update

Author: MorvanZhou

tutorial-contents/401_CNN.py

@@ -85,26 +85,16 @@ def forward(self, x):
 
 # 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

tutorial-contents/403_RNN_regressor.py

@@ -87,8 +87,7 @@ def forward(self, x, h_state):
     # plotting
     plt.plot(steps, y_np.flatten(), 'r-')
     plt.plot(steps, prediction.data.numpy().flatten(), 'b-')
-    plt.draw()
-    plt.pause(0.05)
+    plt.draw(); plt.pause(0.05)
 
 plt.ioff()
 plt.show()

tutorial-contents/404_autoencoder.py

@@ -89,9 +89,7 @@ def forward(self, x):
 # original data (first row) for viewing
 view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)
 for i in range(N_TEST_IMG):
-    a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray')
-    a[0][i].set_xticks(())
-    a[0][i].set_yticks(())
+    a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray'); a[0][i].set_xticks(()); a[0][i].set_yticks(())
 
 for epoch in range(EPOCH):
     for step, (x, y) in enumerate(train_loader):
@@ -114,27 +112,19 @@ def forward(self, x):
             for i in range(N_TEST_IMG):
                 a[1][i].clear()
                 a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')
-                a[1][i].set_xticks(())
-                a[1][i].set_yticks(())
-            plt.draw()
-            plt.pause(0.05)
+                a[1][i].set_xticks(()); a[1][i].set_yticks(())
+            plt.draw(); plt.pause(0.05)
 
 plt.ioff()
 plt.show()
 
 # visualize in 3D plot
 view_data = Variable(train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.)
 encoded_data, _ = autoencoder(view_data)
-fig = plt.figure(2)
-ax = Axes3D(fig)
-X = encoded_data.data[:, 0].numpy()
-Y = encoded_data.data[:, 1].numpy()
-Z = encoded_data.data[:, 2].numpy()
+fig = plt.figure(2); ax = Axes3D(fig)
+X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()
 values = train_data.train_labels[:200].numpy()
 for x, y, z, s in zip(X, Y, Z, values):
-    c = cm.rainbow(int(255*s/9))
-    ax.text(x, y, z, s, backgroundcolor=c)
-ax.set_xlim(X.min(), X.max())
-ax.set_ylim(Y.min(), Y.max())
-ax.set_zlim(Z.min(), Z.max())
+    c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)
+ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())
 plt.show()

tutorial-contents/406_GAN.py

@@ -25,10 +25,10 @@
 PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])
 
 # show our beautiful painting range
-plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
-plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')
-plt.legend(loc='upper right')
-plt.show()
+# plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
+# plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')
+# plt.legend(loc='upper right')
+# plt.show()
 
 
 def artist_works():     # painting from the famous artist (real target)
@@ -81,10 +81,7 @@ def artist_works():     # painting from the famous artist (real target)
         plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')
         plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})
         plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})
-        plt.ylim((0, 3))
-        plt.legend(loc='upper right', fontsize=12)
-        plt.draw()
-        plt.pause(0.01)
+        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)
 
 plt.ioff()
 plt.show()

tutorial-contents/406_conditional_GAN.py

@@ -25,10 +25,10 @@
 PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])
 
 # show our beautiful painting range
-plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
-plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')
-plt.legend(loc='upper right')
-plt.show()
+# plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
+# plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')
+# plt.legend(loc='upper right')
+# plt.show()
 
 
 def artist_works_with_labels():     # painting from the famous artist (real target)
@@ -91,10 +91,7 @@ def artist_works_with_labels():     # painting from the famous artist (real targ
         plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})
         plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})
         plt.text(-.5, 1.7, 'Class = %i' % int(labels.data[0, 0]), fontdict={'size': 15})
-        plt.ylim((0, 3))
-        plt.legend(loc='upper right', fontsize=12)
-        plt.draw()
-        plt.pause(0.1)
+        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.1)
 
 plt.ioff()
 plt.show()
@@ -107,6 +104,4 @@ def artist_works_with_labels():     # painting from the famous artist (real targ
 plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='G painting for upper class',)
 plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound (class 1)')
 plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound (class 1)')
-plt.ylim((0, 3))
-plt.legend(loc='upper right', fontsize=12)
-plt.show()
+plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.show()

tutorial-contents/503_dropout.py

@@ -87,9 +87,7 @@
         plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)')
         plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data[0], fontdict={'size': 20, 'color':  'red'})
         plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data[0], fontdict={'size': 20, 'color': 'blue'})
-        plt.legend(loc='upper left')
-        plt.ylim((-2.5, 2.5))
-        plt.pause(0.1)
+        plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5));plt.pause(0.1)
 
         # change back to train mode
         net_overfitting.train()

tutorial-contents/504_batch_normalization.py

@@ -99,30 +99,17 @@ def forward(self, x):
 f, axs = plt.subplots(4, N_HIDDEN+1, figsize=(10, 5))
 plt.ion()   # something about plotting
 plt.show()
-
 def plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn):
     for i, (ax_pa, ax_pa_bn, ax,  ax_bn) in enumerate(zip(axs[0, :], axs[1, :], axs[2, :], axs[3, :])):
         [a.clear() for a in [ax_pa, ax_pa_bn, ax, ax_bn]]
-        if i == 0:
-            p_range = (-7, 10)
-            the_range = (-7, 10)
-        else:
-            p_range = (-4, 4)
-            the_range = (-1, 1)
+        if i == 0: p_range = (-7, 10);the_range = (-7, 10)
+        else:p_range = (-4, 4);the_range = (-1, 1)
         ax_pa.set_title('L' + str(i))
-        ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5)
-        ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)
-        ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359')
-        ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')
-        for a in [ax_pa, ax, ax_pa_bn, ax_bn]:
-            a.set_yticks(())
-            a.set_xticks(())
-        ax_pa_bn.set_xticks(p_range)
-        ax_bn.set_xticks(the_range)
-        axs[0, 0].set_ylabel('PreAct')
-        axs[1, 0].set_ylabel('BN PreAct')
-        axs[2, 0].set_ylabel('Act')
-        axs[3, 0].set_ylabel('BN Act')
+        ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5);ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)
+        ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359');ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')
+        for a in [ax_pa, ax, ax_pa_bn, ax_bn]: a.set_yticks(());a.set_xticks(())
+        ax_pa_bn.set_xticks(p_range);ax_bn.set_xticks(the_range)
+        axs[0, 0].set_ylabel('PreAct');axs[1, 0].set_ylabel('BN PreAct');axs[2, 0].set_ylabel('Act');axs[3, 0].set_ylabel('BN Act')
     plt.pause(0.01)
 
 # training
@@ -155,10 +142,7 @@ def plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn):
 plt.figure(2)
 plt.plot(losses[0], c='#FF9359', lw=3, label='Original')
 plt.plot(losses[1], c='#74BCFF', lw=3, label='Batch Normalization')
-plt.xlabel('step')
-plt.ylabel('test loss')
-plt.ylim((0, 2000))
-plt.legend(loc='best')
+plt.xlabel('step');plt.ylabel('test loss');plt.ylim((0, 2000));plt.legend(loc='best')
 
 # evaluation
 # set net to eval mode to freeze the parameters in batch normalization layers