def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
accumulators = [
K.variable(np.zeros(K.get_value(p).shape)) for p in params
]
delta_accumulators = [
K.variable(np.zeros(K.get_value(p).shape)) for p in params
]
self.updates = []
for p, g, a, d_a, c in zip(params, grads, accumulators,
delta_accumulators, constraints):
# update accumulator
new_a = self.rho * a + (1 - self.rho) * K.square(g)
self.updates.append((a, new_a))
# use the new accumulator and the *old* delta_accumulator
update = g * K.sqrt(d_a + self.epsilon) / K.sqrt(new_a +
self.epsilon)
new_p = p - self.lr * update
self.updates.append((p, c(new_p))) # apply constraints
# update delta_accumulator
new_d_a = self.rho * d_a + (1 - self.rho) * K.square(update)
self.updates.append((d_a, new_d_a))
return self.updates
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-8,
*args, **kwargs):
super(Adamax, self).__init__(**kwargs)
self.__dict__.update(locals())
self.iterations = K.variable(0)
self.lr = K.variable(lr)
self.beta_1 = K.variable(beta_1)
self.beta_2 = K.variable(beta_2)
def __init__(self, lr=0.01, momentum=0., decay=0., nesterov=False,
*args, **kwargs):
super(SGD, self).__init__(**kwargs)
self.__dict__.update(locals())
self.iterations = K.variable(0.)
self.lr = K.variable(lr)
self.momentum = K.variable(momentum)
self.decay = K.variable(decay)
def __init__(self,
lr=0.01,
momentum=0.,
decay=0.,
nesterov=False,
*args,
**kwargs):
super(SGD, self).__init__(**kwargs)
self.__dict__.update(locals())
self.iterations = K.variable(0.)
self.lr = K.variable(lr)
self.momentum = K.variable(momentum)
self.decay = K.variable(decay)
def __init__(self,
lr=0.002,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-8,
*args,
**kwargs):
super(Adamax, self).__init__(**kwargs)
self.__dict__.update(locals())
self.iterations = K.variable(0)
self.lr = K.variable(lr)
self.beta_1 = K.variable(beta_1)
self.beta_2 = K.variable(beta_2)
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
accumulators = [K.variable(np.zeros(K.get_value(p).shape)) for p in params]
self.updates = []
for p, g, a, c in zip(params, grads, accumulators, constraints):
new_a = a + K.square(g) # update accumulator
self.updates.append((a, new_a))
new_p = p - self.lr * g / K.sqrt(new_a + self.epsilon)
self.updates.append((p, c(new_p))) # apply constraints
return self.updates
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
self.updates = [(self.iterations, self.iterations+1.)]
t = self.iterations + 1
lr_t = self.lr / (1 - K.pow(self.beta_1, t))
for p, g, c in zip(params, grads, constraints):
# zero init of 1st moment
m = K.variable(np.zeros(K.get_value(p).shape))
# zero init of exponentially weighted infinity norm
u = K.variable(np.zeros(K.get_value(p).shape))
m_t = (self.beta_1 * m) + (1 - self.beta_1) * g
u_t = K.maximum(self.beta_2 * u, K.abs(g))
p_t = p - lr_t * m_t / (u_t + self.epsilon)
self.updates.append((m, m_t))
self.updates.append((u, u_t))
self.updates.append((p, c(p_t))) # apply constraints
return self.updates
def get_updates(self, params, constraints, loss):
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))
for p, g, c in zip(params, grads, constraints):
# zero init of moment
m = K.variable(np.zeros(K.get_value(p).shape))
# zero init of velocity
v = K.variable(np.zeros(K.get_value(p).shape))
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 / (K.sqrt(v_t) + self.epsilon)
self.updates.append((m, m_t))
self.updates.append((v, v_t))
self.updates.append((p, c(p_t))) # apply constraints
return self.updates
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
self.updates = [(self.iterations, self.iterations + 1.)]
t = self.iterations + 1
lr_t = self.lr / (1 - K.pow(self.beta_1, t))
for p, g, c in zip(params, grads, constraints):
# zero init of 1st moment
m = K.variable(np.zeros(K.get_value(p).shape))
# zero init of exponentially weighted infinity norm
u = K.variable(np.zeros(K.get_value(p).shape))
m_t = (self.beta_1 * m) + (1 - self.beta_1) * g
u_t = K.maximum(self.beta_2 * u, K.abs(g))
p_t = p - lr_t * m_t / (u_t + self.epsilon)
self.updates.append((m, m_t))
self.updates.append((u, u_t))
self.updates.append((p, c(p_t))) # apply constraints
return self.updates
def get_updates(self, params, constraints, loss):
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))
for p, g, c in zip(params, grads, constraints):
# zero init of moment
m = K.variable(np.zeros(K.get_value(p).shape))
# zero init of velocity
v = K.variable(np.zeros(K.get_value(p).shape))
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 / (K.sqrt(v_t) + self.epsilon)
self.updates.append((m, m_t))
self.updates.append((v, v_t))
self.updates.append((p, c(p_t))) # apply constraints
return self.updates
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
accumulators = [K.variable(np.zeros(K.get_value(p).shape)) for p in params]
delta_accumulators = [K.variable(np.zeros(K.get_value(p).shape)) for p in params]
self.updates = []
for p, g, a, d_a, c in zip(params, grads, accumulators,
delta_accumulators, constraints):
# update accumulator
new_a = self.rho * a + (1 - self.rho) * K.square(g)
self.updates.append((a, new_a))
# use the new accumulator and the *old* delta_accumulator
update = g * K.sqrt(d_a + self.epsilon) / K.sqrt(new_a + self.epsilon)
new_p = p - self.lr * update
self.updates.append((p, c(new_p))) # apply constraints
# update delta_accumulator
new_d_a = self.rho * d_a + (1 - self.rho) * K.square(update)
self.updates.append((d_a, new_d_a))
return self.updates
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
accumulators = [
K.variable(np.zeros(K.get_value(p).shape)) for p in params
]
self.updates = []
for p, g, a, c in zip(params, grads, accumulators, constraints):
new_a = a + K.square(g) # update accumulator
self.updates.append((a, new_a))
new_p = p - self.lr * g / K.sqrt(new_a + self.epsilon)
self.updates.append((p, c(new_p))) # apply constraints
return self.updates
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
lr = self.lr * (1.0 / (1.0 + self.decay * self.iterations))
self.updates = [(self.iterations, self.iterations + 1.)]
for p, g, c in zip(params, grads, constraints):
m = K.variable(np.zeros(K.get_value(p).shape)) # momentum
v = self.momentum * m - lr * g # velocity
self.updates.append((m, v))
if self.nesterov:
new_p = p + self.momentum * v - lr * g
else:
new_p = p + v
self.updates.append((p, c(new_p))) # apply constraints
return self.updates
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
lr = self.lr * (1.0 / (1.0 + self.decay * self.iterations))
self.updates = [(self.iterations, self.iterations + 1.)]
for p, g, c in zip(params, grads, constraints):
m = K.variable(np.zeros(K.get_value(p).shape)) # momentum
v = self.momentum * m - lr * g # velocity
self.updates.append((m, v))
if self.nesterov:
new_p = p + self.momentum * v - lr * g
else:
new_p = p + v
self.updates.append((p, c(new_p))) # apply constraints
return self.updates
def __init__(self, lr=1.0, rho=0.95, epsilon=1e-6, *args, **kwargs):
super(Adadelta, self).__init__(**kwargs)
self.__dict__.update(locals())
self.lr = K.variable(lr)
def __init__(self, lr=0.01, epsilon=1e-6, *args, **kwargs):
super(Adagrad, self).__init__(**kwargs)
self.__dict__.update(locals())
self.lr = K.variable(lr)
def __init__(self, lr=0.01, epsilon=1e-6, *args, **kwargs):
super(Adagrad, self).__init__(**kwargs)
self.__dict__.update(locals())
self.lr = K.variable(lr)
def __init__(self, lr=1.0, rho=0.95, epsilon=1e-6, *args, **kwargs):
super(Adadelta, self).__init__(**kwargs)
self.__dict__.update(locals())
self.lr = K.variable(lr)
def __init__(self, lr=0.001, rho=0.9, epsilon=1e-6, *args, **kwargs):
super(RMSprop, self).__init__(**kwargs)
self.__dict__.update(locals())
self.lr = K.variable(lr)
self.rho = K.variable(rho)
def __init__(self, lr=0.001, rho=0.9, epsilon=1e-6, *args, **kwargs):
super(RMSprop, self).__init__(**kwargs)
self.__dict__.update(locals())
self.lr = K.variable(lr)
self.rho = K.variable(rho)
