update

tutorial-contents/401_CNN.py

@@ -85,26 +85,16 @@ loss_func = nn.CrossEntropyLoss()                       # the target label is no
 
 # following function (plot_with_labels) is for visualization, can be ignored if not interested
 from matplotlib import cm
-try:
-    from sklearn.manifold import TSNE
-    HAS_SK = True
-except:
-    HAS_SK = False
-    print('Please install sklearn for layer visualization')
+try: from sklearn.manifold import TSNE; HAS_SK = True
+except: HAS_SK = False; print('Please install sklearn for layer visualization')
 def plot_with_labels(lowDWeights, labels):
     plt.cla()
     X, Y = lowDWeights[:, 0], lowDWeights[:, 1]
     for x, y, s in zip(X, Y, labels):
-        c = cm.rainbow(int(255 * s / 9))
-        plt.text(x, y, s, backgroundcolor=c, fontsize=9)
-    plt.xlim(X.min(), X.max())
-    plt.ylim(Y.min(), Y.max())
-    plt.title('Visualize last layer')
-    plt.show()
-    plt.pause(0.01)
+        c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)
+    plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)
 
 plt.ion()
-
 # training and testing
 for epoch in range(EPOCH):
     for step, (x, y) in enumerate(train_loader):   # gives batch data, normalize x when iterate train_loader