[英]Add bias to Lasagne neural network layers
I am wondering if there is a way to add bias node to each layer in Lasagne neural network toolkit? 我想知道是否有办法在Lasagne神经网络工具包中为每一层添加偏置节点? I have been trying to find related information in documentation.
我一直试图在文档中找到相关信息。
This is the network I built but i don't know how to add a bias node to each layer. 这是我构建的网络,但我不知道如何向每个层添加偏向节点。
def build_mlp(input_var=None):
# This creates an MLP of two hidden layers of 800 units each, followed by
# a softmax output layer of 10 units. It applies 20% dropout to the input
# data and 50% dropout to the hidden layers.
# Input layer, specifying the expected input shape of the network
# (unspecified batchsize, 1 channel, 28 rows and 28 columns) and
# linking it to the given Theano variable `input_var`, if any:
l_in = lasagne.layers.InputLayer(shape=(None, 60),
input_var=input_var)
# Apply 20% dropout to the input data:
l_in_drop = lasagne.layers.DropoutLayer(l_in, p=0.2)
# Add a fully-connected layer of 800 units, using the linear rectifier, and
# initializing weights with Glorot's scheme (which is the default anyway):
l_hid1 = lasagne.layers.DenseLayer(
l_in_drop, num_units=800,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.Uniform())
# We'll now add dropout of 50%:
l_hid1_drop = lasagne.layers.DropoutLayer(l_hid1, p=0.5)
# Another 800-unit layer:
l_hid2 = lasagne.layers.DenseLayer(
l_hid1_drop, num_units=800,
nonlinearity=lasagne.nonlinearities.rectify)
# 50% dropout again:
l_hid2_drop = lasagne.layers.DropoutLayer(l_hid2, p=0.5)
# Finally, we'll add the fully-connected output layer, of 10 softmax units:
l_out = lasagne.layers.DenseLayer(
l_hid2_drop, num_units=2,
nonlinearity=lasagne.nonlinearities.softmax)
# Each layer is linked to its incoming layer(s), so we only need to pass
# the output layer to give access to a network in Lasagne:
return l_out
Actually you don't have to explicitly create biases, because DenseLayer()
, and convolution base layers too, has a default keyword argument: 实际上你不必显式创建偏差,因为
DenseLayer()
和卷积基础层也有一个默认的关键字参数:
b=lasagne.init.Constant(0.)
. b=lasagne.init.Constant(0.)
。
Thus you can avoid creating bias
, if you don't want to have with explicitly pass bias=None
, but this is not that case. 因此,如果您不希望显式传递
bias=None
,则可以避免创建bias
,但事实并非如此。
Thus in brief you do have bias parameters while you don't pass None
to bias
parameter eg: 因此,在短暂你有偏见参数,而你没有通过
None
以bias
参数如:
hidden = Denselayer(...bias=None)
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