Python keras.backend 模块，batch_get_value() 实例源码
我们从Python开源项目中，提取了以下3个代码示例，用于说明如何使用keras.backend.batch_get_value()。
def save_optimizer_weights(model, path):
with h5py.File(path, 'w') as f:
symbolic_weights = getattr(model.optimizer, 'weights')
if symbolic_weights:
optimizer_weights_group = f.create_group('optimizer_weights')
weight_values = K.batch_get_value(symbolic_weights)
weight_names = []
for i, (w, val) in enumerate(zip(symbolic_weights,
weight_values)):
# Default values of symbolic_weights is /variable
# for theano and cntk
if K.backend() == 'theano' or K.backend() == 'cntk':
if hasattr(w, 'name'):
if w.name.split('/')[-1] == 'variable':
name = str(w.name) + '_' + str(i)
else:
name = str(w.name)
else:
name = 'param_' + str(i)
else:
if hasattr(w, 'name') and w.name:
name = str(w.name)
else:
name = 'param_' + str(i)
weight_names.append(name.encode('utf8'))
optimizer_weights_group.attrs['weight_names'] = weight_names
for name, val in zip(weight_names, weight_values):
param_dset = optimizer_weights_group.create_dataset(
name,
val.shape,
dtype=val.dtype)
if not val.shape:
# scalar
param_dset[()] = val
else:
param_dset[:] = val
f.flush()
## Hacked from Keras core code
def get_model_params(model):
weight_values = []
for lay in model.layers:
weight_values.extend( backend.batch_get_value(lay.weights))
return weight_values
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
t = (self.iterations + 1.)/self.accum_iters
accum_switch = K.cast(K.equal((self.iterations + 1.) % self.accum_iters, 0), dtype=K.floatx())
# Due to the recommendations in [2],i.e. warming momentum schedule
momentum_cache_t = self.beta_1 * (1. - 0.5 * (K.pow(0.96, t * self.schedule_decay)))
momentum_cache_t_1 = self.beta_1 * (1. - 0.5 * (K.pow(0.96, (t + 1) * self.schedule_decay)))
m_schedule_new = self.m_schedule * momentum_cache_t
m_schedule_next = self.m_schedule * momentum_cache_t * momentum_cache_t_1
self.updates.append((self.m_schedule, accum_switch*m_schedule_new + (1. - accum_switch)*self.m_schedule))
shapes = [x.shape for x in K.batch_get_value(params)]
ms = [K.zeros(shape) for shape in shapes]
vs = [K.zeros(shape) for shape in shapes]
gs = [K.zeros(shape) for shape in shapes]
self.weights = [self.iterations] + ms + vs
for p, gp, m, v, ga in zip(params, grads, ms, vs, gs):
g = (ga + gp)/self.accum_iters
# the following equations given in [1]
g_prime = g / (1. - m_schedule_new)
m_t = self.beta_1 * m + (1. - self.beta_1) * g
m_t_prime = m_t / (1. - m_schedule_next)
v_t = self.beta_2 * v + (1. - self.beta_2) * K.square(g)
v_t_prime = v_t / (1. - K.pow(self.beta_2, t))
m_t_bar = (1. - momentum_cache_t) * g_prime + momentum_cache_t_1 * m_t_prime
self.updates.append(K.update(m, (1. - accum_switch)*m + accum_switch*m_t))
self.updates.append(K.update(v, (1. - accum_switch)*v + accum_switch*v_t))
self.updates.append(K.update(ga, (1. - accum_switch)*(ga + gp)))
p_t = p - self.lr * m_t_bar / (K.sqrt(v_t_prime) + self.epsilon)
new_p = p_t
# apply constraints
if p in constraints:
c = constraints[p]
new_p = c(new_p)
self.updates.append(K.update(p, (1-accum_switch)*p + accum_switch*new_p))
return self.updates
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