Create MNIST Classification Model..py

Author: infinitecoder1729

MNIST Classification Model..py

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+import torch as tch
+import torchvision.datasets as dt
+import torchvision.transforms as trans
+import torch.nn as nn
+import matplotlib.pyplot as plt
+from time import time
+
+train = dt.MNIST(root="./datasets", train=True, transform=trans.ToTensor(), download=True)
+test = dt.MNIST(root="./datasets", train=False, transform=trans.ToTensor(), download=True)
+print("No. of Training examples: ",len(train))
+print("No. of Test examples: ",len(test))
+
+train_batch = tch.utils.data.DataLoader(train, batch_size=30, shuffle=True)
+
+
+input = 784
+hidden = 490
+output = 10
+
+model = nn.Sequential(nn.Linear(input, hidden),
+                      nn.LeakyReLU(),
+                      nn.Linear(hidden, output),
+                      nn.LogSoftmax(dim=1))
+
+lossfn = nn.NLLLoss()
+images, labels = next(iter(train_batch))
+images = images.view(images.shape[0], -1)
+
+logps = model(images)
+loss = lossfn(logps, labels)
+loss.backward()
+
+optimize = tch.optim.SGD(model.parameters(), lr=0.003, momentum=0.9)
+time_start = time()
+epochs = 18
+for num in range(epochs):
+    run=0
+    for images, labels in train_batch:
+        images = images.view(images.shape[0], -1)
+        optimize.zero_grad()
+        output = model(images)
+        loss = lossfn(output, labels)
+        loss.backward()
+        optimize.step()
+        run += loss.item()
+    else:
+        print("Epoch Number : {} = Loss : {}".format(num, run/len(train_batch)))
+Elapsed=(time()-time_start)/60
+print("\nTraining Time (in minutes) : ",Elapsed)
+
+correct=0
+all = 0
+for images,labels in test:
+  img = images.view(1, 784)
+  with tch.no_grad():
+    logps = model(img)   
+  ps = tch.exp(logps)
+  probab = list(ps.numpy()[0])
+  prediction = probab.index(max(probab))
+  truth = labels
+  if(truth == prediction):
+    correct += 1
+  all += 1
+
+print("Number Of Images Tested : ", all)
+print("Model Accuracy : ", (correct/all))
+
+tch.save(model, './mnist_model.pt')