如何用自动机器学习实现神经网络进化

'relu_'+i,

nn.ReLU()

)

if layer_info['activation']['val'] == 'sigmoid':

self.model.add_module(

'sigm_'+i,

nn.Sigmoid()

)

if layer_info['activation']['val'] == 'elu':

self.model.add_module(

'elu_'+i,

nn.ELU()

)

previous_units = layer_info['nb_units']['val']

self.model.add_module(

'classification_layer',

nn.Linear(previous_units, 10)

)

self.model.add_module('sofmax', nn.LogSoftmax())

self.model.cpu()

if build_info['optimizer']['val'] == 'adam':

optimizer = optim.Adam(self.model.parameters(),

lr=build_info['weight_decay']['val'],

weight_decay=build_info['weight_decay']['val'])

elif build_info['optimizer']['val'] == 'adadelta':

optimizer = optim.Adadelta(self.model.parameters(),

lr=build_info['weight_decay']['val'],

weight_decay=build_info['weight_decay']['val'])

elif build_info['optimizer']['val'] == 'rmsprop':

optimizer = optim.RMSprop(self.model.parameters(),

lr=build_info['weight_decay']['val'],

weight_decay=build_info['weight_decay']['val'])

else:

optimizer = optim.SGD(self.model.parameters(),

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