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Updater.py
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Updater.py
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from __future__ import print_function
import theano
import numpy
import os
from Log import log
from math import sqrt
import theano.tensor.shared_randomstreams
import theano.tensor as T
import theano.ifelse
import theano.compile
from TheanoUtil import opt_contiguous_on_gpu
class Updater:
"""
This defines how to update the model parameters per mini-batch.
All kind of gradient-based optimization methods are implemented here, such as Adam etc.
"""
@classmethod
def initFromConfig(cls, config):
"""
Will construct a :class:`Updater` instance where all params are automatically determined by the given config.
:param Config.Config config:
:rtype: Updater
"""
kwargs = {}
for k, v in cls._get_kwarg_defaults().items():
if type(v) == bool: g = config.bool
elif type(v) == float: g = config.float
elif type(v) == int: g = config.int
else: assert False, "invalid default type: %s = (%s) %s" % (k, type(v), v)
kwargs[k] = g(k, v)
return cls(**kwargs)
@classmethod
def initRule(cls, rule, **kwargs):
if rule != "default":
kwargs[rule] = True
return cls(**kwargs)
@classmethod
def _get_kwarg_defaults(cls):
import inspect
arg_spec = inspect.getargspec(cls.__init__)
N_defs = len(arg_spec.defaults)
N_args = len(arg_spec.args)
defaults = {arg_spec.args[N_args - N_defs + i]: d for i, d in enumerate(arg_spec.defaults)}
return defaults
# Note that the default value type is important for initFromConfig to determine
# whether to call config.bool/config.int/etc.
def __init__(self,
momentum=0.0, nesterov_momentum=0.0, momentum2=0.0,
gradient_clip=-1.0,
update_clip=-1.0,
weight_clip=0.0,
adagrad=False,
adadelta=False, adadelta_decay=0.90, adadelta_offset=1e-6,
max_norm=0.0,
adasecant=False,
adam=False,
adamdelta=False,
adam_fit_learning_rate=True,
adamax=False,
nadam=False,
nadam_decay=0.004, # Magical 250.0 denominator in nesterov scaling of i_t
eve=False,
gradient_l2_norm=False,
mean_normalized_sgd=False,
mean_normalized_sgd_average_interpolation=0.5,
rmsprop=0.0,
smorms3=False,
update_multiple_models=0, update_multiple_models_average_step=0,
update_multiple_models_average_step_i=0, update_multiple_models_averaging=True,
update_multiple_models_param_is_cur_model=False,
multi_batch_update=0,
variance_reduction=False,
enforce_triangular_matrix_zero=False,
gradient_noise=0.0,
gradient_noise_decay=0.55,
grad_noise_rel_grad_norm=0.0,
reset_update_params=False
):
"""
Initializes the Updater class.
All the params determine the specific optimization variants and their hyper params.
Normally this is constructed by :func:`Updater.initFromConfig`.
:param momentum:
:param nesterov_momentum:
:param momentum2:
:param gradient_clip:
:param update_clip:
:param adagrad:
:param adadelta:
:param adadelta_decay:
:param adadelta_offset:
:param max_norm:
:param adasecant:
:param adam:
:param adamdelta:
:param adam_fit_learning_rate:
:param adamax:
:param eve: Eve optimizer - Adam with a feedback from loss
:param adamvr: Adam with adasecant variance reduction
:param nadam: Adam with nag part momentum
:param nadam_decay:
:param mean_normalized_sgd:
:param mean_normalized_sgd_average_interpolation:
:param rmsprop:
:param smorms3:
:param update_multiple_models:
:param update_multiple_models_average_step:
:param update_multiple_models_average_step_i:
:param update_multiple_models_averaging:
:param update_multiple_models_param_is_cur_model:
:param multi_batch_update:
:param enforce_triangular_matrix_zero:
:param gradient_noise:
:param gradient_noise_decay:
:param grad_noise_rel_grad_norm:
:param reset_update_params:
"""
self.rng = numpy.random.RandomState(0o101)
self.momentum = numpy.float32(momentum)
self.nesterov_momentum = numpy.float32(nesterov_momentum)
self.momentum2 = numpy.float32(momentum2)
self.gradient_clip = numpy.float32(gradient_clip)
self.update_clip = numpy.float32(update_clip)
self.weight_clip = numpy.float32(weight_clip)
self.max_norm = max_norm
self.adagrad = adagrad
self.adadelta = adadelta
self.adadelta_decay = numpy.float32(adadelta_decay)
self.adadelta_offset = numpy.float32(adadelta_offset)
self.adasecant = adasecant
self.nadam = nadam
self.nadam_decay = nadam_decay
self.adam = adam
self.eve = eve
self.adamdelta = adamdelta
self.adam_fit_learning_rate = adam_fit_learning_rate
self.variance_reduction = variance_reduction
self.adamax = adamax
self.mean_normalized_sgd = mean_normalized_sgd
self.mean_normalized_sgd_average_interpolation = numpy.float32(mean_normalized_sgd_average_interpolation)
self.rmsprop = rmsprop
self.smorms3 = smorms3
self.update_multiple_models = update_multiple_models
self.update_multiple_models_averaging = update_multiple_models_averaging
self.update_multiple_models_average_step = update_multiple_models_average_step
self.update_multiple_models_average_step_i = update_multiple_models_average_step_i
self.update_multiple_models_param_is_cur_model = update_multiple_models_param_is_cur_model
self.multi_batch_update = multi_batch_update
self.enforce_triangular_matrix_zero = enforce_triangular_matrix_zero
self.gradient_noise = gradient_noise
self.gradient_noise_decay = gradient_noise_decay
self.grad_noise_rel_grad_norm = grad_noise_rel_grad_norm
self.reset_update_params = reset_update_params
self.gradient_l2_norm = gradient_l2_norm
self.device = str(theano.config.device)
self.params = {}
self.pid = -1
if self.adadelta or self.adamdelta:
self.momentum = 0.0
self.nesterov_momentum = 0.0
self.momentum2 = 0.0
print("using adadelta with decay", self.adadelta_decay, ", offset", self.adadelta_offset, file=log.v4)
if self.adagrad:
print("using adagrad", file=log.v4)
if self.momentum:
print("using momentum %f" % self.momentum, file=log.v4)
if self.nesterov_momentum:
print("using simplified nesterov momentum %f" % self.nesterov_momentum, file=log.v4)
if self.momentum2:
print("using reverted momentum %f" % self.momentum2, file=log.v4)
if self.gradient_clip > 0:
print("using gradient clipping %f" % self.gradient_clip, file=log.v4)
if self.update_clip > 0:
print("using update clipping %f" % self.update_clip, file=log.v4)
if self.rmsprop:
print("using RMSProp with rho = %f" % self.rmsprop, file=log.v4)
if self.smorms3:
print("using SMORMS3", file=log.v4)
if self.adamax:
print("using AdaMax with b1 = 0.9 and b2 = 0.999", file=log.v4)
if self.adam:
print("using adam", file=log.v4)
if self.nadam:
print("using adam with nag and momentum schedule", file=log.v4)
if self.eve:
print("using eve optimizer (Adam with feedback)", file=log.v4)
def initVars(self, network, net_param_deltas):
"""
Initializes the Theano shared variables.
This should be called in the process where you want to do the updating.
All further calls must be from the same process.
The network.gparams must be created in the same process.
:type network: Network.LayerNetwork
:type net_param_deltas: dict[theano.compile.sharedvalue.SharedVariable,theano.Variable] | None
"""
assert not self.isInitialized
self.pid = os.getpid()
self.network = network
if net_param_deltas is not None:
self.update_on_device = True
self.net_train_param_deltas = net_param_deltas
else:
self.update_on_device = False
self.net_train_param_deltas = {p : theano.shared(numpy.zeros(p.get_value(borrow=True,
return_internal_type=True).shape,
dtype=theano.config.floatX))
for p in network.train_params_vars}
" :type: dict[theano.compile.sharedvalue.SharedVariable,theano.compile.sharedvalue.SharedVariable] "
self.learning_rate_var = theano.shared(value=numpy.cast[theano.config.floatX](0), name="learning_rate")
" :type: theano.compile.sharedvalue.SharedVariable "
self.i = self.var(numpy.float32(0 if self.reset_update_params else network.update_step), name="updater_i")
self.e = self.var(numpy.float32(0 if self.reset_update_params else network.update_step), name="updater_epoch")
if self.momentum > 0:
self.deltas = {p: self.var(p, zero=True, name="momentum_deltas_%s" % p.name)
for p in network.train_params_vars}
if self.adagrad:
self.accu = {p: self.var(p, zero=True, name="adagrad_accu_%s" % p.name)
for p in network.train_params_vars}
if self.adadelta or self.adamdelta:
# http://arxiv.org/pdf/1212.5701v1.pdf
self.eg2 = {p: self.var(p, zero=True, name="adadelta_eg2_%s" % p.name)
for p in self.network.train_params_vars} #E[g^2]
self.edx2 = {p: self.var(p, zero=True, name="adadelta_edx2_%s" % p.name)
for p in self.network.train_params_vars} #E[\delta x^2]
self.dx = {p: self.var(p, zero=True, name="adadelta_dx_%s" % p.name)
for p in self.network.train_params_vars} #\delta x
@property
def isInitialized(self):
return self.pid >= 0
def setNetParamDeltas(self, net_param_deltas):
assert self.pid == os.getpid()
assert self.update_on_device == False
for p in net_param_deltas:
self.net_train_param_deltas[p].set_value(net_param_deltas[p], borrow=True)
def norm_constraint(self, tensor_var, max_norm, norm_axes=None, epsilon=1e-12):
ndim = tensor_var.ndim
if norm_axes is not None:
sum_over = tuple(norm_axes)
elif ndim == 2: # DenseLayer
sum_over = (0,)
elif ndim == 3: # Depth
sum_over = (0,2)
else:
sum_over = (0,)
dtype = numpy.dtype(theano.config.floatX).type
norms = T.sqrt(T.sum(T.sqr(tensor_var), axis=sum_over, keepdims=True))
target_norms = T.clip(norms, 0, dtype(max_norm))
constrained_output = \
(tensor_var * (target_norms / (dtype(epsilon) + norms)))
return constrained_output
def _var_get_value(self, value, zero=False, dtype="float32"):
if zero:
if isinstance(value, theano.compile.SharedVariable):
value = value.get_value(borrow=True, return_internal_type=True)
shape = value.shape
value = numpy.zeros(shape, dtype=dtype)
else:
if isinstance(value, theano.compile.SharedVariable):
value = value.get_value()
value = numpy.asarray(value).astype(dtype)
return value
def var(self, value, name="", broadcastable=None, dtype="float32", zero=False):
orig_value = value
if broadcastable is None and isinstance(value, theano.compile.SharedVariable):
broadcastable = value.broadcastable
value = self._var_get_value(value, zero=zero, dtype=dtype)
kwargs = {"value": value}
if name: kwargs["name"] = name
if broadcastable: kwargs["broadcastable"] = broadcastable
param = theano.shared(**kwargs)
self.params[param] = {
"value": orig_value, "zero": zero,
"broadcastable": broadcastable, "name": name, "dtype": dtype}
return param
def reset(self):
for param, info in self.params.items():
if info["zero"]: continue
value = info["value"]
if isinstance(value, theano.compile.SharedVariable):
# We copied from this shared var. This should be done here in all cases.
# The first copy might even be invalid because networks params
# are not loaded in the beginning, so this is important.
value = value.get_value()
value = numpy.asarray(value).astype(info["dtype"])
param.set_value(value)
if self.reset_update_params:
# Also reset all remaining params.
for param, info in self.params.items():
value = info["value"]
if info["zero"]:
value = self._var_get_value(value, zero=True, dtype=info["dtype"])
if isinstance(value, theano.compile.SharedVariable):
continue # this is handled above
value = numpy.asarray(value).astype(info["dtype"])
param.set_value(value)
def getUpdateList(self):
assert self.pid == os.getpid()
updates = []
" :type: list[(theano.SharedVariable, theano.Variable)] "
upd = { p: 0 for p in self.net_train_param_deltas.keys() }
grads = {p: T.switch(T.or_(T.isinf(g), T.isnan(g)), numpy.float32(0), g) for (p, g) in self.net_train_param_deltas.items()}
#grads = {p: g for (p, g) in self.net_train_param_deltas.items()}
if self.mean_normalized_sgd:
# https://www-i6.informatik.rwth-aachen.de/publications/download/903/WieslerSimonRichardAlexerSchl%7Bu%7DterRalfNeyHermann--Mean-normalizedstochasticgradientforlarge-scaledeeplearning--2014.pdf
assert self.update_on_device, "not implemented otherwise. we need the activation running average"
for layer_name, layer in sorted(self.network.hidden.items()) + sorted(self.network.output.items()):
if not hasattr(layer, "W_in"): continue
assert len(layer.sources) == len(layer.W_in)
all_in_train = layer.b in self.network.train_params_vars
sparse_input = False
for s, W in zip(layer.sources, layer.W_in):
if W not in self.network.train_params_vars: all_in_train = False
if s.attrs['sparse']: sparse_input = True
if not all_in_train:
print("Mean-normalized SGD: layer", layer_name, "not trained", file=log.v4)
continue
if sparse_input:
print("Mean-normalized SGD: layer", layer_name, "has sparse input, not supported yet", file=log.v4)
continue
print("Mean-normalized SGD: used for W_in of layer", layer_name, file=log.v4)
avg_f = numpy.float32(self.mean_normalized_sgd_average_interpolation)
delta_b = grads[layer.b]
for s, W_in in zip(layer.sources, layer.W_in):
avg_v = self.var(numpy.zeros((s.attrs["n_out"],), dtype="float32"),
name="avg_%s_%s" % (s.name, layer.name))
# Without the opt_contiguous_on_gpu, I get a crash (together with LSTMP)...
cur_avg = T.mean(opt_contiguous_on_gpu(s.output), axis=(0, 1))
avg = avg_f * avg_v + (numpy.float32(1.0) - avg_f) * cur_avg
updates.append((avg_v, avg))
grads[W_in] -= T.outer(avg, delta_b)
grads[layer.b] -= T.dot(grads[W_in].T, avg)
self.counter = self.var(0, name="counter", dtype="int64")
updates.append((self.counter, self.counter + 1))
dt = T.cast(1.,'float32') #T.cast(T.max(T.sum(self.network.output.values()[0].index,axis=0)), 'float32')
i_t = self.i + dt #1.
prev_epoch = self.var(numpy.zeros((), dtype="int32"),'prev_epoch',dtype='int32')
updates.append((prev_epoch, self.network.epoch))
e_t = T.switch(T.eq(self.network.epoch,prev_epoch), self.e + dt,
T.switch(T.eq(i_t, self.e), numpy.float32(1), self.e/numpy.float32(2) + dt))
updates.append((self.e, e_t))
beta1=numpy.float32(0.9)
beta2=numpy.float32(0.999)
beta3=numpy.float32(0.999)
default_output_layer = None
batch_num_output_frames = None
if self.network.output:
if "output" in self.network.output:
default_output_layer = self.network.output["output"]
else:
default_output_layer = sorted(self.network.output.items())[0][1]
batch_num_output_frames = T.sum(default_output_layer.index)
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
srng = RandomStreams(self.rng.randint(1234) + 1)
total_grad_norm = numpy.float32(0)
for grad in grads.values(): total_grad_norm += T.sum(grad * grad)
n_total_params = 0
for grad in grads.values(): n_total_params += T.prod(grad.shape)
avg_grad_norm = total_grad_norm / T.cast(n_total_params, dtype="float32")
for param in grads.keys():
# This loops sets upd[param], where param_new = param + upd[param].
if hasattr(param, 'custom_update'):
# For this case, Device will set grads[param] to custom_update (thus like negative gradient).
# We also don't apply the learning rate here.
if param.custom_update_normalized: # cumulative moving average
upd[param] = (grads[param] - param) / e_t
elif param.custom_update_exp_average: # exponential moving average
alpha = numpy.float32(param.custom_update_exp_average)
upd[param] = alpha * (grads[param] - param) # ((alpha - 1) * old + alpha * new)
else:
upd[param] = grads[param]
if param.custom_update_condition is not None:
upd[param] = T.switch(param.custom_update_condition, upd[param], 0)
if param.custom_update_accumulate_batches is not None:
assert param.custom_update_accumulate_batches >= 1
do_update_now = T.eq(self.counter % param.custom_update_accumulate_batches, param.custom_update_accumulate_batches - 1)
accumulated_param = self.var(param, name="%s_accumulated" % param.name, zero=True)
accumulated_param_new = accumulated_param + upd[param]
updates.append((
accumulated_param,
theano.ifelse.ifelse(
do_update_now,
T.zeros_like(param),
accumulated_param_new
)
))
upd[param] = theano.ifelse.ifelse(
do_update_now,
accumulated_param_new / numpy.float32(param.custom_update_accumulate_batches),
T.zeros_like(param)
)
continue
if param.layer.device != self.device and param.layer.device is not None:
grads[param] = grads[param].transfer(self.device)
gradient_scale = param.layer.gradient_scale
if isinstance(param.layer.gradient_scale, list):
gradient_scale = T.switch(T.eq(T.mod(i_t - 1,gradient_scale[1]),0), gradient_scale[0], numpy.float32(0))
deltas = grads[param] * T.cast(gradient_scale,'float32')
if self.max_norm > 0:
deltas = self.norm_constraint(deltas, self.max_norm)
if self.gradient_l2_norm:
deltas = deltas / (deltas.norm(2) + numpy.float32(1e-10))
if self.gradient_noise > 0.0: # http://arxiv.org/pdf/1511.06807v1.pdf
nu = self.gradient_noise # try 0.01 0.3 1.0
gamma = self.gradient_noise_decay
sigma = nu / (1 + i_t)**gamma
deltas += srng.normal(size=deltas.shape, ndim=deltas.ndim, avg=0.0, std=sigma, dtype="float32")
if self.grad_noise_rel_grad_norm > 0.0:
# Idea extended from here: RandomOut, http://arxiv.org/pdf/1602.05931v2.pdf
# The total gradient norm is a measure how much error there is.
# If the relative gradient norm is low, it means that this element
# has low impact on the loss function. -> Change that, add noise.
elemwise_grad_norm = grads[param] * grads[param]
eps = numpy.float32(1e-7)
rel_elemwise_grad_norm = elemwise_grad_norm - avg_grad_norm
sigma = self.grad_noise_rel_grad_norm
noise = srng.normal(size=deltas.shape, ndim=deltas.ndim, avg=0.0, std=sigma, dtype="float32")
noise *= T.maximum(-rel_elemwise_grad_norm, numpy.float32(1))
deltas += noise
#print param, param.get_value().shape, numpy.prod(param.get_value().shape)
if self.gradient_clip > 0:
# Note that there is also theano.gradient.grad_clip, which would clip it already
# at the backprop step and which would affect also other dependent gradients.
# However, this is simpler for now.
# Also note that this is yet without the learning rate factor -
# this might be different to other gradient clipping implementations.
deltas = T.clip(deltas, -self.gradient_clip, self.gradient_clip)
#if self.momentum > 0:
# upd[p] += self.momentum * self.deltas[param]
if self.variance_reduction:
self.start_var_reduction = 0
self.use_corrected_grad = True
self.decay = 0.9 #75
self.delta_clip = 0 #50.0
self.gamma_clip = 2.5 # 1.8
eps = numpy.float32(1e-7)
deltas = deltas / (deltas.norm(2) + eps)
taus_x_t = self.var((numpy.ones_like(param.get_value()) + eps) * 2.1, name="taus_x_t_" + param.name)
# Variance reduction parameters
# Numerator of the gamma:
gamma_nume_sqr = self.var(numpy.zeros_like(param.get_value()) + eps, name="gamma_nume_sqr_" + param.name)
# Denominator of the gamma:
gamma_deno_sqr = self.var(numpy.zeros_like(param.get_value()) + eps, name="gamma_deno_sqr_" + param.name)
# For the covariance parameter := E[\gamma \alpha]_{t-1}
cov_num_t = self.var(numpy.zeros_like(param.get_value()) + eps, name="cov_num_t_" + param.name)
# mean_grad := E[g]_{t-1}
mean_grad = self.var(numpy.zeros_like(param.get_value()) + eps, name="mean_grad_%s" % param.name)
# mean_squared_grad := E[g^2]_{t-1}
mean_square_grad = self.var(numpy.zeros_like(param.get_value()) + eps, name="msg_" + param.name)
# mean_square_dx := E[(\Delta x)^2]_{t-1}
mean_square_dx = self.var(value=numpy.zeros_like(param.get_value()), name="msd_" + param.name)
old_grad = self.var(value=numpy.zeros_like(param.get_value()) + eps, name="old_grad_" + param.name)
# The uncorrected gradient of previous of the previous update:
old_plain_grad = self.var(numpy.zeros_like(param.get_value()) + eps, name="old_plain_grad_" + param.name)
mean_curvature = self.var(numpy.zeros_like(param.get_value()) + eps, name="mean_curvature_" + param.name)
mean_curvature_sqr = self.var(numpy.zeros_like(param.get_value()) + eps,
name="mean_curvature_sqr_" + param.name)
# Initialize the E[\Delta]_{t-1}
mean_dx = self.var(numpy.zeros_like(param.get_value()), name="mean_dx_" + param.name)
# Block-wise normalize the gradient:
# For the first time-step, assume that delta_x_t := deltas
cond = T.eq(self.i, 0)
msdx = cond * deltas ** 2 + (1 - cond) * mean_square_dx
mdx = cond * deltas + (1 - cond) * mean_dx
"""
Compute the new updated values.
"""
# E[g_i^2]_t
new_mean_squared_grad = mean_square_grad * self.decay + T.sqr(deltas) * (1 - self.decay)
new_mean_squared_grad.name = "msg_" + param.name
# E[g_i]_t
new_mean_grad = mean_grad * self.decay + deltas * (1 - self.decay)
new_mean_grad.name = "nmg_" + param.name
# Keep the rms for numerator and denominator of gamma.
new_gamma_nume_sqr = gamma_nume_sqr * (1 - 1 / taus_x_t) + T.sqr(
(deltas - old_grad) * (old_grad - new_mean_grad)) / taus_x_t
new_gamma_nume_sqr.name = "ngammasqr_num_" + param.name
new_gamma_deno_sqr = gamma_deno_sqr * (1 - 1 / taus_x_t) + T.sqr(
(new_mean_grad - deltas) * (old_grad - new_mean_grad)) / taus_x_t
new_gamma_deno_sqr.name = "ngammasqr_den_" + param.name
gamma = T.sqrt(gamma_nume_sqr) / T.sqrt(gamma_deno_sqr + eps)
gamma.name = "gamma_" + param.name
if self.gamma_clip:
gamma = T.minimum(gamma, self.gamma_clip)
momentum_step = gamma * new_mean_grad
corrected_grad_cand = (deltas + momentum_step) / (1 + gamma)
# For starting the variance reduction.
if self.start_var_reduction > -1:
cond = T.le(self.start_var_reduction, self.i)
corrected_grad = cond * corrected_grad_cand + (1 - cond) * deltas
else:
corrected_grad = deltas
# Use the gradients from the previous update
# to compute the \nabla f(x_t) - \nabla f(x_{t-1})
cur_curvature = deltas - old_plain_grad
new_curvature_ave = mean_curvature * (1 - 1 / taus_x_t) + cur_curvature / taus_x_t
new_curvature_ave.name = "ncurve_ave_" + param.name
# Average average curvature
nc_ave = new_curvature_ave
new_curvature_sqr_ave = mean_curvature_sqr * (1 - 1 / taus_x_t) + T.sqr(cur_curvature) / taus_x_t
new_curvature_sqr_ave.name = "ncurve_sqr_ave_" + param.name
# Unbiased average squared curvature
nc_sq_ave = new_curvature_sqr_ave
slope = self.learning_rate_var
rms_dx_tm1 = T.sqrt(msdx + slope)
rms_curve_t = T.sqrt(new_curvature_sqr_ave + slope)
# This is where the update step is being defined
# delta_x_t = -scaled_lr * (rms_dx_tm1 / rms_curve_t - cov_num_t / (new_curvature_sqr_ave + epsilon))
delta_x_t = -(rms_dx_tm1 / rms_curve_t - cov_num_t / (new_curvature_sqr_ave + self.learning_rate_var))
delta_x_t.name = "delta_x_t_" + param.name
delta_x_t = delta_x_t * corrected_grad
new_taus_t = (1 - T.sqr(mdx) / (msdx + eps)) * taus_x_t + self.var(1 + eps, name="stabilized")
# To compute the E[\Delta^2]_t
new_mean_square_dx = msdx * (1 - 1 / taus_x_t) + T.sqr(delta_x_t) / taus_x_t
# To compute the E[\Delta]_t
new_mean_dx = mean_dx * (1 - 1 / taus_x_t) + delta_x_t / taus_x_t
# Perform the outlier detection:
# This outlier detection is slightly different:
self.upper_bound_tau = 1e8
self.lower_bound_tau = 1.5
new_taus_t = T.switch(
T.or_(abs(deltas - new_mean_grad) > (2 * T.sqrt(new_mean_squared_grad - new_mean_grad ** 2)),
abs(cur_curvature - nc_ave) > (2 * T.sqrt(nc_sq_ave - nc_ave ** 2))),
self.var(2.2), new_taus_t)
# Apply the bound constraints on tau:
new_taus_t = T.maximum(self.lower_bound_tau, new_taus_t)
new_taus_t = T.minimum(self.upper_bound_tau, new_taus_t)
new_cov_num_t = cov_num_t * (1 - 1 / taus_x_t) + (delta_x_t * cur_curvature) * (1 / taus_x_t)
deltas = -delta_x_t
# Apply updates
updates.append((mean_square_grad, new_mean_squared_grad))
updates.append((mean_square_dx, new_mean_square_dx))
updates.append((mean_dx, new_mean_dx))
updates.append((gamma_nume_sqr, new_gamma_nume_sqr))
updates.append((gamma_deno_sqr, new_gamma_deno_sqr))
updates.append((taus_x_t, new_taus_t))
updates.append((cov_num_t, new_cov_num_t))
updates.append((mean_grad, new_mean_grad))
updates.append((old_plain_grad, deltas))
updates.append((mean_curvature, new_curvature_ave))
updates.append((mean_curvature_sqr, new_curvature_sqr_ave))
updates.append((old_grad, corrected_grad))
if self.adasecant:
# https://github.com/caglar/adasecant_wshp_paper/blob/master/adasecant/codes/learning_rule.py
self.use_adam = False
self.use_adagrad = False
self.use_adadelta = False
self.skip_nan_inf = True
self.start_var_reduction = 1
self.use_corrected_grad = True
self.decay = 0.75
self.delta_clip = 50.0
self.gamma_clip = 1.8
eps = numpy.float32(1e-7)
if not self.gradient_l2_norm:
deltas = deltas / (deltas.norm(2) + eps)
if self.use_adagrad:
sum_square_grad = self.var(param.get_value(borrow=True) * 0., name="sum_square_grad_%s" % param.name, broadcastable=param.broadcastable)
if self.use_adadelta:
eg2 = self.var(param.get_value(borrow=True) * 0., name= "eg2_%s" % param.name, broadcastable=param.broadcastable)
edx2 = self.var(param.get_value(borrow=True) * 0., name= "edx2_%s" % param.name, broadcastable=param.broadcastable)
if self.use_adam:
m_prev = self.var(param, zero=True, name="adam_m_%s" % param.name)
v_prev = self.var(param, zero=True, name="adam_v_%s" % param.name)
taus_x_t = self.var((numpy.ones_like(param.get_value()) + eps) * 2.1, name="taus_x_t_" + param.name)
#Variance reduction parameters
#Numerator of the gamma:
gamma_nume_sqr = self.var(numpy.zeros_like(param.get_value()) + eps, name="gamma_nume_sqr_" + param.name)
#Denominator of the gamma:
gamma_deno_sqr = self.var(numpy.zeros_like(param.get_value()) + eps, name="gamma_deno_sqr_" + param.name)
#For the covariance parameter := E[\gamma \alpha]_{t-1}
cov_num_t = self.var(numpy.zeros_like(param.get_value()) + eps, name="cov_num_t_" + param.name)
# mean_grad := E[g]_{t-1}
mean_grad = self.var(numpy.zeros_like(param.get_value()) + eps, name="mean_grad_%s" % param.name)
# mean_squared_grad := E[g^2]_{t-1}
mean_square_grad = self.var(numpy.zeros_like(param.get_value()) + eps, name="msg_" + param.name)
# mean_square_dx := E[(\Delta x)^2]_{t-1}
mean_square_dx = self.var(value = numpy.zeros_like(param.get_value()), name="msd_" + param.name)
old_grad = self.var(value = numpy.zeros_like(param.get_value()) + eps, name="old_grad_" + param.name)
#The uncorrected gradient of previous of the previous update:
old_plain_grad = self.var(numpy.zeros_like(param.get_value()) + eps, name="old_plain_grad_" + param.name)
mean_curvature = self.var(numpy.zeros_like(param.get_value()) + eps, name="mean_curvature_" + param.name)
mean_curvature_sqr = self.var(numpy.zeros_like(param.get_value()) + eps, name="mean_curvature_sqr_" + param.name)
# Initialize the E[\Delta]_{t-1}
mean_dx = self.var(numpy.zeros_like(param.get_value()), name="mean_dx_" + param.name)
# Block-wise normalize the gradient:
#For the first time-step, assume that delta_x_t := deltas
cond = T.eq(self.i, 0)
msdx = cond * deltas**2 + (1 - cond) * mean_square_dx
mdx = cond * deltas + (1 - cond) * mean_dx
"""
Compute the new updated values.
"""
# E[g_i^2]_t
new_mean_squared_grad = mean_square_grad * self.decay + T.sqr(deltas) * (1 - self.decay)
new_mean_squared_grad.name = "msg_" + param.name
# E[g_i]_t
new_mean_grad = mean_grad * self.decay + deltas * (1 - self.decay)
new_mean_grad.name = "nmg_" + param.name
# Keep the rms for numerator and denominator of gamma.
new_gamma_nume_sqr = gamma_nume_sqr * (1 - 1 / taus_x_t) + T.sqr((deltas - old_grad) * (old_grad - new_mean_grad)) / taus_x_t
new_gamma_nume_sqr.name = "ngammasqr_num_" + param.name
new_gamma_deno_sqr = gamma_deno_sqr * (1 - 1 / taus_x_t) + T.sqr((new_mean_grad - deltas) * (old_grad - new_mean_grad)) / taus_x_t
new_gamma_deno_sqr.name = "ngammasqr_den_" + param.name
gamma = T.sqrt(gamma_nume_sqr) / T.sqrt(gamma_deno_sqr + eps)
gamma.name = "gamma_" + param.name
if self.gamma_clip:
gamma = T.minimum(gamma, self.gamma_clip)
momentum_step = gamma * new_mean_grad
corrected_grad_cand = (deltas + momentum_step) / (1 + gamma)
#For starting the variance reduction.
if self.start_var_reduction > -1:
cond = T.le(self.start_var_reduction, self.i)
corrected_grad = cond * corrected_grad_cand + (1 - cond) * deltas
else:
corrected_grad = deltas
if self.use_adagrad:
g = corrected_grad
# Accumulate gradient (windowed version)
new_sum_squared_grad = (
sum_square_grad + T.sqr(g)
)
rms_g_t = T.sqrt(new_sum_squared_grad)
rms_g_t = T.maximum(rms_g_t, 1.0)
if self.use_adadelta:
decay = self.decay #self.adadelta_decay
offset = eps #self.adadelta_offset
g2 = T.sqr(corrected_grad)
eg2_new = decay * eg2 + (1 - decay) * g2
rms_g_t = T.sqrt(eg2_new + offset) / T.sqrt(edx2 + offset) #- 1.0 / dx_new
#rms_g_t = T.maximum(rms_g_t, 1.0)
# Use the gradients from the previous update
# to compute the \nabla f(x_t) - \nabla f(x_{t-1})
cur_curvature = deltas - old_plain_grad
new_curvature_ave = mean_curvature * (1 - 1 / taus_x_t) + cur_curvature / taus_x_t
new_curvature_ave.name = "ncurve_ave_" + param.name
#Average average curvature
nc_ave = new_curvature_ave
new_curvature_sqr_ave = mean_curvature_sqr * (1 - 1 / taus_x_t) + T.sqr(cur_curvature) / taus_x_t
new_curvature_sqr_ave.name = "ncurve_sqr_ave_" + param.name
#Unbiased average squared curvature
nc_sq_ave = new_curvature_sqr_ave
slope = self.learning_rate_var
rms_dx_tm1 = T.sqrt(msdx + slope)
rms_curve_t = T.sqrt(new_curvature_sqr_ave + slope)
#This is where the update step is being defined
#delta_x_t = -scaled_lr * (rms_dx_tm1 / rms_curve_t - cov_num_t / (new_curvature_sqr_ave + epsilon))
delta_x_t = -(rms_dx_tm1 / rms_curve_t - cov_num_t / (new_curvature_sqr_ave + self.learning_rate_var))
delta_x_t.name = "delta_x_t_" + param.name
# This part seems to be necessary for only RNNs
# For feedforward networks this does not seem to be important.
if self.delta_clip:
delta_x_t = delta_x_t.clip(-self.delta_clip, self.delta_clip)
if self.use_adagrad or self.use_adadelta:
delta_x_t = delta_x_t * corrected_grad / rms_g_t
elif self.use_adam:
m_t = beta1 * m_prev + (numpy.float32(1) - beta1) * deltas
v_t = beta2 * v_prev + (numpy.float32(1) - beta2) * deltas ** 2
a_t = T.cast(T.sqrt(1 - beta2 ** i_t) / (1 - beta1 ** i_t), dtype="float32")
delta_x_t = delta_x_t * corrected_grad * a_t
else:
#logger.info("Clipped adagrad is disabled.")
delta_x_t = delta_x_t * corrected_grad
new_taus_t = (1 - T.sqr(mdx) / (msdx + eps)) * taus_x_t + self.var(1 + eps, name="stabilized")
#To compute the E[\Delta^2]_t
new_mean_square_dx = msdx * (1 - 1 / taus_x_t) + T.sqr(delta_x_t) / taus_x_t
#To compute the E[\Delta]_t
new_mean_dx = mean_dx * (1 - 1 / taus_x_t) + delta_x_t / taus_x_t
#Perform the outlier detection:
#This outlier detection is slightly different:
self.upper_bound_tau = 1e8
self.lower_bound_tau = 1.5
new_taus_t = T.switch(T.or_(abs(deltas - new_mean_grad) > (2 * T.sqrt(new_mean_squared_grad - new_mean_grad**2)),
abs(cur_curvature - nc_ave) > (2 * T.sqrt(nc_sq_ave - nc_ave**2))),
self.var(2.2), new_taus_t)
#Apply the bound constraints on tau:
new_taus_t = T.maximum(self.lower_bound_tau, new_taus_t)
new_taus_t = T.minimum(self.upper_bound_tau, new_taus_t)
new_cov_num_t = cov_num_t * (1 - 1 / taus_x_t) + (delta_x_t * cur_curvature) * (1 / taus_x_t)
upd[param] = delta_x_t
# Apply updates
updates.append((mean_square_grad, new_mean_squared_grad))
updates.append((mean_square_dx, new_mean_square_dx))
updates.append((mean_dx, new_mean_dx))
updates.append((gamma_nume_sqr, new_gamma_nume_sqr))
updates.append((gamma_deno_sqr, new_gamma_deno_sqr))
updates.append((taus_x_t, new_taus_t))
updates.append((cov_num_t, new_cov_num_t))
updates.append((mean_grad, new_mean_grad))
updates.append((old_plain_grad, deltas))
updates.append((mean_curvature, new_curvature_ave))
updates.append((mean_curvature_sqr, new_curvature_sqr_ave))
#updates.append((param, param + update_step))
if self.use_adagrad:
updates.append((sum_square_grad, new_sum_squared_grad))
if self.use_adadelta:
edx2_new = self.decay * edx2 + (1 - self.decay) * delta_x_t ** 2
updates.append((eg2, eg2_new))
updates.append((edx2, edx2_new))
#updates.append((dx, dx_new))
if self.use_adam:
updates.append((m_prev, m_t))
updates.append((v_prev, v_t))
if self.use_corrected_grad:
updates.append((old_grad, corrected_grad))
elif self.nadam: # http://cs229.stanford.edu/proj2015/054_report.pdf
m_cache = self.var(1, name="momemtum_cache")
m_prev = self.var(param, zero=True, name="nadam_m_%s" % param.name)
v_prev = self.var(param, zero=True, name="nadam_v_%s" % param.name)
self.adam_offset = numpy.float32(1e-8)
mt = (beta1 * ( 1 - 0.5 * 0.96**( i_t * float(self.nadam_decay) ) )) # momentum schedule, http://www.cs.toronto.edu/~fritz/absps/momentum.pdf
mtnext = beta1 * ( 1 - 0.5 * 0.96**( (i_t + 1) * float(self.nadam_decay) ) ) # for simplified NAG
m_cache_new = m_cache * mt
bias_corr = m_cache_new * mtnext
_deltas = deltas / T.cast(1 - m_cache_new, dtype="float32")
m = beta1 * m_prev + (numpy.float32(1) - beta1) * deltas
_m = m / T.cast(1 - bias_corr, dtype="float32") # bias correction (with momentum schedule (include the next t+1))
v = beta2 * v_prev + (numpy.float32(1) - beta2) * (deltas**2)
_v = v / T.cast(1 - beta2 ** i_t, dtype="float32")
__m = T.cast(1 - mt, dtype="float32") * _deltas + T.cast(mtnext, dtype="float32") * _m
step = -self.learning_rate_var * gradient_scale * __m / ( T.sqrt(_v) + self.adam_offset )
upd[param] += step
updates.append((m_cache, m_cache_new))
updates.append((m_prev, m))
updates.append((v_prev, v))
elif self.eve: # https://arxiv.org/pdf/1611.01505v1.pdf https://github.com/jayanthkoushik/sgd-feedback/blob/master/src/eve.py
loss = self.network.get_objective() # current objective value
loss_prev = self.var(0, name="loss at t-1")
d_prev = self.var(1, name="d_(t-1)")
m_prev = self.var(param, zero=True, name="eve_m_%s" % param.name)
v_prev = self.var(param, zero=True, name="eve_v_%s" % param.name)
thl = numpy.float32(0.1)
thu = numpy.float32(10.)
loss_ch_fact = T.cast(loss / loss_prev, dtype="float32")
ch_fact_lbound = T.switch(T.gt(loss, loss_prev), 1 + thl, 1 / (1 + thu))
ch_fact_ubound = T.switch(T.gt(loss, loss_prev), 1 + thu, 1 / (1 + thl))
loss_ch_fact = T.switch(T.lt(loss_ch_fact, ch_fact_lbound), ch_fact_lbound, loss_ch_fact)
loss_ch_fact = T.switch(T.gt(loss_ch_fact, ch_fact_ubound), ch_fact_ubound, loss_ch_fact)
loss_hat = T.switch(T.gt(i_t, 1), loss_prev * loss_ch_fact, loss)
d_den = T.switch(T.gt(loss_hat, loss_prev), loss_prev, loss_hat)
d_t = T.cast( (beta3 * d_prev) + (1. - beta3) * T.abs_((loss_hat - loss_prev)) / d_den, dtype="float32" )
d_t = T.cast( T.switch(T.gt(i_t, 1), d_t, 1.), dtype="float32" )
updates.append((d_prev, d_t))
m_t = beta1 * m_prev + (numpy.float32(1) - beta1) * deltas
mhat_t = m_t / (1. - beta1**i_t)
updates.append((m_prev, m_t))
v_t = beta2 * v_prev + (numpy.float32(1) - beta2) * deltas ** 2
vhat_t = v_t / (1. - beta2**i_t)
updates.append((v_prev, v_t))
self.adam_offset = numpy.float32(1e-16)
step = self.learning_rate_var * mhat_t / ((T.sqrt(vhat_t) * d_t) + self.adam_offset)
upd[param] += -step
updates.append((loss_prev, loss))
elif self.adam:
#epsilon = numpy.float32(1e-8)
#epsilon = numpy.float32(1.0)
self.adam_offset = numpy.float32(1e-16)
m_prev = self.var(param, zero=True, name="adam_m_%s" % param.name)
v_prev = self.var(param, zero=True, name="adam_v_%s" % param.name)
m_t = beta1 * m_prev + (numpy.float32(1) - beta1) * deltas
v_t = beta2 * v_prev + (numpy.float32(1) - beta2) * deltas ** 2
a_t = self.learning_rate_var
if self.adam_fit_learning_rate:
a_t *= T.cast(T.sqrt(1 - beta2 ** i_t) / (1 - beta1 ** i_t), dtype="float32")
step = a_t * m_t / (T.sqrt(v_t) + self.adam_offset)
updates.append((m_prev, m_t))
updates.append((v_prev, v_t))
#updates.append((param, param - step))
upd[param] += -step
elif self.adamax:
epsilon = numpy.float32(1e-8)
m_prev = self.var(param, zero=True, name="adamax_m_%s" % param.name)
v_prev = self.var(param, zero=True, name="adamax_v_%s" % param.name)
m_t = beta1 * m_prev + (numpy.float32(1) - beta1) * deltas
v_t = T.maximum(beta2 * v_prev, abs(deltas) + epsilon)
step = (self.learning_rate_var / (numpy.float32(1) - beta1 ** i_t)) * (m_t / v_t)
updates.append((m_prev, m_t))
updates.append((v_prev, v_t))
upd[param] += -step
elif self.adagrad:
epsilon = numpy.float32(1e-6)
accu_new = self.accu[param] + deltas ** 2
updates.append((self.accu[param], accu_new))
upd[param] += -self.learning_rate_var * deltas / T.sqrt(accu_new + epsilon)
#updates.append((self.sqrsum[param], self.sqrsum[param] + deltas ** 2))
#upd = upd * 0.1 / (0.1 + (self.sqrsum[param] + deltas ** 2) ** 0.5)
elif self.adamdelta: # adam moment normalization + adadelta learning rate scaling
self.adam_offset = numpy.float32(1e-16)
m_prev = self.var(param, zero=True, name="adam_m_%s" % param.name)
v_prev = self.var(param, zero=True, name="adam_v_%s" % param.name)
m_t = beta1 * m_prev + (numpy.float32(1) - beta1) * deltas
v_t = beta2 * v_prev + (numpy.float32(1) - beta2) * deltas ** 2
g = m_t / (T.sqrt(v_t) + self.adam_offset)
#updates.append((m_prev, m_t))
#updates.append((v_prev, v_t))
decay = self.adadelta_decay
offset = self.adadelta_offset
g = deltas
g2 = g ** 2
eg2_new = decay * self.eg2[param] + (numpy.float32(1) - decay) * g2
dx_new = - g * T.sqrt(self.edx2[param] + offset) / T.sqrt(eg2_new + offset)
edx2_new = decay * self.edx2[param] + (numpy.float32(1) - decay) * dx_new ** 2
upd[param] += self.learning_rate_var * dx_new
updates.append((self.eg2[param], eg2_new))
updates.append((self.edx2[param], edx2_new))
updates.append((self.dx[param], dx_new))
elif self.adadelta:
decay = self.adadelta_decay
offset = self.adadelta_offset
g = deltas
g2 = g ** 2
eg2_new = decay * self.eg2[param] + (numpy.float32(1) - decay) * g2
dx_new = - g * T.sqrt(self.edx2[param] + offset) / T.sqrt(eg2_new + offset)
edx2_new = decay * self.edx2[param] + (numpy.float32(1) - decay) * dx_new ** 2
updates.append((self.eg2[param], eg2_new))
updates.append((self.edx2[param], edx2_new))
updates.append((self.dx[param], dx_new))
upd[param] += self.learning_rate_var * dx_new
elif self.rmsprop:
# https://github.com/fchollet/keras/blob/master/keras/optimizers.py#L156
# https://github.com/Lasagne/Lasagne/blob/master/lasagne/updates.py#L398-L453
accumulator = self.var(param, zero=True, name="accumulator_%s" % param.name)
epsilon = numpy.float32(1e-8)
accumulator_new = numpy.float32(self.rmsprop) * accumulator + (numpy.float32(1) - numpy.float32(self.rmsprop)) * deltas ** numpy.float32(2)
updates.append((accumulator, accumulator_new))
upd[param] += - (self.learning_rate_var * deltas) / (T.sqrt(accumulator_new) + epsilon)
elif self.smorms3:
# http://sifter.org/~simon/journal/20150420.html
# https://www.reddit.com/r/MachineLearning/comments/3edb42/rmsprop_loses_to_smorms3_beware_the_epsilon/?
epsilon = numpy.float32(1e-16)
g = self.var(param, zero=True, name="g")
g2 = self.var(param, zero=True, name="g2")
mem = self.var(param.get_value() * numpy.float32(0) + numpy.float32(1), name="mem")
r = numpy.float32(1) / (mem + numpy.float32(1))
g_ = (numpy.float32(1) - r) * g + r * deltas
g2_ = (numpy.float32(1) - r) * g2 + r * T.sqr(deltas)
gg_ = T.sqr(g_)
denoise = gg_ / (g2_ + epsilon)
mem_ = numpy.float32(1) + mem * (numpy.float32(1) - denoise)
updates.extend([(g, g_), (g2, g2_), (mem, mem_)])
upd[param] += - (T.minimum(self.learning_rate_var, denoise) * deltas) / (T.sqrt(g2_) + epsilon)
else: # SGD
upd[param] += - self.learning_rate_var * deltas
if self.momentum > 0:
updates.append((self.deltas[param], upd[param]))
upd[param] += self.deltas[param] * self.momentum
if self.nesterov_momentum > 0:
#The following code inspired by https://github.com/fidlej/optim/raw/master/dok/nesterov_simple.pdf
velocity = self.var(param, zero=True, name="nesterov_velocity_%s" % param.name)
tmp = self.nesterov_momentum * velocity + upd[param]
updates.append((velocity, tmp))
upd[param] += tmp*self.nesterov_momentum
if self.momentum2 > 0:
velocity = self.var(param, zero=True, name="momentum2_velocity_%s" % param.name)
upd[param] += velocity * self.momentum2
updates.append((velocity, upd[param]))
if self.update_clip > 0:
for p, u in list(upd.items()):
if not u: continue
upd[p] = T.clip(u, -self.update_clip, self.update_clip)
if self.multi_batch_update > 1:
do_update_now = T.eq(self.counter % self.multi_batch_update, self.multi_batch_update - 1)
self.multi_batch_num_output_frames = self.var(0, name="multi_batch_num_output_frames", dtype="int64")
multi_batch_num_output_frames = self.multi_batch_num_output_frames + batch_num_output_frames
updates.append((self.multi_batch_num_output_frames, T.switch(do_update_now, 0, multi_batch_num_output_frames)))
for param in grads.keys():
multi_batch_update_param = self.var(param, name="%s_multi_batch_update" % param.name, zero=True)
new_multi_batch_update_param = multi_batch_update_param + upd[param]
updates.append((
multi_batch_update_param,
theano.ifelse.ifelse(
do_update_now,
T.zeros_like(param),
new_multi_batch_update_param
)
))
upd[param] = theano.ifelse.ifelse(
do_update_now,
new_multi_batch_update_param / numpy.float32(self.multi_batch_update),
T.zeros_like(param)
)
# Simulate multi GPU training. This might help for regularization.
if self.update_multiple_models: