def call(self, inputs):
inputs, weights = inputs
weights = weights / tf.reduce_sum(weights) # Normalize sample weights
weights_expand = tf.expand_dims(weights, axis=1)
mean, variance = tf.nn.weighted_moments(
inputs, [0], weights_expand) # Compute weighed mean and variance
counter = K.update_add(
self.counter, K.ones_like(self.counter)
) # Count number of times the data passes through the model
init = K.sign(
counter - K.ones_like(counter)
) # Indicator is 1 if model is being initalized, 0 otherwise
mean = K.update(
self.mean, init * self.mean +
(1.0 - init) * mean) # Store the mean when the indicator is 1
variance = K.update(
self.variance, init * self.variance + (1.0 - init) *
variance) # Store the variance when the indicator is 1
mean_expand = tf.expand_dims(mean, axis=0)
variance_expand = tf.expand_dims(variance, axis=0)
outputs = (inputs - mean_expand) / tf.sqrt(
variance_expand + self.epsilon) # Normalize the inputs
return outputs
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.learning_rate
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
if self.amsgrad:
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
else:
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
# Learning rate multipliers
if self.multipliers:
multiplier = [
mult for mult in self.multipliers if mult in p.name
]
else:
multiplier = None
if multiplier:
new_lr_t = lr_t * self.multipliers[multiplier[0]]
if self.debug_verbose:
print('Setting {} to learning rate {}'.format(
multiplier[0], new_lr_t))
print(K.get_value(new_lr_t))
else:
new_lr_t = lr_t
if self.debug_verbose:
print('No change in learning rate {}'.format(p.name))
print(K.get_value(new_lr_t))
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - new_lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - new_lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
sync_cond = K.equal((self.iterations + 1) // self.sync_period *
self.sync_period, (self.iterations + 1))
if TF_KERAS:
slow_params = [K.variable(K.get_value(p), name='sp_{}'.format(i))
for i, p in enumerate(params)]
self.updates = self.optimizer.get_updates(loss, params)
slow_updates = []
for p, sp in zip(params, slow_params):
sp_t = sp + self.slow_step * (p - sp)
slow_updates.append(K.update(sp, K.switch(
sync_cond,
sp_t,
sp,
)))
slow_updates.append(K.update_add(p, K.switch(
sync_cond,
sp_t - p,
K.zeros_like(p),
)))
else:
slow_params = {p.name: K.variable(K.get_value(
p), name='sp_{}'.format(i)) for i, p in enumerate(params)}
update_names = ['update', 'update_add', 'update_sub']
original_updates = [getattr(K, name) for name in update_names]
setattr(K, 'update', lambda x, new_x: ('update', x, new_x))
setattr(K, 'update_add', lambda x, new_x: ('update_add', x, new_x))
setattr(K, 'update_sub', lambda x, new_x: ('update_sub', x, new_x))
self.updates = self.optimizer.get_updates(loss, params)
for name, original_update in zip(update_names, original_updates):
setattr(K, name, original_update)
slow_updates = []
for i, update in enumerate(self.updates):
if isinstance(update, tuple):
name, x, new_x, adjusted = update + (update[-1],)
update_func = getattr(K, name)
if name == 'update_add':
adjusted = x + new_x
if name == 'update_sub':
adjusted = x - new_x
if x.name not in slow_params:
self.updates[i] = update_func(x, new_x)
else:
slow_param = slow_params[x.name]
slow_param_t = slow_param + \
self.slow_step * (adjusted - slow_param)
slow_updates.append(K.update(slow_param, K.switch(
sync_cond,
slow_param_t,
slow_param,
)))
self.updates[i] = K.update(x, K.switch(
sync_cond,
slow_param_t,
adjusted,
))
slow_params = list(slow_params.values())
self.updates += slow_updates
self.weights = self.optimizer.weights + slow_params
return self.updates
def _resource_apply_op(self, grad, var, indices=None):
# 更新判据
cond = K.equal(self.iterations % self.grad_accum_steps, 0)
# 获取梯度
ag = self.get_slot(var, 'ag')
old_update = K.update
def new_update(x, new_x):
new_x = K.switch(cond, new_x, x)
return old_update(x, new_x)
K.update = new_update
ag_t = ag / self.grad_accum_steps
op = super(new_optimizer, self)._resource_apply_op(ag_t, var)
K.update = old_update
# 累积梯度
with tf.control_dependencies([op]):
ag_t = K.switch(cond, K.zeros_like(ag), ag)
with tf.control_dependencies([K.update(ag, ag_t)]):
if indices is None:
ag_t = K.update(ag, ag + grad)
else:
ag_t = self._resource_scatter_add(ag, indices, grad)
return ag_t
def _resource_apply(self, grad, var, indices=None):
# 准备变量
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
beta_1_t = self._get_hyper('beta_1', var_dtype)
beta_2_t = self._get_hyper('beta_2', var_dtype)
epsilon_t = K.cast(self.epsilon, var_dtype)
local_step = K.cast(self.iterations + 1, var_dtype)
# 更新公式
if indices is None:
m_t = K.update(m, beta_1_t * m + (1 - beta_1_t) * grad)
v_t = K.update(v, (1 + beta_2_t) * v + beta_2_t * grad**2)
else:
mv_ops = [
K.update(m, beta_1_t * m),
K.update(v, (1 + beta_2_t) * v)
]
with tf.control_dependencies(mv_ops):
m_t = self._resource_scatter_add(
m, indices, (1 - beta_1_t) * grad
)
v_t = self._resource_scatter_add(v, indices, beta_2_t * grad**2)
# 返回算子
with tf.control_dependencies([m_t, v_t]):
v_t = v_t / (K.pow(1.0 + beta_2_t, local_step) - 1.0)
var_t = var - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
return K.update(var, var_t)
def get_updates(self, loss, params):
assert len(params) == len(self.multipliers)
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr *= (1. /
(1. +
self.decay * K.cast(self.iterations, K.dtype(self.decay))))
# momentum
shapes = [K.int_shape(p) for p in params]
moments = [K.zeros(shape) for shape in shapes]
self.weights = [self.iterations] + moments
for p, g, m, mult in zip(params, grads, moments, self.multipliers):
v = self.momentum * m - (lr * mult) * g # velocity
self.updates.append(K.update(m, v))
if self.nesterov:
new_p = p + self.momentum * v - lr * g
else:
new_p = p + v
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
g2 = K.square(g)
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = v - (1. - self.beta_2) * K.sign(v - g2) * g2
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
accumulators = [
K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params
]
self.weights = accumulators
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
for i, (p, g, a) in enumerate(zip(params, grads, accumulators)):
# update accumulator
rho = 0.5 if i == 0 and self.e2efs_layer is not None and not self.lr_momentum else self.rho
i_lr = self.e2efs_lr if i == 0 and self.e2efs_layer is not None and not self.lr_momentum else lr
new_a = rho * a + (1. - rho) * K.square(g)
self.updates.append(K.update(a, new_a))
new_p = p - i_lr * g / (K.sqrt(new_a) + self.epsilon)
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
adam_lr = self.adam_lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
adam_lr = adam_lr * (1. / (1. + self.decay * K.cast(
self.iterations, K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
adam_lr_t = adam_lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
# momentum
shapes = [K.int_shape(p) for p in params]
moments = [K.zeros(shape) for shape in shapes]
if self.amsgrad:
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
else:
vhats = [K.zeros(1) for _ in params]
self.ms = K.zeros(K.int_shape(params[0]), dtype=K.dtype(params[0]))
self.vs = K.zeros(K.int_shape(params[0]), dtype=K.dtype(params[0]))
self.weights = [self.iterations] + moments + vhats + [self.ms
] + [self.vs]
for i, (p, g, m, vhat) in enumerate(zip(params, grads, moments,
vhats)):
v = self.momentum * m - lr * g # velocity
self.updates.append(K.update(m, v))
if self.nesterov:
new_p = p + self.momentum * v - lr * g
else:
new_p = p + v
if i == 0 and self.e2efs_layer is not None:
nnz = K.sum(K.cast(K.greater(p, 0.), K.floatx()))
m_t = (self.beta_1 * self.ms) + (1. - self.beta_1) * g
v_t = (self.beta_2 *
self.vs) + (1. - self.beta_2) * K.square(g)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - adam_lr_t * m_t / (K.sqrt(vhat_t) + K.epsilon())
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - adam_lr_t * m_t / (K.sqrt(v_t) + K.epsilon())
self.updates.append(K.update(self.ms, m_t))
self.updates.append(K.update(self.vs, v_t))
new_p = K.switch(K.less_equal(nnz, self.e2efs_layer.units),
new_p, p_t)
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, params, loss):
grads = self.get_gradients(loss, params)
shapes = [K.get_variable_shape(p) for p in params]
alphas = [
K.variable(K.ones(shape) * self.init_alpha) for shape in shapes
]
old_grads = [K.zeros(shape) for shape in shapes]
self.weights = alphas + old_grads
self.updates = []
for p, grad, old_grad, alpha in zip(params, grads, old_grads, alphas):
grad = K.sign(grad)
new_alpha = K.switch(
K.greater(grad * old_grad, 0),
K.minimum(alpha * self.scale_up, self.max_alpha),
K.switch(K.less(grad * old_grad, 0),
K.maximum(alpha * self.scale_down, self.min_alpha),
alpha))
grad = K.switch(K.less(grad * old_grad, 0), K.zeros_like(grad),
grad)
new_p = p - grad * new_alpha
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
self.updates.append(K.update(alpha, new_alpha))
self.updates.append(K.update(old_grad, grad))
return self.updates
def __call__(self, y_true,
y_pred): # y_true, y_pred shape: (BS, row, col, ch * 2)
data_true, generator_true = y_true[:, :, :, 0:3], y_true[:, :, :, 3:6]
data_pred, generator_pred = y_pred[:, :, :, 0:3], y_pred[:, :, :, 3:6]
loss_data = K.mean(K.abs(data_true - data_pred), axis=[1, 2, 3])
loss_generator = K.mean(K.abs(generator_true - generator_pred),
axis=[1, 2, 3])
ret = loss_data - self.k_var * loss_generator
# for updating values in each epoch, use `updates` mechanism
# DiscriminatorModel collects Loss Function's updates attributes
mean_loss_data = K.mean(loss_data)
mean_loss_gen = K.mean(loss_generator)
# update K
new_k = self.k_var + self.lambda_k * (self.gamma * mean_loss_data -
mean_loss_gen)
new_k = K.clip(new_k, 0, 1)
self.updates.append(K.update(self.k_var, new_k))
# calculate M-Global
m_global = mean_loss_data + K.abs(self.gamma * mean_loss_data -
mean_loss_gen)
self.updates.append(K.update(self.m_global_var, m_global))
# let loss_real_x mean_loss_data
self.updates.append(K.update(self.loss_real_x_var, mean_loss_data))
# let loss_gen_x mean_loss_gen
self.updates.append(K.update(self.loss_gen_x_var, mean_loss_gen))
return ret
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
accumulators = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
self.weights = accumulators
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
K.dtype(self.decay))))
for p, g, a in zip(params, grads, accumulators):
# update accumulator
new_a = self.rho * a + (1. - self.rho) * K.square(g)
self.updates.append(K.update(a, new_a))
new_p = p - lr * g / (K.sqrt(new_a) + self.epsilon)
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
clptrkey = set_pattern_find(p.name,self.clips.keys())
if self.clips_val and clptrkey:
if self.verbose>0:
print("CLpping variable",p.name," to ", self.clips[p.name] )
c = K.eval(self.clips[clptrkey])
new_p = K.clip(new_p, c[0], c[1])
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
# Applies bounds on actual learning rate
step_size = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
final_lr = self.final_lr * lr / self.base_lr
lower_bound = final_lr * (1. - 1. / (self.gamma * t + 1.))
upper_bound = final_lr * (1. + 1. / (self.gamma * t))
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
if self.amsbound:
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
else:
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
# apply weight decay
if self.weight_decay != 0.:
g += self.weight_decay * K.stop_gradient(p)
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
if self.amsbound:
vhat_t = K.maximum(vhat, v_t)
denom = (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
denom = (K.sqrt(v_t) + self.epsilon)
# Compute the bounds
step_size_p = step_size * K.ones_like(denom)
step_size_p_bound = step_size_p / denom
bounded_lr_t = m_t * K.minimum(
K.maximum(step_size_p_bound, lower_bound), upper_bound)
p_t = p - bounded_lr_t
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.learning_rate
if self.initial_decay > 0:
lr *= (1. / (1. + self.decay * K.cast(self.iterations,
K.dtype(self.decay))))
# momentum
shapes = [K.int_shape(p) for p in params]
moments = [K.zeros(shape) for shape in shapes]
self.weights = [self.iterations] + moments
for p, g, m in zip(params, grads, moments):
matched_layer = [x for x in self.lr_multipliers.keys() if x in p.name]
if matched_layer:
new_lr = lr * self.lr_multipliers[matched_layer[0]]
else:
new_lr = lr
v = self.momentum * m - new_lr * g # velocity
self.updates.append(K.update(m, v))
if self.nesterov:
new_p = p + self.momentum * v - new_lr * g
else:
new_p = p + v
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
""" Obtain the optimizer loss updates.
Parameters
----------
loss: list
List of tensors
params: list
List of tensors
Returns
-------
list
List of tensors
"""
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
# Pass off to CPU if requested
if self.cpu_mode:
with K.tf.device("/cpu:0"):
ms, vs, vhats = self._update_1(params)
else:
ms, vs, vhats = self._update_1(params)
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def update_func():
update_params = [
K.update(q, p * self.slow_ratio + q * self.fast_ratio)
for p, q in zip(self.slow_params, params)
]
with tf.control_dependencies(update_params):
reset_slow_params = [
K.update(p, q) for p, q in zip(self.slow_params, params)
]
return tf.group(*(reset_slow_params + update_iter))
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
'''Bias corrections according to the Adam paper
'''
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
self.weights = [self.iterations] + ms + vs
for p, g, m, v in zip(params, grads, ms, vs):
####################################################
# Add a lr multiplier for vars outside excluded_vars
if p.name in self.excluded_vars:
multiplied_lr_t = lr_t
else:
multiplied_lr_t = lr_t * self.lr_mult
###################################################
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
'''Schedule multiplier eta_t = 1 for simple AdamW
According to the AdamW paper, eta_t can be fixed, decay, or
also be used for warm restarts (AdamWR to come).
'''
eta_t = 1.
p_t = p - eta_t * (multiplied_lr_t * m_t /
(K.sqrt(v_t) + self.epsilon))
if self.weight_decay != 0:
'''Normalized weight decay according to the AdamW paper
'''
w_d = self.weight_decay * K.sqrt(
self.batch_size / (self.samples_per_epoch * self.epochs))
p_t = p_t - eta_t * (w_d * p)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def add_weightnorm_param_updates(updates, new_V_param, new_g_param, W,
V_scaler):
ps = K.get_variable_shape(new_V_param)
norm_axes = [i for i in range(len(ps) - 1)]
# update W and V_scaler
new_V_norm = tf.sqrt(tf.reduce_sum(tf.square(new_V_param), norm_axes))
new_V_scaler = new_g_param / new_V_norm
new_W = tf.reshape(new_V_scaler, [1] * len(norm_axes) + [-1]) * new_V_param
updates.append(K.update(W, new_W))
updates.append(K.update(V_scaler, new_V_scaler))
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
self.weights = [self.iterations]
lr = self.learning_rate
for i, (p, g) in enumerate(zip(params, grads)):
g2 = K.square(g) + self.epsilon1
shape, dtype = K.int_shape(p), K.dtype(p)
factored_shape = self.factored_shape(shape)
if factored_shape is None:
# 定义参数
v = K.zeros(shape, dtype=dtype, name='v_' + str(i))
self.weights.append(v)
# 定义更新
v_t = self.beta2 * v + (1.0 - self.beta2) * g2
self.updates.append(K.update(v, v_t))
else:
# 定义参数
shape1, axis1, shape2, axis2 = factored_shape
vr = K.zeros(shape1, dtype=dtype, name='vr_' + str(i))
vc = K.zeros(shape2, dtype=dtype, name='vc_' + str(i))
self.weights.extend([vr, vc])
# 定义更新
g2r = K.mean(g2, axis=axis1, keepdims=True)
g2c = K.mean(g2, axis=axis2, keepdims=True)
vr_t = self.beta2 * vr + (1.0 - self.beta2) * g2r
vc_t = self.beta2 * vc + (1.0 - self.beta2) * g2c
self.updates.extend([K.update(vr, vr_t), K.update(vc, vc_t)])
# 合成矩阵
v_t = vr_t * vc_t / K.mean(vr_t, axis=axis2, keepdims=True)
# 增量主体
u = g / K.sqrt(v_t)
# 增量裁剪
if self.clipping_threshold is not None:
u_rms = self.rms(u)
d = self.clipping_threshold
u = u / K.maximum(1.0, u_rms / d)
# 增量滑动
if self.beta1 > 0.0:
# 定义参数
m = K.zeros(shape, dtype=dtype, name='m_' + str(i))
self.weights.append(m)
# 定义更新
m_t = self.beta1 * m + (1.0 - self.beta1) * u
self.updates.append(K.update(m, m_t))
u = m_t
# 增量调整
if self.multiply_by_parameter_scale:
u = u * K.maximum(self.rms(p), self.epsilon2)
# 更新参数
self.updates.append(K.update(p, p - lr * u))
return self.updates
def get_updates(self, loss, params):
# Mostly the same code as Adam class, with added multiplier variables.
# Keras code from:
# https://github.com/tensorflow/tensorflow/blob/r1.12/tensorflow/python/keras/optimizers.py#L456
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (
1.0 / (1.0 + self.decay * K.cast(self.iterations, K.dtype(self.decay)))
)
t = K.cast(self.iterations, K.floatx()) + 1
lr_t = lr * (
K.sqrt(1.0 - K.pow(self.beta_2, t)) / (1.0 - K.pow(self.beta_1, t))
)
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
if self.amsgrad:
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
else:
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
layername = p.name.split("/", 1)[0]
mult = self.multipliers.get(layername, 1.0)
m_t = (self.beta_1 * m) + (1.0 - self.beta_1) * g
v_t = (self.beta_2 * v) + (1.0 - self.beta_2) * K.square(g)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - mult * lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - mult * lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, "constraint", None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def south_good(loss, pred, lossS, tape, velocities):
gradients = tape.gradient(lossS, trainable_vars)
grad_mag_sq = tf.reduce_sum(
[tf.reduce_sum(K.square(g)) for g in gradients])
norm = 1.0 / grad_mag_sq
vprimes = []
for (v, g, accum) in zip(velocities, gradients,
self._gradient_ms_accums):
vflat, gflat = K.flatten(v), -K.flatten(g)
vprime = K.reshape(2 * K.sum(vflat * gflat) * norm * g,
v.shape) - v
vprimes.append(vprime)
new_accum = (self.galilean_gradient_momentum * accum +
(1 - self.galilean_gradient_momentum) *
tf.reduce_mean(K.square(g)))
K.update(accum, new_accum)
# x0 - v (S) -> x0 + vprime (E)
for (w, v, vprime) in zip(trainable_vars, velocities, vprimes):
K.update_add(w, v + vprime)
predE = self(x, training=True)
lossE = K.mean(loss_fun(y, predE))
# x0 + vprime (E) -> x0 - vprime (W)
for (w, v, vprime) in zip(trainable_vars, velocities, vprimes):
K.update_add(w, -2 * vprime)
predW = self(x, training=True)
lossW = K.mean(loss_fun(y, predW))
go_east = tf.math.logical_and(lossE <= loss0, lossW > loss0)
go_west = tf.math.logical_and(lossW <= loss0, lossE > loss0)
# x0 - vprime (W) -> x0
for (w, vprime) in zip(trainable_vars, vprimes):
K.update_add(w, vprime)
east_good_f = functools.partial(east_good, vprimes)
west_good_f = functools.partial(west_good, vprimes)
fallback_case_f = functools.partial(fallback_case, velocities)
new_v, gradient_calcs = tf.cond(
go_east, east_good_f,
lambda: tf.cond(go_west, west_good_f, fallback_case_f))
return loss, pred, new_v, gradient_calcs
def get_updates(self, params, loss):
grads = self.get_gradients(loss, params)
shapes = [K.shape(p) for p in params]
alphas = [
K.variable(K.ones(shape) * self.init_alpha) for shape in shapes
]
old_grads = [K.zeros(shape) for shape in shapes]
prev_weight_deltas = [K.zeros(shape) for shape in shapes]
self.weights = alphas + old_grads
self.updates = []
for param, grad, old_grad, prev_weight_delta, alpha in zip(
params, grads, old_grads, prev_weight_deltas, alphas):
# equation 4
new_alpha = K.switch(
K.greater(grad * old_grad, 0),
K.minimum(alpha * self.scale_up, self.max_alpha),
K.switch(K.less(grad * old_grad, 0),
K.maximum(alpha * self.scale_down, self.min_alpha),
alpha))
# equation 5
new_delta = K.switch(
K.greater(grad, 0), -new_alpha,
K.switch(K.less(grad, 0), new_alpha, K.zeros_like(new_alpha)))
# equation 7
weight_delta = K.switch(K.less(grad * old_grad, 0),
-prev_weight_delta, new_delta)
# equation 6
new_param = param + weight_delta
# reset gradient_{t-1} to 0 if gradient sign changed (so that we do
# not "double punish", see paragraph after equation 7)
grad = K.switch(K.less(grad * old_grad, 0), K.zeros_like(grad),
grad)
# Apply constraints
#if param in constraints:
# c = constraints[param]
# new_param = c(new_param)
self.updates.append(K.update(param, new_param))
self.updates.append(K.update(alpha, new_alpha))
self.updates.append(K.update(old_grad, grad))
self.updates.append(K.update(prev_weight_delta, weight_delta))
return self.updates
def update_function():
with tensorflow.control_dependencies(orig_get_updates(
loss, params)):
reset_grads = [
K.update(p, K.zeros(K.int_shape(p), dtype=K.dtype(p)))
for p in self.accumulate_gradient_accumulators
]
if ema_decay > 0:
reset_grads += [K.update_add(self.total_iterations, 1)]
reset_grads += [
K.update(e_p, (e_p * ema_decay) + (1 - ema_decay) * p)
for e_p, p in zip(self.params_ema, params)
]
return tensorflow.group(*(reset_grads +
[updates_accumulated_iterations]))
def get_updates(self, params, loss):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.inital_decay > 0:
lr *= (1. / (1. + self.decay * self.iterations))
t = self.iterations + 1
lr_t = lr * K.sqrt(1. - K.pow(self.beta_2, t)) / (
1. - K.pow(self.beta_1, t))
shapes = [K.get_variable_shape(p) for p in params]
ms = [K.zeros(shape) for shape in shapes]
vs = [K.zeros(shape) for shape in shapes]
f = K.variable(0)
d = K.variable(1)
self.weights = [self.iterations] + ms + vs + [f, d]
cond = K.greater(t, K.variable(1))
small_delta_t = K.switch(K.greater(loss, f), self.small_k + 1,
1. / (self.big_K + 1))
big_delta_t = K.switch(K.greater(loss, f), self.big_K + 1,
1. / (self.small_k + 1))
c_t = K.minimum(K.maximum(small_delta_t, loss / (f + self.epsilon)),
big_delta_t)
f_t = c_t * f
r_t = K.abs(f_t - f) / (K.minimum(f_t, f))
d_t = self.beta_3 * d + (1 - self.beta_3) * r_t
f_t = K.switch(cond, f_t, loss)
d_t = K.switch(cond, d_t, K.variable(1.))
self.updates.append(K.update(f, f_t))
self.updates.append(K.update(d, d_t))
for p, g, m, v in zip(params, grads, ms, vs):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
p_t = p - lr_t * m_t / (d_t * K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
if self.amsgrad:
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
else:
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
clptrkey = set_pattern_find(p.name,self.clips.keys())
if self.clips_val and clptrkey:
c = K.eval(self.clips[clptrkey])
if self.verbose>0:
print("Clipping variable",p.name," to ", c )
new_p = K.clip(new_p, c[0], c[1])
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
self.weights = [self.iterations] + ms + vs
for p, g, m, v in zip(params, grads, ms, vs):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
m_t_hat = m_t / (1. - K.pow(self.beta_1, t))
v_t_hat = v_t / (1. - K.pow(self.beta_2, t))
p_dash = m_t_hat / (K.sqrt(v_t_hat + self.epsilon))
if self.weight_decay > 0.:
wd = self.weight_decay * p
p_dash = p_dash + wd
r1 = K.sqrt(K.sum(K.square(p)))
r2 = K.sqrt(K.sum(K.square(p_dash)))
r = tf.where(tf.greater(r1, 0.),
tf.where(tf.greater(r2, 0.), r1 / r2, 1.0), 1.0)
# r = r1 / r2
eta = r * lr
p_t = p - eta * p_dash
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def update_function():
with tf.control_dependencies(orig_get_updates(loss, params)):
reset_grads = [
K.update(p, K.zeros(K.int_shape(p), dtype=K.dtype(p)))
for p in self.accumulated_grads
]
return tf.group(*(reset_grads + [iters]))
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [(self.iterations, self.iterations + 1)]
t = self.iterations + 1
lr_t = self.lr * K.sqrt(1. - K.pow(self.beta_2, t)) / (
1. - K.pow(self.beta_1, t))
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
gs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
self.weights = ms + vs
flag = K.equal(t % self.accum_iters, 0)
flag = K.cast(flag, dtype='float32')
for p, g, m, v, gg in zip(params, grads, ms, vs, gs):
gg_t = (1 - flag) * (gg + g)
m_t = (self.beta_1 *
m) + (1. - self.beta_1) * (gg + flag * g) / self.accum_iters
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(
(gg + flag * g) / self.accum_iters)
p_t = p - flag * lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append((m, flag * m_t + (1 - flag) * m))
self.updates.append((v, flag * v_t + (1 - flag) * v))
self.updates.append((gg, gg_t))
# apply constraints.
new_p = p_t
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def call(self, inputs, **kwargs):
inputs, memory_length = inputs
memory_length = K.cast(memory_length[0][0], 'int32')
batch_size = K.cast(K.shape(inputs)[0], 'int32')
seq_len = K.cast(K.shape(inputs)[1], 'int32')
# Build new memory
pad = K.tile(inputs[0:1, ...], (self.batch_size - batch_size, 1, 1))
padded = K.concatenate([inputs, pad], axis=0) # (self.batch_size, seq_len, output_dim)
new_memory = K.concatenate([self.memory, padded], axis=1) # (self.batch_size, self.memory_len + seq_len, ...)
new_memory = tf.slice( # (self.batch_size, self.memory_len, output_dim)
new_memory,
(0, seq_len, 0),
(self.batch_size, self.memory_len + self.target_len, self.output_dim),
)
self.add_update(K.update(self.memory, new_memory), inputs)
# Build output
old_memory = tf.slice( # (batch_size, memory_length, output_dim)
new_memory,
(0, K.maximum(0, self.memory_len + self.target_len - seq_len - memory_length), 0),
(batch_size, K.minimum(self.memory_len, memory_length), self.output_dim),
)
return old_memory
def _assign(self, x, y):
if self._tf1:
result = K.update(x, y)
else:
result = state_ops.assign(x, y, use_locking=self._use_locking)
self._updates.append(result)
return result