output = nn(data)
时间: 2024-05-19 20:11:07 浏览: 5
This line of code suggests that there is a neural network model called "nn" and it is being used to make predictions on the input data. The output of the model is being assigned to the variable "output". The nature of the output depends on the architecture of the neural network model and the type of problem it is being used to solve (e.g. classification, regression, etc.).
相关问题
加载数据 X = data.iloc[:, :-1].values y = data.iloc[:, -1:].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 训练模型 input_dim=13 hidden_dim=25 output_dim=1 nn=NeuralNetwork(input_dim, hidden_dim, output_dim) learning_rate=0.0016 num_epochs=2000 loss_history=nn.train(X, y, learning_rate, num_epochs) plt.plot(loss_history) plt.title('loss') plt.xlabel('Epoch') plt.ylabel('MSE') X_pred = data_pred.iloc[:, :-1].values y_pred = data_pred.iloc[:, -1:].values y_pred = nn.predict(X_pred)报错NameError: name 'data_pred' is not defined解决代码
你需要先定义 `data_pred` 变量并赋值,再执行 `X_pred = data_pred.iloc[:, :-1].values` 和 `y_pred = data_pred.iloc[:, -1:].values` 这两行代码。例如:
```python
import pandas as pd
# 加载数据
data = pd.read_csv('train_data.csv')
data_pred = pd.read_csv('predict_data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1:].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练模型
input_dim = 13
hidden_dim = 25
output_dim = 1
nn = NeuralNetwork(input_dim, hidden_dim, output_dim)
learning_rate = 0.0016
num_epochs = 2000
loss_history = nn.train(X, y, learning_rate, num_epochs)
plt.plot(loss_history)
plt.title('loss')
plt.xlabel('Epoch')
plt.ylabel('MSE')
X_pred = data_pred.iloc[:, :-1].values
y_pred = data_pred.iloc[:, -1:].values
y_pred = nn.predict(X_pred)
```
class PoetryModel(nn.Module): def init(self, vocab_size, embedding_dim, hidden_dim): super(PoetryModel, self).init() self.hidden_dim = hidden_dim self.embedding = nn.Embedding(vocab_size, embedding_dim) self.lstm = nn.LSTM(embedding_dim, self.hidden_dim, num_layers=3) self.classifier=nn.Sequential( nn.Linear(self.hidden_dim, 512), nn.ReLU(inplace=True), nn.Linear(512, 2048), nn.ReLU(inplace=True), nn.Linear(2048, vocab_size) ) def forward(self, input, hidden = None): seq_len, batch_size = input.size() if hidden is None: h_0 = input.data.new(3, batch_size, self.hidden_dim).fill_(0).float() c_0 = input.data.new(3, batch_size, self.hidden_dim).fill_(0).float() else: h_0, c_0 = hidden embeds = self.embedding(input) output, hidden = self.lstm(embeds, (h_0, c_0)) output = self.classifier(output.view(seq_len * batch_size, -1)) return output, hidden 请优化这段代码
这段代码已经写得比较好了,但是有几个可以改进的地方:
1. 在初始化函数中使用 super() 函数时,应该将 PoetryModel 类的名称作为第一个参数,而不是使用 super() 函数的默认参数。
2. 在 forward 函数中,应该使用 new_zeros() 方法来初始化 h_0 和 c_0,而不是使用 fill_() 方法。
3. 在 forward 函数中,应该使用 contiguous() 方法来确保输入的张量在内存中是连续的,这样可以加快计算速度。
改进后的代码如下所示:
```python
class PoetryModel(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim):
super(PoetryModel, self).__init__()
self.hidden_dim = hidden_dim
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, self.hidden_dim, num_layers=3)
self.classifier = nn.Sequential(
nn.Linear(self.hidden_dim, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 2048),
nn.ReLU(inplace=True),
nn.Linear(2048, vocab_size)
)
def forward(self, input, hidden=None):
seq_len, batch_size = input.size()
if hidden is None:
h_0 = input.data.new_zeros(3, batch_size, self.hidden_dim).float()
c_0 = input.data.new_zeros(3, batch_size, self.hidden_dim).float()
else:
h_0, c_0 = hidden
embeds = self.embedding(input)
embeds = embeds.contiguous()
output, hidden = self.lstm(embeds, (h_0, c_0))
output = self.classifier(output.view(seq_len * batch_size, -1))
return output, hidden
```
这个优化版本主要是将 super() 函数中的参数修改为 PoetryModel 类的名称,使用了 new_zeros() 方法初始化 h_0 和 c_0,使用了 contiguous() 方法确保输入的张量在内存中是连续的。这样可以提高代码的效率和可读性。