regularizer function
正则化函数
using a regularizer
使用正则化
regularizer term
正则化项
add regularizer
添加正则化
regularizer weight
正则化权重
regularizer penalty
正则化惩罚
regularizer effect
正则化效果
regularizer parameter
正则化参数
the regularizer helped prevent overfitting in the model.
正则化器有助于防止模型过拟合。
we used an l1 regularizer to encourage sparsity.
我们使用L1正则化器来鼓励稀疏性。
adding a regularizer can improve generalization performance.
添加正则化器可以提高泛化性能。
the weight decay regularizer penalized large weights.
权重衰减正则化器惩罚了大的权重。
we compared models with and without a regularizer.
我们比较了带有和不带正则化器的模型。
the regularizer minimized the complexity of the neural network.
正则化器最小化了神经网络的复杂性。
ridge regression employs an l2 regularizer.
岭回归使用L2正则化器。
elastic net combines l1 and l2 regularizers.
弹性网络结合了L1和L2正则化器。
the optimal regularizer parameter was determined empirically.
最优的正则化器参数是经验确定的。
a strong regularizer can lead to underfitting.
强大的正则化器可能导致欠拟合。
regularizer function
正则化函数
using a regularizer
使用正则化
regularizer term
正则化项
add regularizer
添加正则化
regularizer weight
正则化权重
regularizer penalty
正则化惩罚
regularizer effect
正则化效果
regularizer parameter
正则化参数
the regularizer helped prevent overfitting in the model.
正则化器有助于防止模型过拟合。
we used an l1 regularizer to encourage sparsity.
我们使用L1正则化器来鼓励稀疏性。
adding a regularizer can improve generalization performance.
添加正则化器可以提高泛化性能。
the weight decay regularizer penalized large weights.
权重衰减正则化器惩罚了大的权重。
we compared models with and without a regularizer.
我们比较了带有和不带正则化器的模型。
the regularizer minimized the complexity of the neural network.
正则化器最小化了神经网络的复杂性。
ridge regression employs an l2 regularizer.
岭回归使用L2正则化器。
elastic net combines l1 and l2 regularizers.
弹性网络结合了L1和L2正则化器。
the optimal regularizer parameter was determined empirically.
最优的正则化器参数是经验确定的。
a strong regularizer can lead to underfitting.
强大的正则化器可能导致欠拟合。
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