From 82dccb7a66ece46db19c7f4f5c922cc51e576058 Mon Sep 17 00:00:00 2001
From: Aditya Soni <soniaditya922@gmail.com>
Date: Sun, 21 Jan 2018 11:05:12 +0530
Subject: [PATCH] Create mnist_softmax.py

---
 PyTorch/mnist_softmax.py | 94 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 PyTorch/mnist_softmax.py

diff --git a/PyTorch/mnist_softmax.py b/PyTorch/mnist_softmax.py
new file mode 100644
index 0000000..48d2483
--- /dev/null
+++ b/PyTorch/mnist_softmax.py
@@ -0,0 +1,94 @@
+# https://github.com/pytorch/examples/blob/master/mnist/main.py
+from __future__ import print_function
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+from torch.autograd import Variable
+
+# Training settings
+batch_size = 64
+
+# MNIST Dataset
+train_dataset = datasets.MNIST(root='./mnist_data/',
+                               train=True,
+                               transform=transforms.ToTensor(),
+                               download=True)
+
+test_dataset = datasets.MNIST(root='./mnist_data/',
+                              train=False,
+                              transform=transforms.ToTensor())
+
+# Data Loader (Input Pipeline)
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=batch_size,
+                                           shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                          batch_size=batch_size,
+                                          shuffle=False)
+
+
+class Net(nn.Module):
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.l1 = nn.Linear(784, 520)
+        self.l2 = nn.Linear(520, 320)
+        self.l3 = nn.Linear(320, 240)
+        self.l4 = nn.Linear(240, 120)
+        self.l5 = nn.Linear(120, 10)
+
+    def forward(self, x):
+        x = x.view(-1, 784)  # Flatten the data (n, 1, 28, 28)-> (n, 784)
+        x = F.relu(self.l1(x))
+        x = F.relu(self.l2(x))
+        x = F.relu(self.l3(x))
+        x = F.relu(self.l4(x))
+        return self.l5(x)
+
+
+model = Net()
+
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
+
+
+def train(epoch):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = Variable(data), Variable(target)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = criterion(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % 10 == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.data[0]))
+
+
+def test():
+    model.eval()
+    test_loss = 0
+    correct = 0
+    for data, target in test_loader:
+        data, target = Variable(data, volatile=True), Variable(target)
+        output = model(data)
+        # sum up batch loss
+        test_loss += criterion(output, target).data[0]
+        # get the index of the max
+        pred = output.data.max(1, keepdim=True)[1]
+        correct += pred.eq(target.data.view_as(pred)).cpu().sum()
+
+    test_loss /= len(test_loader.dataset)
+    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+        test_loss, correct, len(test_loader.dataset),
+        100. * correct / len(test_loader.dataset)))
+
+
+for epoch in range(1, 10):
+    train(epoch)
+    test()