mindflow.pde.SteadyFlowWithLoss
- class mindflow.pde.SteadyFlowWithLoss(model, loss_fn='mse')[源代码]
基于数据驱动的定常流动问题求解的基类。
- 参数:
model (mindspore.nn.Cell) - 用于训练或测试的网络模型。
loss_fn (Union[str, Cell]) - 损失函数。默认值:
'mse'
。
- 支持平台:
Ascend
GPU
样例:
>>> import numpy as np >>> from mindspore import Tensor, nn >>> import mindspore >>> from mindflow.pde import SteadyFlowWithLoss >>> from mindflow.core import RelativeRMSELoss ... >>> class Net(nn.Cell): ... def __init__(self, num_class=10, num_channel=1): ... super(Net, self).__init__() ... self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') ... self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') ... self.fc1 = nn.Dense(16*5*5, 120, weight_init='ones') ... self.fc2 = nn.Dense(120, 84, weight_init='ones') ... self.fc3 = nn.Dense(84, num_class, weight_init='ones') ... self.relu = nn.ReLU() ... self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) ... self.flatten = nn.Flatten() ... ... def construct(self, x): ... x = self.max_pool2d(self.relu(self.conv1(x))) ... x = self.max_pool2d(self.relu(self.conv2(x))) ... x = self.flatten(x) ... x = self.relu(self.fc1(x)) ... x = self.relu(self.fc2(x)) ... x = self.fc3(x) ... return x ... >>> model = Net() >>> problem = SteadyFlowWithLoss(model, loss_fn=RelativeRMSELoss()) ... >>> inputs = Tensor(np.random.randn(32, 1, 32, 32), mindspore.float32) >>> label = Tensor(np.random.randn(32, 10), mindspore.float32) >>> loss = problem.get_loss(inputs, label) >>> print(loss) 680855.1