rnn lint

chapter_recurrent-neural-networks/gru.md

@@ -85,14 +85,14 @@ ctx = gb.try_gpu()
 def get_params():
     def _one(shape):
         return nd.random.normal(scale=0.01, shape=shape, ctx=ctx)
-    
+
     def _three():
         return (_one((num_inputs, num_hiddens)),
                 _one((num_hiddens, num_hiddens)),
-                nd.zeros(num_hiddens, ctx=ctx))  
- 
-    W_xz, W_hz, b_z = _three()  # 更新门参数。    
-    W_xr, W_hr, b_r = _three()  # 重置门参数。    
+                nd.zeros(num_hiddens, ctx=ctx))
+
+    W_xz, W_hz, b_z = _three()  # 更新门参数。
+    W_xr, W_hr, b_r = _three()  # 重置门参数。
     W_xh, W_hh, b_h = _three()  # 候选隐藏状态参数。
     # 输出层参数。
     W_hq = _one((num_hiddens, num_outputs))
@@ -120,7 +120,7 @@ def gru(inputs, state, params):
     W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q = params
     H, = state
     outputs = []
-    for X in inputs:        
+    for X in inputs:
         Z = nd.sigmoid(nd.dot(X, W_xz) + nd.dot(H, W_hz) + b_z)
         R = nd.sigmoid(nd.dot(X, W_xr) + nd.dot(H, W_hr) + b_r)
         H_tilda = nd.tanh(nd.dot(X, W_xh) + R * nd.dot(H, W_hh) + b_h)

chapter_recurrent-neural-networks/lang-model-dataset.md

@@ -63,8 +63,11 @@ def data_iter_random(corpus_indices, batch_size, num_steps, ctx=None):
     epoch_size = num_examples // batch_size
     example_indices = list(range(num_examples))
     random.shuffle(example_indices)
+
     # 返回从 pos 开始的长为 num_steps 的序列。
-    _data = lambda pos: corpus_indices[pos: pos + num_steps]
+    def _data(pos):
+        return corpus_indices[pos: pos + num_steps]
+
     for i in range(epoch_size):
         # 每次读取 batch_size 个随机样本。
         i = i * batch_size

chapter_recurrent-neural-networks/lstm.md

@@ -91,15 +91,15 @@ from mxnet.gluon import rnn
 num_inputs, num_hiddens, num_outputs = vocab_size, 256, vocab_size
 ctx = gb.try_gpu()
 
-def get_params():    
+def get_params():
     def _one(shape):
         return nd.random.normal(scale=0.01, shape=shape, ctx=ctx)
-    
+
     def _three():
         return (_one((num_inputs, num_hiddens)),
                 _one((num_hiddens, num_hiddens)),
-                nd.zeros(num_hiddens, ctx=ctx)) 
-       
+                nd.zeros(num_hiddens, ctx=ctx))
+
     W_xi, W_hi, b_i = _three()  # 输入门参数。
     W_xf, W_hf, b_f = _three()  # 遗忘门参数。
     W_xo, W_ho, b_o = _three()  # 输出门参数。
@@ -121,7 +121,7 @@ def get_params():
 
 ```{.python .input  n=3}
 def init_lstm_state(batch_size, num_hiddens, ctx):
-    return (nd.zeros(shape=(batch_size, num_hiddens), ctx=ctx), 
+    return (nd.zeros(shape=(batch_size, num_hiddens), ctx=ctx),
             nd.zeros(shape=(batch_size, num_hiddens), ctx=ctx))
 ```
 
@@ -133,7 +133,7 @@ def lstm(inputs, state, params):
      W_hq, b_q] = params
     (H, C) = state
     outputs = []
-    for X in inputs:        
+    for X in inputs:
         I = nd.sigmoid(nd.dot(X, W_xi) + nd.dot(H, W_hi) + b_i)
         F = nd.sigmoid(nd.dot(X, W_xf) + nd.dot(H, W_hf) + b_f)
         O = nd.sigmoid(nd.dot(X, W_xo) + nd.dot(H, W_ho) + b_o)
@@ -172,7 +172,7 @@ gb.train_and_predict_rnn(lstm, get_params, init_lstm_state, num_hiddens,
 lstm_layer = rnn.LSTM(num_hiddens)
 model = gb.RNNModel(lstm_layer, vocab_size)
 gb.train_and_predict_rnn_gluon(model, num_hiddens, vocab_size, ctx,
-                               corpus_indices, idx_to_char, char_to_idx, 
+                               corpus_indices, idx_to_char, char_to_idx,
                                num_epochs, num_steps, lr, clipping_theta,
                                batch_size, pred_period, pred_len, prefixes)
 ```

chapter_recurrent-neural-networks/rnn-gluon.md

@@ -101,9 +101,9 @@ predict_rnn_gluon('分开', 10, model, vocab_size, ctx, idx_to_char, char_to_idx
 
 ```{.python .input  n=18}
 # 本函数已保存在 gluonbook 包中方便以后使用。
-def train_and_predict_rnn_gluon(model, num_hiddens, vocab_size, ctx, 
-                                corpus_indices, idx_to_char, char_to_idx, 
-                                num_epochs, num_steps, lr, clipping_theta, 
+def train_and_predict_rnn_gluon(model, num_hiddens, vocab_size, ctx,
+                                corpus_indices, idx_to_char, char_to_idx,
+                                num_epochs, num_steps, lr, clipping_theta,
                                 batch_size, pred_period, pred_len, prefixes):
     loss = gloss.SoftmaxCrossEntropyLoss()
     model.initialize(ctx=ctx, force_reinit=True, init=init.Normal(0.01))
@@ -134,7 +134,7 @@ def train_and_predict_rnn_gluon(model, num_hiddens, vocab_size, ctx,
                 epoch + 1, math.exp(loss_sum / (t + 1)), time.time() - start))
             for prefix in prefixes:
                 print(' -', predict_rnn_gluon(
-                    prefix, pred_len, model, vocab_size, 
+                    prefix, pred_len, model, vocab_size,
                     ctx, idx_to_char, char_to_idx))
 ```
 
@@ -143,9 +143,9 @@ def train_and_predict_rnn_gluon(model, num_hiddens, vocab_size, ctx,
 ```{.python .input  n=19}
 num_epochs, batch_size, lr, clipping_theta = 200, 32, 1e2, 1e-2
 pred_period, pred_len, prefixes = 50, 50, ['分开', '不分开']
-train_and_predict_rnn_gluon(model, num_hiddens, vocab_size, ctx, 
-                            corpus_indices, idx_to_char, char_to_idx, 
-                            num_epochs, num_steps, lr, clipping_theta, 
+train_and_predict_rnn_gluon(model, num_hiddens, vocab_size, ctx,
+                            corpus_indices, idx_to_char, char_to_idx,
+                            num_epochs, num_steps, lr, clipping_theta,
                             batch_size, pred_period, pred_len, prefixes)
 ```
 

chapter_recurrent-neural-networks/rnn-scratch.md

@@ -102,7 +102,7 @@ len(outputs), outputs[0].shape, state_new[0].shape
 def predict_rnn(prefix, num_chars, rnn, params, init_rnn_state,
                 num_hiddens, vocab_size, ctx, idx_to_char, char_to_idx):
     state = init_rnn_state(1, num_hiddens, ctx)
-    output = [char_to_idx[prefix[0]]]    
+    output = [char_to_idx[prefix[0]]]
     for t in range(num_chars + len(prefix) - 1):
         # 将上一时间步的输出作为当前时间步的输入。
         X = to_onehot(nd.array([output[-1]], ctx=ctx), vocab_size)
@@ -173,7 +173,7 @@ def train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens,
     if is_random_iter:
         data_iter_fn = gb.data_iter_random
     else:
-        data_iter_fn = gb.data_iter_consecutive     
+        data_iter_fn = gb.data_iter_consecutive
     params = get_params()
     loss = gloss.SoftmaxCrossEntropyLoss()