making torch code

doc/Programs/nntorch.py

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+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# load the dataset, split into input (X) and output (y) variables
+dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',')
+X = dataset[:,0:8]
+y = dataset[:,8]
+
+X = torch.tensor(X, dtype=torch.float32)
+y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1)
+
+# define the model
+class PimaClassifier(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.hidden1 = nn.Linear(8, 12)
+        self.act1 = nn.ReLU()
+        self.hidden2 = nn.Linear(12, 8)
+        self.act2 = nn.ReLU()
+        self.output = nn.Linear(8, 1)
+        self.act_output = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.act1(self.hidden1(x))
+        x = self.act2(self.hidden2(x))
+        x = self.act_output(self.output(x))
+        return x
+
+model = PimaClassifier()
+print(model)
+
+# train the model
+loss_fn   = nn.BCELoss()  # binary cross entropy
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+n_epochs = 100
+batch_size = 10
+
+for epoch in range(n_epochs):
+    for i in range(0, len(X), batch_size):
+        Xbatch = X[i:i+batch_size]
+        y_pred = model(Xbatch)
+        ybatch = y[i:i+batch_size]
+        loss = loss_fn(y_pred, ybatch)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+# compute accuracy
+y_pred = model(X)
+accuracy = (y_pred.round() == y).float().mean()
+print(f"Accuracy {accuracy}")
+
+# make class predictions with the model
+predictions = (model(X) > 0.5).int()
+for i in range(5):
+    print('%s => %d (expected %d)' % (X[i].tolist(), predictions[i], y[i]))