new_c)))
elif self.spec.step_rule == 'nesterov':
# Nesterov momentum
for p, g, v in zip(self.params, gradients, self.grad_cache):
grad_norm = g.norm(2)
clipped_grad = ifelse(grad_norm >= CLIP_THRESH,
g * CLIP_THRESH / grad_norm, g)
new_v = NESTEROV_MU * v + eta * clipped_grad
new_p = p - NESTEROV_MU * v + (1 + NESTEROV_MU) * new_v
has_non_finite = (T.any(T.isnan(new_p) + T.isinf(new_p)) +
T.any(T.isnan(new_v) + T.isinf(new_v)))
updates.append((p, ifelse(has_non_finite, p, new_p)))
updates.append((v, ifelse(has_non_finite, v, new_v)))
else:
# Simple SGD updates
for p, g in zip(self.params, gradients):
grad_norm = g.norm(2)
clipped_grad = ifelse(grad_norm >= CLIP_THRESH,
g * CLIP_THRESH / grad_norm, g)
new_p = p + eta * clipped_grad
has_non_finite = T.any(T.isnan(new_p) + T.isinf(new_p))
updates.append((p, ifelse(has_non_finite, p, new_p)))
#updates.append((p, new_p))
self._backprop = theano.function(
inputs=[x, y, eta, y_in_x_inds],
outputs=[p_y_seq, log_p_y],
updates=updates)
def any(x, axis=None, keepdims=False):
'''Bitwise reduction (logical OR).
'''
return T.any(x, axis=axis, keepdims=keepdims)
def build(self):
# (batch_size, max_example_action_num, action_type)
tgt_action_seq = ndim_itensor(3, 'tgt_action_seq')
# (batch_size, max_example_action_num, action_type)
tgt_action_seq_type = ndim_itensor(3, 'tgt_action_seq_type')
# (batch_size, max_example_action_num)
tgt_node_seq = ndim_itensor(2, 'tgt_node_seq')
# (batch_size, max_example_action_num)
tgt_par_rule_seq = ndim_itensor(2, 'tgt_par_rule_seq')
# (batch_size, max_example_action_num)
tgt_par_t_seq = ndim_itensor(2, 'tgt_par_t_seq')
# (batch_size, max_example_action_num, symbol_embed_dim)
# tgt_node_embed = self.node_embedding(tgt_node_seq, mask_zero=False)
tgt_node_embed = self.node_embedding[tgt_node_seq]
# (batch_size, max_query_length)
query_tokens = ndim_itensor(2, 'query_tokens')
mask = T.TensorType(dtype='int32',
name='mask',
broadcastable=(True, False))()
# (batch_size, max_query_length, query_token_embed_dim)
# (batch_size, max_query_length)
query_token_embed, query_token_embed_mask = self.query_embedding(
query_tokens, mask_zero=True)
# if WORD_DROPOUT > 0:
# logging.info('used word dropout for source, p = %f', WORD_DROPOUT)
# query_token_embed, query_token_embed_intact = WordDropout(WORD_DROPOUT, self.srng)(query_token_embed, False)
batch_size = tgt_action_seq.shape[0]
max_example_action_num = tgt_action_seq.shape[1]
# previous action embeddings
# (batch_size, max_example_action_num, action_embed_dim)
tgt_action_seq_embed = T.switch(
T.shape_padright(tgt_action_seq[:, :, 0] > 0),
self.rule_embedding_W[tgt_action_seq[:, :, 0]],
self.vocab_embedding_W[tgt_action_seq[:, :, 1]])
tgt_action_seq_embed_tm1 = tensor_right_shift(tgt_action_seq_embed)
# parent rule application embeddings
tgt_par_rule_embed = T.switch(tgt_par_rule_seq[:, :, None] < 0,
T.alloc(0., 1, config.rule_embed_dim),
self.rule_embedding_W[tgt_par_rule_seq])
if not config.frontier_node_type_feed:
tgt_node_embed *= 0.
if not config.parent_action_feed:
tgt_par_rule_embed *= 0.
# (batch_size, max_example_action_num, action_embed_dim + symbol_embed_dim + action_embed_dim)
decoder_input = T.concatenate(
[tgt_action_seq_embed_tm1, tgt_node_embed, tgt_par_rule_embed],
axis=-1)
# (batch_size, max_query_length, query_embed_dim)
query_embed = self.query_encoder_lstm(query_token_embed,
mask=query_token_embed_mask,
dropout=config.dropout,
srng=self.srng)
# (batch_size, max_example_action_num)
tgt_action_seq_mask = T.any(tgt_action_seq_type, axis=-1)
# decoder_hidden_states: (batch_size, max_example_action_num, lstm_hidden_state)
# ctx_vectors: (batch_size, max_example_action_num, encoder_hidden_dim)
decoder_hidden_states, _, ctx_vectors = self.decoder_lstm(
decoder_input,
context=query_embed,
context_mask=query_token_embed_mask,
mask=tgt_action_seq_mask,
parent_t_seq=tgt_par_t_seq,
dropout=config.dropout,
srng=self.srng)
# if DECODER_DROPOUT > 0:
# logging.info('used dropout for decoder output, p = %f', DECODER_DROPOUT)
# decoder_hidden_states = Dropout(DECODER_DROPOUT, self.srng)(decoder_hidden_states)
# ====================================================
# apply additional non-linearity transformation before
# predicting actions
# ====================================================
decoder_hidden_state_trans_rule = self.decoder_hidden_state_W_rule(
decoder_hidden_states)
decoder_hidden_state_trans_token = self.decoder_hidden_state_W_token(
T.concatenate([decoder_hidden_states, ctx_vectors], axis=-1))
# (batch_size, max_example_action_num, rule_num)
rule_predict = softmax(
T.dot(decoder_hidden_state_trans_rule,
T.transpose(self.rule_embedding_W)) + self.rule_embedding_b)
# (batch_size, max_example_action_num, 2)
terminal_gen_action_prob = self.terminal_gen_softmax(
decoder_hidden_states)
# (batch_size, max_example_action_num, target_vocab_size)
logits = T.dot(decoder_hidden_state_trans_token,
T.transpose(
self.vocab_embedding_W)) + self.vocab_embedding_b
# vocab_predict = softmax(T.dot(decoder_hidden_state_trans_token, T.transpose(self.vocab_embedding_W)) + self.vocab_embedding_b)
vocab_predict = softmax(
logits.transpose(1, 0, 2) * mask +
(T.min(logits.transpose(1, 0, 2), axis=1, keepdims=True) - 1) *
(1 - mask)).transpose(1, 0, 2)
# (batch_size, max_example_action_num, lstm_hidden_state + encoder_hidden_dim)
ptr_net_decoder_state = T.concatenate(
[decoder_hidden_states, ctx_vectors], axis=-1)
# (batch_size, max_example_action_num, max_query_length)
copy_prob = self.src_ptr_net(query_embed, query_token_embed_mask,
ptr_net_decoder_state)
# (batch_size, max_example_action_num)
rule_tgt_prob = rule_predict[
T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 0]]
# (batch_size, max_example_action_num)
vocab_tgt_prob = vocab_predict[
T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 1]]
# (batch_size, max_example_action_num)
copy_tgt_prob = copy_prob[
T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 2]]
# (batch_size, max_example_action_num)
tgt_prob = tgt_action_seq_type[:, :, 0] * rule_tgt_prob + \
tgt_action_seq_type[:, :, 1] * terminal_gen_action_prob[:, :, 0] * vocab_tgt_prob + \
tgt_action_seq_type[:, :, 2] * terminal_gen_action_prob[:, :, 1] * copy_tgt_prob
likelihood = T.log(tgt_prob + 1.e-7 * (1 - tgt_action_seq_mask))
loss = -(likelihood * tgt_action_seq_mask).sum(
axis=-1) # / tgt_action_seq_mask.sum(axis=-1)
loss = T.mean(loss)
# let's build the function!
train_inputs = [
query_tokens, tgt_action_seq, tgt_action_seq_type, tgt_node_seq,
tgt_par_rule_seq, tgt_par_t_seq, mask
]
optimizer = optimizers.get(config.optimizer)
optimizer.clip_grad = config.clip_grad
updates, grads = optimizer.get_updates(self.params, loss)
self.train_func = theano.function(
train_inputs,
[loss],
# [loss, tgt_action_seq_type, tgt_action_seq,
# rule_tgt_prob, vocab_tgt_prob, copy_tgt_prob,
# copy_prob, terminal_gen_action_prob],
updates=updates)
# if WORD_DROPOUT > 0:
# self.build_decoder(query_tokens, query_token_embed_intact, query_token_embed_mask)
# else:
# self.build_decoder(query_tokens, query_token_embed, query_token_embed_mask)
self.build_decoder(query_tokens, query_token_embed,
query_token_embed_mask, mask)
def logpow(x, m):
"""
Calculates log(x**m) since m*log(x) will fail when m, x = 0.
"""
# return m * log(x)
return T.switch(T.any(T.eq(x, 0)), -np.inf, m * T.log(x))
def pgrad(g_out):
g_out = T.clip(g_out, self.clip_lower_bound, self.clip_upper_bound)
g_out = ifelse(T.any(T.isnan(g_out)), T.ones_like(g_out)*0.00001, g_out)
return g_out
def mask(self, train=False):
X = self.get_input('input')(train)
return T.any(T.ones_like(X) * (1. - T.eq(X, self.mask_value)), axis=-1)
def get_output_mask(self, train=False):
X = self.get_input(train)
return T.any(T.ones_like(X) * (1.0 - T.eq(X, self.mask_value)), axis=-1)
def __init__(self, n_in, hidden_layer_size, n_out, L1_reg, L2_reg, hidden_layer_type, output_type='LINEAR', dropout_rate=0.0, optimizer='sgd', loss_function='MMSE', rnn_batch_training=False):
""" This function initialises a neural network
:param n_in: Dimensionality of input features
:type in: Integer
:param hidden_layer_size: The layer size for each hidden layer
:type hidden_layer_size: A list of integers
:param n_out: Dimensionality of output features
:type n_out: Integrer
:param hidden_layer_type: the activation types of each hidden layers, e.g., TANH, LSTM, GRU, BLSTM
:param L1_reg: the L1 regulasation weight
:param L2_reg: the L2 regulasation weight
:param output_type: the activation type of the output layer, by default is 'LINEAR', linear regression.
:param dropout_rate: probability of dropout, a float number between 0 and 1.
"""
logger = logging.getLogger("DNN initialization")
self.n_in = int(n_in)
self.n_out = int(n_out)
self.n_layers = len(hidden_layer_size)
self.dropout_rate = dropout_rate
self.optimizer = optimizer
self.loss_function = loss_function
self.is_train = T.iscalar('is_train')
self.rnn_batch_training = rnn_batch_training
assert len(hidden_layer_size) == len(hidden_layer_type)
self.list_of_activations = ['TANH', 'SIGMOID', 'SOFTMAX', 'RELU', 'RESU']
if self.rnn_batch_training:
self.x = T.tensor3('x')
self.y = T.tensor3('y')
else:
self.x = T.matrix('x')
self.y = T.matrix('y')
self.L1_reg = L1_reg
self.L2_reg = L2_reg
self.rnn_layers = []
self.params = []
self.delta_params = []
rng = np.random.RandomState(123)
for i in range(self.n_layers):
if i == 0:
input_size = n_in
else:
input_size = hidden_layer_size[i-1]
if i == 0:
layer_input = self.x
else:
layer_input = self.rnn_layers[i-1].output
if hidden_layer_type[i-1] == 'BSLSTM' or hidden_layer_type[i-1] == 'BLSTM':
input_size = hidden_layer_size[i-1]*2
if hidden_layer_type[i] in self.list_of_activations:
hidden_activation = hidden_layer_type[i].lower()
hidden_layer = GeneralLayer(rng, layer_input, input_size, hidden_layer_size[i], activation=hidden_activation, p=self.dropout_rate, training=self.is_train)
elif hidden_layer_type[i] == 'TANH_LHUC':
hidden_layer = SigmoidLayer_LHUC(rng, layer_input, input_size, hidden_layer_size[i], activation=T.tanh, p=self.dropout_rate, training=self.is_train)
elif hidden_layer_type[i] == 'SLSTM':
hidden_layer = SimplifiedLstm(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'SGRU':
hidden_layer = SimplifiedGRU(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'GRU':
hidden_layer = GatedRecurrentUnit(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_NFG':
hidden_layer = LstmNFG(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_NOG':
hidden_layer = LstmNOG(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_NIG':
hidden_layer = LstmNIG(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_NPH':
hidden_layer = LstmNoPeepholes(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM':
hidden_layer = VanillaLstm(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'BSLSTM':
hidden_layer = BidirectionSLstm(rng, layer_input, input_size, hidden_layer_size[i], hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'BLSTM':
hidden_layer = BidirectionLstm(rng, layer_input, input_size, hidden_layer_size[i], hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'RNN':
hidden_layer = VanillaRNN(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_LHUC':
hidden_layer = VanillaLstm_LHUC(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
else:
logger.critical("This hidden layer type: %s is not supported right now! \n Please use one of the following: SLSTM, BSLSTM, TANH, SIGMOID\n" %(hidden_layer_type[i]))
sys.exit(1)
self.rnn_layers.append(hidden_layer)
self.params.extend(hidden_layer.params)
input_size = hidden_layer_size[-1]
if hidden_layer_type[-1] == 'BSLSTM' or hidden_layer_type[-1] == 'BLSTM':
input_size = hidden_layer_size[-1]*2
output_activation = output_type.lower()
if output_activation == 'linear':
self.final_layer = LinearLayer(rng, self.rnn_layers[-1].output, input_size, self.n_out)
elif output_activation == 'recurrent':
self.final_layer = RecurrentOutputLayer(rng, self.rnn_layers[-1].output, input_size, self.n_out, rnn_batch_training=self.rnn_batch_training)
elif output_type.upper() in self.list_of_activations:
self.final_layer = GeneralLayer(rng, self.rnn_layers[-1].output, input_size, self.n_out, activation=output_activation)
else:
logger.critical("This output layer type: %s is not supported right now! \n Please use one of the following: LINEAR, BSLSTM\n" %(output_type))
sys.exit(1)
self.params.extend(self.final_layer.params)
self.updates = {}
for param in self.params:
self.updates[param] = theano.shared(value = np.zeros(param.get_value(borrow = True).shape,
dtype = theano.config.floatX), name = 'updates')
if self.loss_function == 'CCE':
self.finetune_cost = self.categorical_crossentropy_loss(self.final_layer.output, self.y)
self.errors = self.categorical_crossentropy_loss(self.final_layer.output, self.y)
elif self.loss_function == 'Hinge':
self.finetune_cost = self.multiclass_hinge_loss(self.final_layer.output, self.y)
self.errors = self.multiclass_hinge_loss(self.final_layer.output, self.y)
elif self.loss_function == 'MMSE':
if self.rnn_batch_training:
self.y_mod = T.reshape(self.y, (-1, n_out))
self.final_layer_output = T.reshape(self.final_layer.output, (-1, n_out))
nonzero_rows = T.any(self.y_mod, 1).nonzero()
self.y_mod = self.y_mod[nonzero_rows]
self.final_layer_output = self.final_layer_output[nonzero_rows]
self.finetune_cost = T.mean(T.sum((self.final_layer_output - self.y_mod) ** 2, axis=1))
self.errors = T.mean(T.sum((self.final_layer_output - self.y_mod) ** 2, axis=1))
else:
self.finetune_cost = T.mean(T.sum((self.final_layer.output - self.y) ** 2, axis=1))
self.errors = T.mean(T.sum((self.final_layer.output - self.y) ** 2, axis=1))
def get_reward(self,session_states,session_actions,batch_i):
"""
WARNING! this runs on a single session, not on a batch
reward given for taking the action in current environment state
arguments:
session_states float[batch_id, memory_id]: environment state before taking action
session_actions int[batch_id]: agent action at this tick
returns:
reward float[batch_id]: reward for taking action from the given state
"""
#unpach states and actions
session_states = check_list(session_states)[0]
session_actions = check_list(session_actions)[0]
time_range = T.arange(session_actions.shape[0])
has_tried_already = session_states[time_range,session_actions]
session_is_active = T.eq(session_states[:,self.end_action_id],0)
has_finished_now = T.eq(session_actions,self.end_action_id)
has_finished_now = T.set_subtensor(has_finished_now[-1],1)
end_tick = has_finished_now.nonzero()[0][0]
action_is_categorical = in1d(session_actions, self.category_action_ids)
response = self.joint_data[batch_i,session_actions].ravel()
at_least_one_category_guessed = T.any(action_is_categorical[:end_tick] & (response[:end_tick]>0))
#categorical and attributes
reward_for_intermediate_action = T.switch(
action_is_categorical,
response*(self.rw["category_positive"]-self.rw["category_negative"]) + self.rw["category_negative"],
response*(self.rw["attribute_positive"]-self.rw["attribute_negative"]) + self.rw["attribute_negative"]
)
reward_for_intermediate_action_first_time = T.switch(
has_tried_already,
self.rw["repeated_poll"],
reward_for_intermediate_action,
)
#ending session
reward_for_end_action = T.switch(at_least_one_category_guessed, #if chosen at least 1 category
self.rw["end_action"], #do not penalize
self.rw["end_action_if_no_category_predicted"]) #else punish
#include end action
reward_for_action = T.switch(
has_finished_now,
reward_for_end_action,
reward_for_intermediate_action_first_time,
)
final_reward = T.switch(
session_is_active,
reward_for_action,
0,
)
return final_reward.astype(theano.config.floatX)
def unroll_scan(fn, sequences=(), outputs_info=(), non_sequences=(), n_steps=None,
go_backwards=False):
"""
Helper function to unroll for loops. Can be used to unroll theano.scan.
The parameter names are identical to theano.scan, please refer to here
for more information.
Note that this function does not support the truncate_gradient
setting from theano.scan.
Code adapted from https://github.com/Lasagne/Lasagne.
Thank you!
Parameters
----------
fn : function
Function that defines calculations at each step.
sequences : TensorVariable or list of TensorVariables
List of TensorVariable with sequence data. The function iterates
over the first dimension of each TensorVariable.
outputs_info : list of TensorVariables
List of tensors specifying the initial values for each recurrent
value.
non_sequences: list of TensorVariables
List of theano.shared variables that are used in the step function.
n_steps: int
Number of steps to unroll.
go_backwards: bool
If true the recursion starts at sequences[-1] and iterates
backwards.
Returns
-------
Tuple of the form (outputs, updates).
outputs is a list of TensorVariables. Each element in the list gives the recurrent
values at each time step.
updates is an empty dict for now.
"""
if not isinstance(sequences, (list, tuple)):
sequences = [sequences]
sequences = list(sequences)
outputs_info = list(outputs_info)
non_sequences = list(non_sequences)
# When backwards reverse the recursion direction
counter = range(n_steps)
if go_backwards:
counter = counter[::-1]
output = []
prev_vals = outputs_info
until = []
for i in counter:
assert len(prev_vals) == len(outputs_info)
prev_vals = [prev for prev, out_info in zip(prev_vals, outputs_info) if out_info is not None]
step_input = [s[i] for s in sequences] + prev_vals + non_sequences
out_ = fn(*step_input)
# The returned values from step can be either a TensorVariable,
# a list, or a tuple. Below, we force it to always be a list.
if isinstance(out_, T.TensorVariable):
out_ = [out_]
if isinstance(out_, tuple):
if len(out_) >= 1 and isinstance(out_[0], (list, tuple)):
if len(out_) >= 2:
assert not out_[1], "shared var updates not supported"
if len(out_) >= 3:
assert isinstance(out_[2], theano.scan_module.until)
until.append(T.neq(out_[2].condition, 0))
out_ = list(out_[0])
else:
out_ = list(out_)
output.append(out_)
prev_vals = output[-1]
# iterate over each scan output and convert it to same format as scan:
# [[output11, output12,...output1n],
# [output21, output22,...output2n],...]
output_scan = []
for i in range(len(output[0])):
l = map(lambda x: x[i], output)
output_scan.append(T.stack(*l))
if until:
assert len(until) == n_steps
until_conds = T.stack(*until)
new_len = T.switch(T.any(until_conds),
T.minimum(T.argmax(until_conds) + 1, n_steps),
n_steps)
output_scan = [out[:new_len] for out in output_scan]
if len(output_scan) == 1:
output_scan = output_scan[0]
updates = {}
return output_scan, updates
def __init__(self, n_in, hidden_layer_size, n_out, L1_reg, L2_reg, hidden_layer_type, output_type='LINEAR', network_type='S2S', ed_type='HED', dropout_rate=0.0, optimizer='sgd', MLU_div_lengths = [], loss_function='MMSE', rnn_batch_training=False):
""" This function initialises a neural network
:param n_in: Dimensionality of input features
:type in: Integer
:param hidden_layer_size: The layer size for each hidden layer
:type hidden_layer_size: A list of integers
:param n_out: Dimensionality of output features
:type n_out: Integrer
:param hidden_layer_type: the activation types of each hidden layers, e.g., TANH, LSTM, GRU, BLSTM
:param L1_reg: the L1 regulasation weight
:param L2_reg: the L2 regulasation weight
:param output_type: the activation type of the output layer, by default is 'LINEAR', linear regression.
:param dropout_rate: probability of dropout, a float number between 0 and 1.
"""
logger = logging.getLogger("DNN initialization")
self.n_in = int(n_in)
self.n_out = int(n_out)
self.n_layers = len(hidden_layer_size)
self.dropout_rate = dropout_rate
self.optimizer = optimizer
self.loss_function = loss_function
self.is_train = T.iscalar('is_train')
self.rnn_batch_training = rnn_batch_training
assert len(hidden_layer_size) == len(hidden_layer_type)
self.list_of_activations = ['TANH', 'SIGMOID', 'SOFTMAX', 'RELU', 'RESU']
BLSTM_variants = ['BLSTM', 'BSLSTM', 'BLSTME', 'BSLSTME']
Encoder_variants = ['RNNE', 'LSTME', 'BLSTME', 'SLSTME', 'TANHE']
Decoder_variants = ['RNND', 'LSTMD', 'SLSTMD']
if self.rnn_batch_training:
self.x = T.tensor3('x')
self.y = T.tensor3('y')
else:
self.x = T.matrix('x')
self.y = T.matrix('y')
if network_type == "S2S":
self.d = T.ivector('d')
self.f = T.matrix('f')
self.L1_reg = L1_reg
self.L2_reg = L2_reg
self.rnn_layers = []
self.params = []
self.delta_params = []
rng = np.random.RandomState(123)
prev_seg_end = 0
encoder_count = 0
MLU_div = MLU_div_lengths
for i in range(self.n_layers):
if i == 0:
input_size = n_in
else:
input_size = hidden_layer_size[i-1]
if hidden_layer_type[i-1] in BLSTM_variants:
input_size = hidden_layer_size[i-1]*2
if i == 0:
layer_input = self.x
else:
layer_input = self.rnn_layers[i-1].output
### sequence-to-sequence mapping ###
if hidden_layer_type[i-1] in Encoder_variants:
dur_input = self.d
frame_feat_input = self.f
# vanilla encoder-decoder (phone-level features)
if ed_type == "VED":
seq2seq_model = DistributedSequenceEncoder(rng, layer_input, dur_input)
layer_input = T.concatenate((seq2seq_model.encoded_output, frame_feat_input), axis=1)
input_size = input_size+4
# hierarchical encoder-decoder
elif ed_type == "HED":
seg_len = layer_input.size//input_size
seg_dur_input = dur_input[prev_seg_end: prev_seg_end+seg_len]
num_of_segs = T.sum(seg_dur_input)
seq2seq_model = DistributedSequenceEncoder(rng, layer_input, seg_dur_input)
addfeat_input = frame_feat_input[0:num_of_segs, MLU_div[encoder_count]:MLU_div[encoder_count+1]]
layer_input = T.concatenate((seq2seq_model.encoded_output, addfeat_input), axis=1)
input_size = input_size + (MLU_div[encoder_count+1]-MLU_div[encoder_count])
prev_seg_end = prev_seg_end + seg_len
encoder_count = encoder_count + 1
# hidden layer activation
if hidden_layer_type[i] in self.list_of_activations:
hidden_activation = hidden_layer_type[i].lower()
hidden_layer = GeneralLayer(rng, layer_input, input_size, hidden_layer_size[i], activation=hidden_activation, p=self.dropout_rate, training=self.is_train)
elif hidden_layer_type[i] == 'TANHE' or hidden_layer_type[i] == 'SIGMOIDE':
hidden_activation = hidden_layer_type[i][0:-1].lower()
hidden_layer = GeneralLayer(rng, layer_input, input_size, hidden_layer_size[i], activation=hidden_activation, p=self.dropout_rate, training=self.is_train)
elif hidden_layer_type[i] == 'TANH_LHUC':
hidden_layer = SigmoidLayer_LHUC(rng, layer_input, input_size, hidden_layer_size[i], activation=T.tanh, p=self.dropout_rate, training=self.is_train)
elif hidden_layer_type[i] == 'SLSTM' or hidden_layer_type[i] == 'SLSTME':
hidden_layer = SimplifiedLstm(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'SLSTMD':
hidden_layer = SimplifiedLstmDecoder(rng, layer_input, input_size, hidden_layer_size[i], self.n_out, p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'SGRU':
hidden_layer = SimplifiedGRU(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'GRU':
hidden_layer = GatedRecurrentUnit(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM' or hidden_layer_type[i] == 'LSTME':
hidden_layer = VanillaLstm(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTMD':
hidden_layer = VanillaLstmDecoder(rng, layer_input, input_size, hidden_layer_size[i], self.n_out, p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'BSLSTM' or hidden_layer_type[i] == 'BSLSTME':
hidden_layer = BidirectionSLstm(rng, layer_input, input_size, hidden_layer_size[i], hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'BLSTM' or hidden_layer_type[i] == 'BLSTME':
hidden_layer = BidirectionLstm(rng, layer_input, input_size, hidden_layer_size[i], hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'RNN' or hidden_layer_type[i] == 'RNNE':
hidden_layer = VanillaRNN(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'RNND':
hidden_layer = VanillaRNNDecoder(rng, layer_input, input_size, hidden_layer_size[i], self.n_out, p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_LHUC':
hidden_layer = VanillaLstm_LHUC(rng, layer_input, input_size, hidden_layer_size[i], p=self.dropout_rate, training=self.is_train, rnn_batch_training=self.rnn_batch_training)
else:
logger.critical("This hidden layer type: %s is not supported right now! \n Please use one of the following: SLSTM, BSLSTM, TANH, SIGMOID\n" %(hidden_layer_type[i]))
sys.exit(1)
self.rnn_layers.append(hidden_layer)
self.params.extend(hidden_layer.params)
input_size = hidden_layer_size[-1]
if hidden_layer_type[-1] in BLSTM_variants:
input_size = hidden_layer_size[-1]*2
if hidden_layer_type[-1] in Decoder_variants:
self.final_layer = self.rnn_layers[-1]
else:
output_activation = output_type.lower()
if output_activation == 'linear':
self.final_layer = LinearLayer(rng, self.rnn_layers[-1].output, input_size, self.n_out)
elif output_activation == 'recurrent':
self.final_layer = RecurrentOutputLayer(rng, self.rnn_layers[-1].output, input_size, self.n_out, rnn_batch_training=self.rnn_batch_training)
elif output_type.upper() in self.list_of_activations:
self.final_layer = GeneralLayer(rng, self.rnn_layers[-1].output, input_size, self.n_out, activation=output_activation)
else:
logger.critical("This output layer type: %s is not supported right now! \n Please use one of the following: LINEAR, BSLSTM\n" %(output_type))
sys.exit(1)
self.params.extend(self.final_layer.params)
self.updates = {}
for param in self.params:
self.updates[param] = theano.shared(value = np.zeros(param.get_value(borrow = True).shape,
dtype = theano.config.floatX), name = 'updates')
if self.loss_function == 'CCE':
self.finetune_cost = self.categorical_crossentropy_loss(self.final_layer.output, self.y)
self.errors = self.categorical_crossentropy_loss(self.final_layer.output, self.y)
elif self.loss_function == 'Hinge':
self.finetune_cost = self.multiclass_hinge_loss(self.final_layer.output, self.y)
self.errors = self.multiclass_hinge_loss(self.final_layer.output, self.y)
elif self.loss_function == 'MMSE':
if self.rnn_batch_training:
self.y_mod = T.reshape(self.y, (-1, n_out))
self.final_layer_output = T.reshape(self.final_layer.output, (-1, n_out))
nonzero_rows = T.any(self.y_mod, 1).nonzero()
self.y_mod = self.y_mod[nonzero_rows]
self.final_layer_output = self.final_layer_output[nonzero_rows]
self.finetune_cost = T.mean(T.sum((self.final_layer_output - self.y_mod) ** 2, axis=1))
self.errors = T.mean(T.sum((self.final_layer_output - self.y_mod) ** 2, axis=1))
else:
self.finetune_cost = T.mean(T.sum((self.final_layer.output - self.y) ** 2, axis=1))
self.errors = T.mean(T.sum((self.final_layer.output - self.y) ** 2, axis=1))
def compute_step(self, param, previous_step):
not_finite = tensor.any(tensor.or_(
tensor.isnan(previous_step), tensor.isinf(previous_step)))
step = tensor.switch(not_finite, self.scaler * param, previous_step)
return step, []
def logpow(x, m):
"""
Calculates log(x**m) since m*log(x) will fail when m, x = 0.
"""
# return m * log(x)
return switch(any(eq(x, 0)), -inf, m * log(x))
def get_output(self, train=False):
X = self.get_input(train)
return X * T.shape_padright(T.any((1.0 - T.eq(X, self.mask_value)), axis=-1))
def build(self):
# (batch_size, max_example_action_num, action_type)
tgt_action_seq = ndim_itensor(3, 'tgt_action_seq')
# (batch_size, max_example_action_num, action_type)
tgt_action_seq_type = ndim_itensor(3, 'tgt_action_seq_type')
# (batch_size, max_example_action_num)
tgt_node_seq = ndim_itensor(2, 'tgt_node_seq')
# (batch_size, max_example_action_num)
tgt_par_rule_seq = ndim_itensor(2, 'tgt_par_rule_seq')
# (batch_size, max_example_action_num)
tgt_par_t_seq = ndim_itensor(2, 'tgt_par_t_seq')
# (batch_size, max_example_action_num, symbol_embed_dim)
# tgt_node_embed = self.node_embedding(tgt_node_seq, mask_zero=False)
tgt_node_embed = self.node_embedding[tgt_node_seq]
# (batch_size, max_query_length)
query_tokens = ndim_itensor(2, 'query_tokens')
# (batch_size, max_query_length, query_token_embed_dim)
# (batch_size, max_query_length)
query_token_embed, query_token_embed_mask = self.query_embedding(query_tokens, mask_zero=True)
# if WORD_DROPOUT > 0:
# logging.info('used word dropout for source, p = %f', WORD_DROPOUT)
# query_token_embed, query_token_embed_intact = WordDropout(WORD_DROPOUT, self.srng)(query_token_embed, False)
batch_size = tgt_action_seq.shape[0]
max_example_action_num = tgt_action_seq.shape[1]
# previous action embeddings
# (batch_size, max_example_action_num, action_embed_dim)
tgt_action_seq_embed = T.switch(T.shape_padright(tgt_action_seq[:, :, 0] > 0),
self.rule_embedding_W[tgt_action_seq[:, :, 0]],
self.vocab_embedding_W[tgt_action_seq[:, :, 1]])
tgt_action_seq_embed_tm1 = tensor_right_shift(tgt_action_seq_embed)
# parent rule application embeddings
tgt_par_rule_embed = T.switch(tgt_par_rule_seq[:, :, None] < 0,
T.alloc(0., 1, config.rule_embed_dim),
self.rule_embedding_W[tgt_par_rule_seq])
if not config.frontier_node_type_feed:
tgt_node_embed *= 0.
if not config.parent_action_feed:
tgt_par_rule_embed *= 0.
# (batch_size, max_example_action_num, action_embed_dim + symbol_embed_dim + action_embed_dim)
decoder_input = T.concatenate([tgt_action_seq_embed_tm1, tgt_node_embed, tgt_par_rule_embed], axis=-1)
# (batch_size, max_query_length, query_embed_dim)
query_embed = self.query_encoder_lstm(query_token_embed, mask=query_token_embed_mask,
dropout=config.dropout, srng=self.srng)
# (batch_size, max_example_action_num)
tgt_action_seq_mask = T.any(tgt_action_seq_type, axis=-1)
# decoder_hidden_states: (batch_size, max_example_action_num, lstm_hidden_state)
# ctx_vectors: (batch_size, max_example_action_num, encoder_hidden_dim)
decoder_hidden_states, _, ctx_vectors = self.decoder_lstm(decoder_input,
context=query_embed,
context_mask=query_token_embed_mask,
mask=tgt_action_seq_mask,
parent_t_seq=tgt_par_t_seq,
dropout=config.dropout,
srng=self.srng)
# if DECODER_DROPOUT > 0:
# logging.info('used dropout for decoder output, p = %f', DECODER_DROPOUT)
# decoder_hidden_states = Dropout(DECODER_DROPOUT, self.srng)(decoder_hidden_states)
# ====================================================
# apply additional non-linearity transformation before
# predicting actions
# ====================================================
decoder_hidden_state_trans_rule = self.decoder_hidden_state_W_rule(decoder_hidden_states)
decoder_hidden_state_trans_token = self.decoder_hidden_state_W_token(T.concatenate([decoder_hidden_states, ctx_vectors], axis=-1))
# (batch_size, max_example_action_num, rule_num)
rule_predict = softmax(T.dot(decoder_hidden_state_trans_rule, T.transpose(self.rule_embedding_W)) + self.rule_embedding_b)
# (batch_size, max_example_action_num, 2)
terminal_gen_action_prob = self.terminal_gen_softmax(decoder_hidden_states)
# (batch_size, max_example_action_num, target_vocab_size)
vocab_predict = softmax(T.dot(decoder_hidden_state_trans_token, T.transpose(self.vocab_embedding_W)) + self.vocab_embedding_b)
# (batch_size, max_example_action_num, lstm_hidden_state + encoder_hidden_dim)
ptr_net_decoder_state = T.concatenate([decoder_hidden_states, ctx_vectors], axis=-1)
# (batch_size, max_example_action_num, max_query_length)
copy_prob = self.src_ptr_net(query_embed, query_token_embed_mask, ptr_net_decoder_state)
# (batch_size, max_example_action_num)
rule_tgt_prob = rule_predict[T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 0]]
# (batch_size, max_example_action_num)
vocab_tgt_prob = vocab_predict[T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 1]]
# (batch_size, max_example_action_num)
copy_tgt_prob = copy_prob[T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 2]]
# (batch_size, max_example_action_num)
tgt_prob = tgt_action_seq_type[:, :, 0] * rule_tgt_prob + \
tgt_action_seq_type[:, :, 1] * terminal_gen_action_prob[:, :, 0] * vocab_tgt_prob + \
tgt_action_seq_type[:, :, 2] * terminal_gen_action_prob[:, :, 1] * copy_tgt_prob
likelihood = T.log(tgt_prob + 1.e-7 * (1 - tgt_action_seq_mask))
loss = - (likelihood * tgt_action_seq_mask).sum(axis=-1) # / tgt_action_seq_mask.sum(axis=-1)
loss = T.mean(loss)
# let's build the function!
train_inputs = [query_tokens, tgt_action_seq, tgt_action_seq_type,
tgt_node_seq, tgt_par_rule_seq, tgt_par_t_seq]
optimizer = optimizers.get(config.optimizer)
optimizer.clip_grad = config.clip_grad
updates, grads = optimizer.get_updates(self.params, loss)
self.train_func = theano.function(train_inputs, [loss],
# [loss, tgt_action_seq_type, tgt_action_seq,
# rule_tgt_prob, vocab_tgt_prob, copy_tgt_prob,
# copy_prob, terminal_gen_action_prob],
updates=updates)
# if WORD_DROPOUT > 0:
# self.build_decoder(query_tokens, query_token_embed_intact, query_token_embed_mask)
# else:
# self.build_decoder(query_tokens, query_token_embed, query_token_embed_mask)
self.build_decoder(query_tokens, query_token_embed, query_token_embed_mask)
