update accuracy function

tutorial-contents/302_classification.py

@@ -62,7 +62,7 @@ for t in range(100):
         pred_y = prediction.data.numpy().squeeze()
         target_y = y.data.numpy()
         plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')
-        accuracy = sum(pred_y == target_y)/200.
+        accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)
         plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color':  'red'})
         plt.pause(0.1)
 

tutorial-contents/401_CNN.py

@@ -115,8 +115,8 @@ for epoch in range(EPOCH):
 
         if step % 50 == 0:
             test_output, last_layer = cnn(test_x)
-            pred_y = torch.max(test_output, 1)[1].data.squeeze()
-            accuracy = float(sum(pred_y == test_y)) / float(test_y.size(0))
+            pred_y = torch.max(test_output, 1)[1].data.squeeze().numpy()
+            accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
             print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
             if HAS_SK:
                 # Visualization of trained flatten layer (T-SNE)

tutorial-contents/402_RNN_classifier.py

@@ -95,7 +95,7 @@ for epoch in range(EPOCH):
         if step % 50 == 0:
             test_output = rnn(test_x)                   # (samples, time_step, input_size)
             pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
-            accuracy = float(sum(pred_y == test_y)) / float(test_y.size)
+            accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size)
             print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
 
 # print 10 predictions from test data