| import torch
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
| import torch.optim as optim
|
|
|
| class neural_network (nn.Module):
|
| def __init__(self, input_dim, hidden_dim, output_dim):
|
| super(neural_network, self).__init__()
|
| self.hidden = nn.Linear (input_dim, hidden_dim)
|
| self.act = nn.ReLU()
|
| self.output = nn.Linear (hidden_dim, output_dim)
|
|
|
| def forward (self, x):
|
| x = self.hidden (x)
|
| x = self.act (x)
|
| x = self.output (x)
|
| return x
|
|
|
|
|
| input_dim = 4
|
| hidden_dim = 32
|
| output_dim = 4
|
|
|
| model = neural_network(input_dim, hidden_dim, output_dim)
|
| print(model)
|
|
|
| criterion = nn.CrossEntropyLoss()
|
| optimizer = optim.Adam(model.parameters(), lr= 0.01) |