update

tutorial-contents/301_regression.py

@@ -41,7 +41,6 @@ optimizer = torch.optim.SGD(net.parameters(), lr=0.5)
 loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss
 
 plt.ion()   # something about plotting
-plt.show()
 
 for t in range(100):
     prediction = net(x)     # input x and predict based on x

tutorial-contents/302_classification.py

@@ -47,7 +47,6 @@ optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
 loss_func = torch.nn.CrossEntropyLoss()  # the target label is NOT an one-hotted
 
 plt.ion()   # something about plotting
-plt.show()
 
 for t in range(100):
     out = net(x)                 # input x and predict based on x

tutorial-contents/303_build_nn_quickly.py

@@ -32,4 +32,18 @@ net2 = torch.nn.Sequential(
 
 
 print(net1)     # net1 architecture
-print(net2)     # net2 architecture
+"""
+Net (
+  (hidden): Linear (1 -> 10)
+  (predict): Linear (10 -> 1)
+)
+"""
+
+print(net2)     # net2 architecture
+"""
+Sequential (
+  (0): Linear (1 -> 10)
+  (1): ReLU ()
+  (2): Linear (10 -> 1)
+)
+"""

tutorial-contents/403_RNN_regressor.py

@@ -64,7 +64,6 @@ h_state = None      # for initial hidden state
 
 plt.figure(1, figsize=(12, 5))
 plt.ion()           # continuously plot
-plt.show()
 
 for step in range(60):
     start, end = step * np.pi, (step+1)*np.pi   # time range

tutorial-contents/404_autoencoder.py

@@ -85,7 +85,6 @@ loss_func = nn.MSELoss()
 # initialize figure
 f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
 plt.ion()   # continuously plot
-plt.show()
 
 # original data (first row) for viewing
 view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)

tutorial-contents/406_GAN.py

@@ -54,7 +54,7 @@ opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)
 opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)
 
 plt.ion()   # something about continuous plotting
-plt.show()
+
 for step in range(10000):
     artist_paintings = artist_works()           # real painting from artist
     G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS))    # random ideas

tutorial-contents/406_conditional_GAN.py

@@ -57,7 +57,7 @@ opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)
 opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)
 
 plt.ion()   # something about continuous plotting
-plt.show()
+
 for step in range(10000):
     artist_paintings, labels = artist_works_with_labels()           # real painting, label from artist
     G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS))            # random ideas

tutorial-contents/501_why_torch_dynamic_graph.py

@@ -54,7 +54,6 @@ h_state = None   # for initial hidden state
 
 plt.figure(1, figsize=(12, 5))
 plt.ion()   # continuously plot
-plt.show()
 
 ########################  Below is different #########################
 

tutorial-contents/503_dropout.py

@@ -58,7 +58,6 @@ optimizer_drop = torch.optim.Adam(net_dropped.parameters(), lr=0.01)
 loss_func = torch.nn.MSELoss()
 
 plt.ion()   # something about plotting
-plt.show()
 
 for t in range(500):
     pred_ofit = net_overfitting(x)

tutorial-contents/504_batch_normalization.py

@@ -48,7 +48,6 @@ train_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shu
 # show data
 plt.scatter(train_x.numpy(), train_y.numpy(), c='#FF9359', s=50, alpha=0.2, label='train')
 plt.legend(loc='upper left')
-plt.show()
 
 class Net(nn.Module):
     def __init__(self, batch_normalization=False):