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MultiplicationNet.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 class MultiplicationNet(nn.Module):
     def __init__(self):
         super(MultiplicationNet, self).__init__()
-        layer_sizes = [2, 64, 128, 256, 1]
+        layer_sizes = [2, 256, 512, 1024, 1]
         layers = []
         for i in range(len(layer_sizes) - 1):
             layers.append(nn.Linear(layer_sizes[i], layer_sizes[i + 1]))

model.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:287330a63e005470ced2f2678a429438d4194f83c524e22b8c986c39c800a257
-size 172209
+oid sha256:cefcd52977cd055794c10dba186f321ad2b5aacee73975f99371d3843c0a524c
+size 2639793

train.py

@@ -12,8 +12,8 @@ def generate_data(num_samples, min_val=0, max_val=100):
     return np.hstack([x1, x2]), y
 
 def train():
-    num_samples = 10000
-    num_epochs = 5000
+    num_samples = 20000
+    num_epochs = 20000
     learning_rate = 0.01
 
     x, y = generate_data(num_samples)
@@ -23,7 +23,7 @@ def train():
     model = MultiplicationNet().to(device)
     criterion = nn.MSELoss().to(device)
     optimizer = optim.Adam(model.parameters(), lr=learning_rate)
-    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.95)
+    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.93)
 
     for epoch in range(num_epochs):
         outputs = model(x_train)