origin

digit_depth/train/mlp_model.py

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+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class MLP(nn.Module):
+    dropout_p = 0.05
+
+    def __init__(
+            self, input_size=5, output_size=3, hidden_size=32):
+        super().__init__()
+
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, hidden_size)
+        self.fc3 = nn.Linear(hidden_size, hidden_size)
+        self.fc4 = nn.Linear(hidden_size, output_size)
+        self.drop = nn.Dropout(p=self.dropout_p)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.drop(x)
+        x = F.relu(self.fc2(x))
+        x = self.drop(x)
+        x = self.fc3(x)
+        x = self.drop(x)
+        x = self.fc4(x)
+        return x