| import torch | |
| import torch.nn as nn | |
| from constants import NUM_ROWS, NODE_FEATURES | |
| class MatrixGenerator(nn.Module): | |
| def __init__(self, input_size, hidden_size, output_size): | |
| super(MatrixGenerator, self).__init__() | |
| self.generator = nn.Sequential( | |
| nn.Linear(input_size, hidden_size), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(hidden_size, hidden_size), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(hidden_size, output_size), | |
| nn.Sigmoid() | |
| ) | |
| def forward(self, noise): | |
| gen_matrix = self.generator(noise) | |
| gen_matrix = torch.reshape(gen_matrix, (NUM_ROWS,NODE_FEATURES)) | |
| return gen_matrix |