update

tutorial-contents/401_CNN.py

@@ -17,10 +17,10 @@ import matplotlib.pyplot as plt
 torch.manual_seed(1)    # reproducible
 
 # Hyper Parameters
-EPOCH = 1           # train the training data n times, to save time, we just train 1 epoch
+EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch
 BATCH_SIZE = 50
-LR = 0.001          # learning rate
-DOWNLOAD_MNIST = False
+LR = 0.001              # learning rate
+DOWNLOAD_MNIST = True   # set to False if you have downloaded
 
 
 # Mnist digits dataset
@@ -33,8 +33,8 @@ train_data = torchvision.datasets.MNIST(
 )
 
 # plot one example
-print(train_data.train_data.size())     # (60000, 28, 28)
-print(train_data.train_labels.size())   # (60000)
+print(train_data.train_data.size())                 # (60000, 28, 28)
+print(train_data.train_labels.size())               # (60000)
 plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
 plt.title('%i' % train_data.train_labels[0])
 plt.show()
@@ -51,28 +51,28 @@ test_y = test_data.test_labels[:2000]
 class CNN(nn.Module):
     def __init__(self):
         super(CNN, self).__init__()
-        self.conv1 = nn.Sequential(     # input shape (1, 28, 28)
+        self.conv1 = nn.Sequential(         # input shape (1, 28, 28)
             nn.Conv2d(
-                in_channels=1,          # input height
-                out_channels=16,        # n_filters
-                kernel_size=5,          # filter size
-                stride=1,               # filter movement/step
-                padding=2,              # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
-            ),                          # output shape (16, 28, 28)
-            nn.ReLU(),                  # activation
-            nn.MaxPool2d(kernel_size=2),      # choose max value in 2x2 area, output shape (16, 14, 14)
+                in_channels=1,              # input height
+                out_channels=16,            # n_filters
+                kernel_size=5,              # filter size
+                stride=1,                   # filter movement/step
+                padding=2,                  # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
+            ),                              # output shape (16, 28, 28)
+            nn.ReLU(),                      # activation
+            nn.MaxPool2d(kernel_size=2),    # choose max value in 2x2 area, output shape (16, 14, 14)
         )
-        self.conv2 = nn.Sequential(     # input shape (1, 28, 28)
-            nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
-            nn.ReLU(),                  # activation
-            nn.MaxPool2d(2),            # output shape (32, 7, 7)
+        self.conv2 = nn.Sequential(         # input shape (1, 28, 28)
+            nn.Conv2d(16, 32, 5, 1, 2),     # output shape (32, 14, 14)
+            nn.ReLU(),                      # activation
+            nn.MaxPool2d(2),                # output shape (32, 7, 7)
         )
         self.out = nn.Linear(32 * 7 * 7, 10)   # fully connected layer, output 10 classes
 
     def forward(self, x):
         x = self.conv1(x)
         x = self.conv2(x)
-        x = x.view(x.size(0), -1)       # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
+        x = x.view(x.size(0), -1)           # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
         output = self.out(x)
         return output
 
@@ -81,7 +81,7 @@ cnn = CNN()
 print(cnn)  # net architecture
 
 optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)   # optimize all cnn parameters
-loss_func = nn.CrossEntropyLoss()   # the target label is not one-hotted
+loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted
 
 # training and testing
 for epoch in range(EPOCH):