update data_iter

chapter_deep-learning-basics/linear-regression-scratch.md

@@ -59,7 +59,7 @@ plt.show()
 
 ## 读取数据
 
-在训练模型的时候，我们需要遍历数据集并不断读取小批量数据样本。这里我们定义一个函数：它每次返回批量大小个随机样本的特征和标签。
+在训练模型的时候，我们需要遍历数据集并不断读取小批量数据样本。这里我们定义一个函数：它每次返回`batch_size`（批量大小）个随机样本的特征和标签。
 
 ```{.python .input  n=5}
 batch_size = 10
@@ -74,7 +74,7 @@ def data_iter(batch_size, features, labels):
         yield features.take(j), labels.take(j)
 ```
 
-让我们读取第一个小批量数据样本并打印。每个批量的特征形状为`（10, 2）`，分别对应批量大小和输入个数；标签形状为批量大小。
+让我们读取第一个小批量数据样本并打印。每个批量的特征形状为（10， 2），分别对应批量大小和输入个数；标签形状为批量大小。
 
 ```{.python .input  n=6}
 for X, y in data_iter(batch_size, features, labels):
@@ -86,7 +86,7 @@ for X, y in data_iter(batch_size, features, labels):
 
 ## 初始化模型参数
 
-我们将权重随机化成均值0方差为0.01的正态随机数，偏差则初始化成0。
+我们将权重初始化成均值0标准差为0.01的正态随机数，偏差则初始化成0。
 
 ```{.python .input  n=7}
 w = nd.random.normal(scale=0.01, shape=(num_inputs, 1))
@@ -123,7 +123,7 @@ def squared_loss(y_hat, y):
 
 ## 定义优化算法
 
-以下的`sgd`函数实现了上一节中介绍的小批量随机梯度下降算法中的模型更新。
+以下的`sgd`函数实现了上一节中介绍的小批量随机梯度下降算法。它通过不断更新模型参数来优化损失函数。
 
 ```{.python .input  n=11}
 def sgd(params, lr, batch_size):

chapter_deep-learning-basics/reg-scratch.md

@@ -96,7 +96,7 @@ def fit_and_plot(lambd):
     train_ls = []
     test_ls = []
     for _ in range(num_epochs):        
-        for X, y in gb.data_iter(batch_size, n_train, features, labels):
+        for X, y in gb.data_iter(batch_size, features, labels):
             with autograd.record():
                 # 添加了 L2 范数惩罚项。
                 l = loss(net(X, w, b), y) + lambd * l2_penalty(w)

chapter_optimization/adadelta-scratch.md

@@ -93,7 +93,7 @@ def optimize(batch_size, rho, num_epochs, log_interval):
     ls = [loss(net(features, w, b), labels).mean().asnumpy()]
     for epoch in range(1, num_epochs + 1):
         for batch_i, (X, y) in enumerate(
-            gb.data_iter(batch_size, num_examples, features, labels)):
+            gb.data_iter(batch_size, features, labels)):
             with autograd.record():
                 l = loss(net(X, w, b), y)
             l.backward()

chapter_optimization/adagrad-scratch.md

@@ -100,7 +100,7 @@ def optimize(batch_size, lr, num_epochs, log_interval):
     ls = [loss(net(features, w, b), labels).mean().asnumpy()]
     for epoch in range(1, num_epochs + 1):
         for batch_i, (X, y) in enumerate(
-            gb.data_iter(batch_size, num_examples, features, labels)):
+            gb.data_iter(batch_size, features, labels)):
             with autograd.record():
                 l = loss(net(X, w, b), y)
             l.backward()

chapter_optimization/adam-scratch.md

@@ -109,7 +109,7 @@ def optimize(batch_size, lr, num_epochs, log_interval):
     t = 0
     for epoch in range(1, num_epochs + 1):
         for batch_i, (X, y) in enumerate(
-            gb.data_iter(batch_size, num_examples, features, labels)):
+            gb.data_iter(batch_size, features, labels)):
             with autograd.record():
                 l = loss(net(X, w, b), y)
             l.backward()

chapter_optimization/gd-sgd-scratch.md

@@ -172,7 +172,7 @@ def optimize(batch_size, lr, num_epochs, log_interval, decay_epoch):
         if decay_epoch and epoch > decay_epoch:
             lr *= 0.1
         for batch_i, (X, y) in enumerate(
-            gb.data_iter(batch_size, num_examples, features, labels)):
+            gb.data_iter(batch_size, features, labels)):
             with autograd.record():
                 l = loss(net(X, w, b), y)
             # 先对 l 中元素求和，得到小批量损失之和，然后求参数的梯度。

chapter_optimization/momentum-scratch.md

@@ -135,7 +135,7 @@ def optimize(batch_size, lr, mom, num_epochs, log_interval):
         if epoch > 2:
             lr *= 0.1
         for batch_i, (X, y) in enumerate(
-            gb.data_iter(batch_size, num_examples, features, labels)):
+            gb.data_iter(batch_size, features, labels)):
             with autograd.record():
                 l = loss(net(X, w, b), y)
             l.backward()

chapter_optimization/rmsprop-scratch.md

@@ -89,7 +89,7 @@ def optimize(batch_size, lr, gamma, num_epochs, log_interval):
     ls = [loss(net(features, w, b), labels).mean().asnumpy()]
     for epoch in range(1, num_epochs + 1):
         for batch_i, (X, y) in enumerate(
-            gb.data_iter(batch_size, num_examples, features, labels)):
+            gb.data_iter(batch_size, features, labels)):
             with autograd.record():
                 l = loss(net(X, w, b), y)
             l.backward()

gluonbook/utils.py

@@ -377,8 +377,9 @@ def train_and_predict_rnn(rnn, is_random_iter, num_epochs, num_steps,
                     ctx, idx_to_char, char_to_idx, get_inputs, is_lstm))
 
 
-def data_iter(batch_size, num_examples, features, labels):
+def data_iter(batch_size, features, labels):
     """Iterate through a data set."""
+	num_examples = len(features)
     indices = list(range(num_examples))
     random.shuffle(indices)
     for i in range(0, num_examples, batch_size):