mindspore 构建lstm模型
时间: 2023-11-26 22:05:51 浏览: 35
以下是使用MindSpore构建LSTM模型的示例代码:
```python
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
class LSTM(nn.Cell):
def __init__(self, input_size, hidden_size, num_layers):
super(LSTM, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.flatten = nn.Flatten()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, has_bias=True, batch_first=True, bidirectional=False)
self.fc = nn.Dense(hidden_size, 1)
self.sigmoid = P.Sigmoid()
def construct(self, x):
x = self.flatten(x)
h0 = Tensor.zeros((self.num_layers, x.shape[0], self.hidden_size))
c0 = Tensor.zeros((self.num_layers, x.shape[0], self.hidden_size))
output, _ = self.lstm(x, (h0, c0))
output = self.fc(output[:, -1, :])
output = self.sigmoid(output)
return output
```
其中,LSTM类继承自MindSpore的nn.Cell类,实现了LSTM模型的构建。构造函数中的input_size表示输入数据的特征数,hidden_size表示LSTM单元的隐藏层大小,num_layers表示LSTM模型的层数。
在construct方法中,首先使用MindSpore的nn.Flatten层将输入数据展平成二维张量,然后使用nn.LSTM层实现LSTM模型的前向传播,输出张量的形状为(batch_size, seq_length, hidden_size)。接着,使用nn.Dense层将输出张量映射到一个单一的输出值,最后使用MindSpore的Sigmoid操作对输出进行激活。
使用该模型可以进行二分类问题的训练和预测。例如,可以使用MindSpore的Adam优化器和BinaryCrossEntropy损失函数对该模型进行训练:
```python
import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Model, context
from mindspore.train.callback import LossMonitor
from mindspore.nn.metrics import Accuracy
from mindspore import dataset as ds
# define your dataset and dataloader
net = LSTM(input_size=10, hidden_size=32, num_layers=2)
criterion = nn.BCELoss()
optimizer = nn.Adam(params=net.trainable_params())
model = Model(net, criterion, optimizer, metrics={"acc": Accuracy()})
model.train(2, train_dataset, callbacks=[LossMonitor(100)], dataset_sink_mode=True)
```
其中,train_dataset是训练数据集,可以使用MindSpore的dataset模块进行加载。在训练过程中,每100个batch会打印一次训练损失,训练2个epoch。