revise parameters (d2l-ai#334)

* GAN PiC * Revise para
caloskao · Aug 21, 2018 · 84bf649 · 84bf649
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diff --git a/chapter_natural-language-processing/sentiment-analysis-cnn.md b/chapter_natural-language-processing/sentiment-analysis-cnn.md
@@ -146,7 +146,7 @@ glove_embedding = text.embedding.create(
 ```{.python .input  n=10}
 class TextCNN(nn.Block):
     def __init__(self, vocab, embedding_size, ngram_kernel_sizes,
-                 nums_channels, **kwargs):
+                 nums_channels, num_outputs, **kwargs):
         super(TextCNN, self).__init__(**kwargs)
         self.ngram_kernel_sizes = ngram_kernel_sizes
         self.embedding_static = nn.Embedding(len(vocab), embedding_size)
@@ -215,7 +215,7 @@ ctx = gb.try_all_gpus()
 接下来，我们用预训练的100维GloVe词向量初始化`embedding_static`和`embedding_non_static`。其中只有`embedding_static`在训练中不更新模型参数。
 
 ```{.python .input}
-net = TextCNN(vocab, embed_size, ngram_kernel_sizes, nums_channels)
+net = TextCNN(vocab, embed_size, ngram_kernel_sizes, nums_channels, num_outputs)
 net.initialize(init.Xavier(), ctx=ctx)
 # embedding_static 和 embedding_non_static 均使用预训练的词向量。
 net.embedding_static.weight.set_data(glove_embedding.idx_to_vec)

diff --git a/chapter_natural-language-processing/sentiment-analysis.md b/chapter_natural-language-processing/sentiment-analysis.md
@@ -169,7 +169,7 @@ glove_embedding = text.embedding.create(
 ```{.python .input  n=10}
 class BiRNN(nn.Block):
     def __init__(self, vocab, embed_size, num_hiddens, num_layers,
-                 bidirectional, **kwargs):
+                 bidirectional, num_outputs, **kwargs):
         super(BiRNN, self).__init__(**kwargs)
         self.embedding = nn.Embedding(len(vocab), embed_size)
         self.encoder = rnn.LSTM(num_hiddens, num_layers=num_layers,
@@ -199,7 +199,7 @@ num_layers = 2
 bidirectional = True
 ctx = gb.try_all_gpus()
 
-net = BiRNN(vocab, embed_size, num_hiddens, num_layers, bidirectional)
+net = BiRNN(vocab, embed_size, num_hiddens, num_layers, bidirectional, num_outputs)
 net.initialize(init.Xavier(), ctx=ctx)
 # 设置 embedding 层的 weight 为预训练的词向量。
 net.embedding.weight.set_data(glove_embedding.idx_to_vec)

diff --git a/contrib/img/DCGAN.PNG b/contrib/img/DCGAN.PNG