| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| |
| class DynamicLinearClassifier(nn.Module): |
| def __init__(self,output_size, input_size=250, num_layers=3, dropout_prob=0.5): |
| super(DynamicLinearClassifier, self).__init__() |
| self.hidden_layers = nn.ModuleList() |
| self.batch_norms = nn.ModuleList() |
| |
|
|
| layer_sizes = [int(input_size - i * (input_size - output_size) / (num_layers + 1)) for i in range(1, num_layers + 1)] |
|
|
| self.hidden_layers.append(nn.Linear(input_size, layer_sizes[0])) |
| self.batch_norms.append(nn.BatchNorm1d(layer_sizes[0])) |
|
|
| for i in range(1, num_layers): |
| self.hidden_layers.append(nn.Linear(layer_sizes[i-1], layer_sizes[i])) |
| self.batch_norms.append(nn.BatchNorm1d(layer_sizes[i])) |
|
|
| self.output_layer = nn.Linear(layer_sizes[-1], output_size) |
| self.dropout = nn.Dropout(dropout_prob) |
| self.loss_layer = nn.CrossEntropyLoss(reduction='mean') |
|
|
| def forward(self, x): |
| for i, hidden_layer in enumerate(self.hidden_layers): |
| x = hidden_layer(x) |
| x = self.batch_norms[i](x) |
| x = F.relu(x) |
| x = self.dropout(x) |
| x = self.output_layer(x) |
| return x |
| |
| def loss(self, predictions, labels): |
| loss_val = self.loss_layer(predictions, labels) |
| return loss_val |
|
|
| class LinearClassifier(nn.Module): |
| def __init__(self, output_size,input_size=250): |
| super(LinearClassifier, self).__init__() |
| self.linear1 = nn.Linear(input_size, 1) |
| self.linear2 = nn.Linear(1,output_size) |
| self.loss_layer = nn.CrossEntropyLoss(reduction='mean') |
| |
| def forward(self, x): |
| input = self.linear1(x) |
| return self.linear2(input) |
| |
| def loss(self, predictions, labels): |
| loss_val = self.loss_layer(predictions, labels) |
| return loss_val |
