| 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) |