Create network.py

ai/optimization/network.py

@@ -0,0 +1,54 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+class NeuralNetworkOptimizer:
+    def __init__(self, model: nn.Module, criterion: nn.Module, optimizer: optim.Optimizer):
+        self.model = model
+        self.criterion = criterion
+        self.optimizer = optimizer
+
+    def train(self, inputs: torch.Tensor, targets: torch.Tensor):
+        self.optimizer.zero_grad()
+        outputs = self.model(inputs)
+        loss = self.criterion(outputs, targets)
+        loss.backward()
+        self.optimizer.step()
+
+    def prune(self, amount: float):
+        # Prune the model by removing the smallest weights
+        for module in self.model.modules():
+            if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
+                weights = module.weight.data
+                threshold = torch.abs(weights).mean() * amount
+                mask = torch.abs(weights) > threshold
+                module.weight.data *= mask
+
+    def quantize(self, bits: int):
+        # Quantize the model's weights and activations
+        for module in self.model.modules():
+            if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
+                weights = module.weight.data
+                min_val = weights.min()
+                max_val = weights.max()
+                scale = (max_val - min_val) / (2 ** bits - 1)
+                module.weight.data = torch.round((weights - min_val) / scale) * scale + min_val
+
+    def knowledge_distillation(self, teacher_model: nn.Module):
+        # Perform knowledge distillation from the teacher model
+        for module in self.model.modules():
+            if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
+                teacher_weights = teacher_model.state_dict()[module.weight.name]
+                module.weight.data = teacher_weights
+
+class NeuralNetworkPruningScheduler:
+    def __init__(self, optimizer: NeuralNetworkOptimizer, prune_amount: float, prune_frequency: int):
+        self.optimizer = optimizer
+        self.prune_amount = prune_amount
+        self.prune_frequency = prune_frequency
+        self.epoch = 0
+
+    def step(self):
+        if self.epoch % self.prune_frequency == 0:
+            self.optimizer.prune(self.prune_amount)
+        self.epoch += 1