本文整理汇总了Python中theano.compat.python2x.OrderedDict.append方法的典型用法代码示例。如果您正苦于以下问题：Python OrderedDict.append方法的具体用法？Python OrderedDict.append怎么用？Python OrderedDict.append使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类theano.compat.python2x.OrderedDict的用法示例。
在下文中一共展示了OrderedDict.append方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _build_model
# 需要导入模块: from theano.compat.python2x import OrderedDict [as 别名]
# 或者: from theano.compat.python2x.OrderedDict import append [as 别名]
#.........这里部分代码省略.........
updates = OrderedDict()
zipped = zip(params, gradients, model_updates)
for ndx, (param, gradient, accumulated_gradient) in enumerate(zipped):
agrad = rho * accumulated_gradient + (1 - rho) * gradient ** 2
effective_learning_rate = (learning_rate / (Tensor.sqrt(agrad) + eps))
update, delta = Trainer._calc_update(effective_learning_rate, gradient, param, last_layer=last_layer)
# delta = effective_learning_rate * gradient
updates[param] = update
updates[accumulated_gradient] = agrad
elif strategy == 'RMSProp2':
updates = OrderedDict()
zipped = zip(params, gradients, model_updates)
for ndx, (param, gradient, accumulated_gradient) in enumerate(zipped):
agrad = rho * accumulated_gradient + gradient ** 2
effective_learning_rate = (learning_rate / (Tensor.sqrt(agrad) + eps))
update, delta = Trainer._calc_update(effective_learning_rate, gradient, param, last_layer=last_layer)
# delta = effective_learning_rate * gradient
updates[param] = update
updates[accumulated_gradient] = agrad
elif strategy == 'adaDelta':
updates = OrderedDict()
zipped = zip(params, gradients, model_updates, model_deltas)
for ndx, (param, gradient, accumulated_gradient, accumulated_delta) in enumerate(zipped):
agrad = rho * accumulated_gradient + (1 - rho) * gradient ** 2
# delta = Tensor.sqrt((accumulated_delta + eps) / (agrad + eps)) * gradient
step_size = Tensor.sqrt((accumulated_delta + eps) / (agrad + eps))
update, delta = Trainer._calc_update(step_size, gradient, param, last_layer=last_layer)
updates[param] = update
updates[accumulated_gradient] = agrad
updates[accumulated_delta] = rho * accumulated_delta + (1 - rho) * (delta ** 2)
elif strategy == 'adam':
updates = OrderedDict()
zipped = zip(params, gradients, model_updates, model_deltas)
for ndx, (param, gradient, accumulated_gradient, accumulated_delta) in enumerate(zipped):
moment_1 = bias_1 * accumulated_gradient + (1 - bias_1) * gradient
moment_2 = bias_2 * accumulated_delta + (1 - bias_2) * gradient ** 2
corrected_moment_1 = moment_1 / Tensor.cast((1 - bias_1 ** t), theano.config.floatX)
corrected_moment_2 = moment_2 / Tensor.cast((1 - bias_2 ** t), theano.config.floatX)
g = corrected_moment_1 / (Tensor.sqrt(corrected_moment_2 + eps))
update, delta = Trainer._calc_update(learning_rate, g, param, last_layer=last_layer)
updates[param] = update
updates[accumulated_gradient] = moment_1
updates[accumulated_delta] = moment_2
elif strategy == 'SGDCayley':
updates = []
for param, gradient in zip(params, gradients):
if param.name == 'Wx_layer0' or param.name == 'Wy_layer0':
param_update = Trainer._calc_update(learning_rate, gradient, param, 'Cayley',
last_layer=last_layer)
else:
param_update = Trainer._calc_update(learning_rate, gradient, param, 'Regular',
last_layer=last_layer)
updates.append((param, param_update))
else:
msg = 'Unknown optimization strategy'
OutputLog().write(msg)
raise Exception(msg)
print 'Building function'
variance_hidden_x = Tensor.var(x_hidden, axis=0)
variance_hidden_y = Tensor.var(y_hidden, axis=0)
if moving_averages is not None:
Trainer._add_moving_averages(moving_averages, updates, number_of_batches)
update_mean = []
update_var = []
for param in params:
if 'W' in param.name:
OutputLog().write('Adding weight: {0}'.format(param.name))
update_mean.append(Tensor.mean(abs(updates[param])))
update_var.append(Tensor.var(abs(updates[param])))
update_mean = Tensor.stacklists(update_mean)
update_var = Tensor.stacklists(update_var)
# Building the theano function
# input : batch index
# output : both losses
# updates : gradient decent updates for all params
# givens : replacing inputs for each iteration
model = function(inputs=[t],
outputs=[Tensor.mean(loss_backward),
Tensor.mean(loss_forward),
Tensor.mean(variance_hidden_x),
Tensor.mean(variance_hidden_y),
x_hidden,
y_hidden, update_mean, update_var] + regularizations + [t],
updates=updates)
return model