Merge pull request d2l-ai#150 from astonzhang/rnn

update utils

utils.py

@@ -239,16 +239,16 @@ def data_iter_consecutive(corpus_indices, batch_size, num_steps, ctx=None):
         yield data, label
 
 
-def grad_clipping(params, theta, ctx):
+def grad_clipping(params, clipping_norm, ctx):
     """Gradient clipping."""
-    if theta is not None:
+    if clipping_norm is not None:
         norm = nd.array([0.0], ctx)
         for p in params:
             norm += nd.sum(p.grad ** 2)
         norm = nd.sqrt(norm).asscalar()
-        if norm > theta:
+        if norm > clipping_norm:
             for p in params:
-                p.grad[:] *= theta / norm
+                p.grad[:] *= clipping_norm / norm
 
 
 def predict_rnn(rnn, prefix, num_chars, params, hidden_dim, ctx, idx_to_char,
@@ -274,7 +274,7 @@ def predict_rnn(rnn, prefix, num_chars, params, hidden_dim, ctx, idx_to_char,
 
 
 def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim, 
-                          learning_rate, clipping_theta, batch_size,
+                          learning_rate, clipping_norm, batch_size,
                           pred_period, pred_len, seqs, get_params, get_inputs,
                           ctx, corpus_indices, idx_to_char, char_to_idx,
                           is_lstm=False):
@@ -321,7 +321,7 @@ def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim,
                 loss = softmax_cross_entropy(outputs, label)
             loss.backward()
 
-            grad_clipping(params, clipping_theta, ctx)
+            grad_clipping(params, clipping_norm, ctx)
             SGD(params, learning_rate)
 
             train_loss += nd.sum(loss).asscalar()