train_fcae.py 文件源码

def build_fcae(input_var, channels=1):
    ret = {}
    ret['input'] = layer = InputLayer(shape=(None, channels, None, None), input_var=input_var)
    ret['conv1'] = layer = bn(Conv2DLayer(layer, num_filters=128, filter_size=5, pad='full'))
    ret['pool1'] = layer =  MaxPool2DLayer(layer, pool_size=2)
    ret['conv2'] = layer = bn(Conv2DLayer(layer, num_filters=256, filter_size=3, pad='full'))
    ret['pool2'] = layer = MaxPool2DLayer(layer, pool_size=2)
    ret['conv3'] = layer = bn(Conv2DLayer(layer, num_filters=32, filter_size=3, pad='full'))
    ret['enc'] = layer = GlobalPoolLayer(layer)
    ret['ph1'] = layer = NonlinearityLayer(layer, nonlinearity=None)
    ret['ph2'] = layer = NonlinearityLayer(layer, nonlinearity=None)
    ret['unenc'] = layer = bn(InverseLayer(layer, ret['enc']))
    ret['deconv3'] = layer = bn(Conv2DLayer(layer, num_filters=256, filter_size=3))
    ret['depool2'] = layer = InverseLayer(layer, ret['pool2'])
    ret['deconv2'] = layer = bn(Conv2DLayer(layer, num_filters=128, filter_size=3))
    ret['depool1'] = layer = InverseLayer(layer, ret['pool1'])
    ret['output'] = layer = Conv2DLayer(layer, num_filters=1, filter_size=5,
                                     nonlinearity=nn.nonlinearities.sigmoid)
    return ret