update

tutorial-contents/401_CNN.py

@@ -20,7 +20,7 @@ torch.manual_seed(1)    # reproducible
 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
+DOWNLOAD_MNIST = True   # set to False if you have downloaded
 
 
 # Mnist digits dataset

tutorial-contents/402_RNN_classifier.py

@@ -23,7 +23,7 @@ BATCH_SIZE = 64
 TIME_STEP = 28          # rnn time step / image height
 INPUT_SIZE = 28         # rnn input size / image width
 LR = 0.01               # learning rate
-DOWNLOAD_MNIST = False  # set to True if haven't download the data
+DOWNLOAD_MNIST = True   # set to True if haven't download the data
 
 
 # Mnist digital dataset

tutorial-contents/404_autoencoder.py

@@ -39,9 +39,9 @@ train_data = torchvision.datasets.MNIST(
 # plot one example
 print(train_data.train_data.size())     # (60000, 28, 28)
 print(train_data.train_labels.size())   # (60000)
-# plt.imshow(train_data.train_data[2].numpy(), cmap='gray')
+plt.imshow(train_data.train_data[2].numpy(), cmap='gray')
-# plt.title('%i' % train_data.train_labels[2])
+plt.title('%i' % train_data.train_labels[2])
-# plt.show()
+plt.show()
 
 # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
 train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

tutorial-contents/405_DQN_Reinforcement_learning.py

@@ -46,7 +46,7 @@ class DQN(object):
     def __init__(self):
         self.eval_net, self.target_net = Net(), Net()
 
-        self.learn_step_counter = 0     # for target updateing
+        self.learn_step_counter = 0                                     # for target updating
         self.memory_counter = 0                                         # for storing memory
         self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))     # initialize memory
         self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)
@@ -100,7 +100,6 @@ for i_episode in range(400):
     ep_r = 0
     while True:
         env.render()
-
         a = dqn.choose_action(s)
 
         # take action
@@ -112,7 +111,6 @@ for i_episode in range(400):
         r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
         r = r1 + r2
 
-        # store experience
         dqn.store_transition(s, a, r, s_)
 
         ep_r += r
@@ -120,10 +118,8 @@ for i_episode in range(400):
             dqn.learn()
             if done:
                 print('Ep: ', i_episode,
-                      '| Ep_r: ', round(ep_r, 2),
+                      '| Ep_r: ', round(ep_r, 2))
-                      )
 
         if done:
             break
-
         s = s_

tutorial-contents/501_why_torch_dynamic_graph.py

@@ -20,7 +20,7 @@ BATCH_SIZE = 64
 TIME_STEP = 5           # rnn time step / image height
 INPUT_SIZE = 1          # rnn input size / image width
 LR = 0.02               # learning rate
-DOWNLOAD_MNIST = False  # set to True if haven't download the data
+DOWNLOAD_MNIST = True   # set to False if have downloaded the data
 
 
 class RNN(nn.Module):

tutorial-contents/504_batch_normalization.py

@@ -147,7 +147,7 @@ for epoch in range(EPOCH):
             loss = loss_func(pred, b_y)
             opt.zero_grad()
             loss.backward()
-            opt.step()    # it will also learn the parameters in Batch Normalization
+            opt.step()    # it will also learns the parameters in Batch Normalization
 
 
 plt.ioff()