loss
module loss
loss模块是模型训练使用的模块,提供了多个损失函数
cross_entropy(predicts, onehot_targets)
求交叉熵损失
参数:
predicts:Var
输出层的预测值,dtype=float
,shape=(batch_size, num_classes)
onehot_targets:Var
onehot编码的标签,dtype=float
,shape=(batch_size, num_classes)
返回:交叉熵损失
返回类型:Var
示例
>>> predict = np.random.random([2,3])
>>> onehot = np.array([[1., 0., 0.], [0., 1., 0.]])
>>> nn.loss.cross_entropy(predict, onehot)
array(4.9752955, dtype=float32)
kl(predicts, onehot_targets)
求KL损失
参数:
predicts:Var
输出层的预测值,dtype=float
,shape=(batch_size, num_classes)
onehot_targets:Var
onehot编码的标签,dtype=float
,shape=(batch_size, num_classes)
返回:KL损失
返回类型:Var
示例
>>> predict = np.random.random([2,3])
>>> onehot = np.array([[1., 0., 0.], [0., 1., 0.]])
>>> nn.loss.kl(predict, onehot)
array(inf, dtype=float32)
mse(predicts, onehot_targets)
求MSE损失
参数:
predicts:Var
输出层的预测值,dtype=float
,shape=(batch_size, num_classes)
onehot_targets:Var
onehot编码的标签,dtype=float
,shape=(batch_size, num_classes)
返回:MSE损失
返回类型:Var
示例
>>> predict = np.random.random([2,3])
>>> onehot = np.array([[1., 0., 0.], [0., 1., 0.]])
>>> nn.loss.mse(predict, onehot)
array(1.8694793, dtype=float32)
mae(predicts, onehot_targets)
求MAE损失
参数:
predicts:Var
输出层的预测值,dtype=float
,shape=(batch_size, num_classes)
onehot_targets:Var
onehot编码的标签,dtype=float
,shape=(batch_size, num_classes)
返回:MAE损失
返回类型:Var
示例
>>> predict = np.random.random([2,3])
>>> onehot = np.array([[1., 0., 0.], [0., 1., 0.]])
>>> nn.loss.mae(predict, onehot)
array(2.1805272, dtype=float32)
hinge(predicts, onehot_targets)
求Hinge损失
参数:
predicts:Var
输出层的预测值,dtype=float
,shape=(batch_size, num_classes)
onehot_targets:Var
onehot编码的标签,dtype=float
,shape=(batch_size, num_classes)
返回:Hinge损失
返回类型:Var
示例
>>> predict = np.random.random([2,3])
>>> onehot = np.array([[1., 0., 0.], [0., 1., 0.]])
>>> nn.loss.hinge(predict, onehot)
array(2.791432, dtype=float32)