Initial commit to add a simple network file

SimpleNet.py

@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 22 13:07:34 2022
+
+@author: sunro
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as f
+import matplotlib as plt
+
+class simpleNet(nn.Module):
+
+    def __init__(self,inFeature=2,outFeature=2,hiddenLayer1=8,hiddenLayer2=9):
+        super().__init__()
+        self.fc1 = nn.Linear(inFeature,hiddenLayer1)
+        self.fc2 = nn.Linear(hiddenLayer1,hiddenLayer2)
+        self.out = nn.Linear(hiddenLayer2,outFeature)
+        self.criterion = nn.CrossEntropyLoss()
+        self.optimizer = torch.optim.Adam(self.parameters(), lr=0.01)
+
+    def predict(self, x):
+        x = f.relu(self.fc1(x))
+        x = f.relu(self.fc2(x))
+        x = self.out(x)
+        return x
+
+    def train(self, x, y, epochs=100):
+        losses = []
+        i=0
+        for i in range(epochs):
+            i=i+1
+            predict = self.predict(x)
+            loss = self.criterion(predict, y)
+            losses.append(loss)
+            if i%10 == 1:
+                print(f'Epoch number: {i:2} Corresponding loss: {loss.item():10.2f}')
+
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+        self.drawGD(losses, epochs)
+
+    def drawGD(self,losses,epoches):
+        plt.plot(range(epoches),losses)
+        plt.xlabel("Epoches")
+        plt.ylabel("Losses")