update cifar-10 acc 0.93110 (#298)

* Add files via upload

* Add files via upload

* Add files via upload

* Add files via upload

* Add files via upload

* Add files via upload

* Update sentiment-analysis-cnn.md

* Update sentiment-analysis-cnn.md

* Update sentiment-analysis-cnn.md

* Update sentiment-analysis-cnn.md

* Update sentiment-analysis-cnn.md

* Add files via upload

* Update sentiment-analysis-cnn.md

* Update sentiment-analysis-cnn.md

* Update sentiment-analysis-cnn.md

* Update sentiment-analysis.md

* Update sentiment-analysis-cnn.md

* Update PTB

* update ptb

* Update kaggle-gluon-cifar10.md

* Update kaggle-gluon-cifar10.md

* Update rnn-gluon.md

* update acc 0.93110

* Update kaggle-gluon-cifar10.md

* update acc 0.93110

chapter_computer-vision/kaggle-gluon-cifar10.md

@@ -210,7 +210,7 @@ class Residual(nn.HybridBlock):
     
 def resnet18(num_classes):
     net = nn.HybridSequential()
-    net.add(nn.Conv2D(32, kernel_size=3, strides=1, padding=1),
+    net.add(nn.Conv2D(64, kernel_size=3, strides=1, padding=1),
             nn.BatchNorm(), nn.Activation('relu'))
 
     def resnet_block(num_channels, num_residuals, first_block=False):
@@ -222,10 +222,10 @@ def resnet18(num_classes):
                 blk.add(Residual(num_channels))
         return blk 
 
-    net.add(resnet_block(32, 2, first_block=True),
-            resnet_block(64, 2), 
+    net.add(resnet_block(64, 2, first_block=True),
             resnet_block(128, 2), 
-            resnet_block(256, 2)) 
+            resnet_block(256, 2), 
+            resnet_block(512, 2)) 
     net.add(nn.GlobalAvgPool2D(), nn.Dense(num_classes))
     return net