def get_updates(self, params, loss):
grads = self.get_gradients(loss, params)
c=0
prev_grads = [shared_zeros(p.shape,name=f"prev_grads") for p in params]
c=0
prev_steps = [sharedX(np.full(p.shape, self.step_init),name=f"prev_stepd") for p in params]
self.updates = []
for p, grad, prev_grad, prev_step, c in zip(params, grads, prev_grads,
prev_steps):
grad_sgn = prev_grad * grad
new_step = K.switch(K.ge(grad_sgn, 0.0),
K.minimum(prev_step * self.step_inc, self.step_max),
K.maximum(prev_step * self.step_dec, self.step_min))
self.updates.append((prev_step, new_step))
new_grad = K.switch(K.ge(grad_sgn, 0.0), grad, 0.0)
self.updates.append((prev_grad, new_grad))
new_p = p - K.sgn(new_grad) * new_step
self.updates.append((p, c(new_p)))
return self.updates
def clip_norm(g, c, n):
''' Clip gradients '''
if c > 0:
g = K.switch(K.ge(n, c), g * c / n, g)
return g
def clip_norm(g, c, n):
"""Clip gradients
"""
if c > 0:
g = K.switch(K.ge(n, c), g * c / n, g)
return g
def clip_norm(g, c, n):
''' Clip gradients '''
if c > 0:
g = K.switch(K.ge(n, c), g * c / n, g)
return g