本文整理汇总了Python中lasagne.updates方法的典型用法代码示例。如果您正苦于以下问题：Python lasagne.updates方法的具体用法？Python lasagne.updates怎么用？Python lasagne.updates使用的例子？那么, 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lasagne的用法示例。
在下文中一共展示了lasagne.updates方法的4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: make_training_functions
# 需要导入模块: import lasagne [as 别名]
# 或者: from lasagne import updates [as 别名]
def make_training_functions(network, new_params, input_var, aug_var, target_var):
output = lasagne.layers.get_output(network, deterministic=True, batch_norm_use_averages=True, batch_norm_update_averages=False);
loss = lasagne.objectives.binary_crossentropy(output, target_var).mean();
deter_output = lasagne.layers.get_output(network, deterministic=True);
deter_loss = lasagne.objectives.binary_crossentropy(deter_output, target_var).mean();
params = layers.get_all_params(network, trainable=True);
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=LearningRate, momentum=0.985);
new_params_updates = lasagne.updates.nesterov_momentum(loss, new_params, learning_rate=LearningRate, momentum=0.985);
val_fn = theano.function([input_var, aug_var, target_var], [deter_loss, deter_output]);
train_fn = theano.function([input_var, aug_var, target_var], loss, updates=updates);
new_params_train_fn = theano.function([input_var, aug_var, target_var], loss, updates=new_params_updates);
return train_fn, new_params_train_fn, val_fn; 示例2: make_training_functions
# 需要导入模块: import lasagne [as 别名]
# 或者: from lasagne import updates [as 别名]
def make_training_functions(network, new_params, input_var, aug_var, target_var):
output = lasagne.layers.get_output(network);
loss = lasagne.objectives.binary_crossentropy(output, target_var).mean();
deter_output = lasagne.layers.get_output(network, deterministic=True);
deter_loss = lasagne.objectives.binary_crossentropy(deter_output, target_var).mean();
params = layers.get_all_params(network, trainable=True);
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=LearningRate, momentum=0.985);
new_params_updates = lasagne.updates.nesterov_momentum(loss, new_params, learning_rate=LearningRate, momentum=0.985);
val_fn = theano.function([input_var, aug_var, target_var], [deter_loss, deter_output]);
train_fn = theano.function([input_var, aug_var, target_var], loss, updates=updates);
new_params_train_fn = theano.function([input_var, aug_var, target_var], loss, updates=new_params_updates);
return train_fn, new_params_train_fn, val_fn; 示例3: make_training_functions
# 需要导入模块: import lasagne [as 别名]
# 或者: from lasagne import updates [as 别名]
def make_training_functions(network, new_params, input_var, aug_var, target_var):
output = lasagne.layers.get_output(network, deterministic=False);
loss = lasagne.objectives.binary_crossentropy(output, target_var).mean();
deter_output = lasagne.layers.get_output(network, deterministic=True);
deter_loss = lasagne.objectives.binary_crossentropy(deter_output, target_var).mean();
params = layers.get_all_params(network, trainable=True);
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=LearningRate, momentum=0.985);
new_params_updates = lasagne.updates.nesterov_momentum(loss, new_params, learning_rate=LearningRate, momentum=0.985);
val_fn = theano.function([input_var, aug_var, target_var], [deter_loss, deter_output]);
train_fn = theano.function([input_var, aug_var, target_var], loss, updates=updates);
new_params_train_fn = theano.function([input_var, aug_var, target_var], loss, updates=new_params_updates);
return train_fn, new_params_train_fn, val_fn; 示例4: make_training_functions
# 需要导入模块: import lasagne [as 别名]
# 或者: from lasagne import updates [as 别名]
def make_training_functions(network, new_params, input_var, aug_var, target_var):
output = lasagne.layers.get_output(network, deterministic=True);
loss = lasagne.objectives.binary_crossentropy(output, target_var).mean();
deter_output = lasagne.layers.get_output(network, deterministic=True);
deter_loss = lasagne.objectives.binary_crossentropy(deter_output, target_var).mean();
params = layers.get_all_params(network, trainable=True);
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=LearningRate, momentum=0.985);
new_params_updates = lasagne.updates.nesterov_momentum(loss, new_params, learning_rate=LearningRate, momentum=0.985);
val_fn = theano.function([input_var, aug_var, target_var], [deter_loss, deter_output]);
train_fn = theano.function([input_var, aug_var, target_var], loss, updates=updates);
new_params_train_fn = theano.function([input_var, aug_var, target_var], loss, updates=new_params_updates);
return train_fn, new_params_train_fn, val_fn;