update read

Author: Syndrome777

1_Getting_Started_入门.md

@@ -14,38 +14,37 @@
 
  [MNIST](http://yann.lecun.com/exdb/mnist)是一个包含60000个训练样例和10000个测试样例的手写数字图像的数据集。在许多论文，包括本教程，都将60000个训练样例分为50000个样例的训练集和10000个样例的验证集（为了超参数，例如学习率、模型尺寸等等）。所有的数字图像都被归一化和中心化为28*28的像素，256位图的灰度图。
  为了方便在Python中的使用，我们对数据集进行了处理。你可以在这里[下载](http://deeplearning.net/data/mnist/mnist.pkl.gz)。这个文件被表示为包含3个lists的tuple：训练集、验证集和测试集。每个lists都是都是两个list的组合，一个list是有numpy的1维array表示的784（28*28）维的0～1（0是黑，1是白）的float值，另一个list是0～9的图像标签。下面的代码显示了如何去加载这个数据集。
-
-	import cPickle, gzip, numpy
-
-	# Load the dataset
-	f = gzip.open('mnist.pkl.gz', 'rb')
-	train_set, valid_set, test_set = cPickle.load(f)
-	f.close()
-
+```Python
+import cPickle, gzip, numpy
+# Load the dataset
+f = gzip.open('mnist.pkl.gz', 'rb')
+train_set, valid_set, test_set = cPickle.load(f)
+f.close()
+```
  当我们使用这个数据集的时候，通常将它分割维几个minibatch。我们建议你将数据集储存为共享变量（shared variables），通过minibatch的索引（一个固定的被告知的batch的尺寸）来存取它们。使用共享变量的原因是为了使用GPU。因为往GPUX显存中复制数据是一个巨大的开销。如果不使用共享变量，GPU代码的运行效率将不会比CPU代码快。如果你将自己的数据定义为共享变量，当共享变量被构建的时候，你就给了Theano在一次请求中将整个数据复制到GPU上的可能。之后，GPU就可以通过共享变量的slice（切片）来存取任何一个minibatch，而不必再从CPU上拷贝数据。同时，因为数据向量（实数）和标签（整数）通常是不同属性的，测试集、验证集和训练集是不同目的的，所以我们建议通过不同的共享变量来储存（这就产生了6个不同的共享变量）。
  由于现在的数据再一个变量里面，一个minibatch被定义为这个变量的一个切片。通过指定它的索引和它的尺寸，可以更加自然的来定义一个minibatch。下面的代码展示了如何去存取数据和如何存取一个minibatch。
-
-    def shared_dataset(data_xy):
-        """ Function that loads the dataset into shared variables
-
-        The reason we store our dataset in shared variables is to allow
-        Theano to copy it into the GPU memory (when code is run on GPU).
-        Since copying data into the GPU is slow, copying a minibatch everytime
-        is needed (the default behaviour if the data is not in a shared
-        variable) would lead to a large decrease in performance.
-        """
-        data_x, data_y = data_xy
-        shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX))
-        shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX))
-        # When storing data on the GPU it has to be stored as floats
-        # therefore we will store the labels as ``floatX`` as well
-        # (``shared_y`` does exactly that). But during our computations
-        # we need them as ints (we use labels as index, and if they are
-        # floats it doesn't make sense) therefore instead of returning
-        # ``shared_y`` we will have to cast it to int. This little hack
-        # lets us get around this issue
-        return shared_x, T.cast(shared_y, 'int32')
-
+```Python
+def shared_dataset(data_xy):
+    """ Function that loads the dataset into shared variables
+
+    The reason we store our dataset in shared variables is to allow
+    Theano to copy it into the GPU memory (when code is run on GPU).
+    Since copying data into the GPU is slow, copying a minibatch everytime
+    is needed (the default behaviour if the data is not in a shared
+    variable) would lead to a large decrease in performance.
+    """
+    data_x, data_y = data_xy
+    shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX))
+    shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX))
+    # When storing data on the GPU it has to be stored as floats
+    # therefore we will store the labels as ``floatX`` as well
+    # (``shared_y`` does exactly that). But during our computations
+    # we need them as ints (we use labels as index, and if they are
+    # floats it doesn't make sense) therefore instead of returning
+    # ``shared_y`` we will have to cast it to int. This little hack
+    # lets us get around this issue
+    return shared_x, T.cast(shared_y, 'int32')
+```
  这个数据以float的形式被存储在GPU上（`dtype`被定义为`theano.confug.floatX`）。然后再将标签转换为int型。
 	如果你再GPU上跑代码，并且数据集太大，可能导致内存崩溃。在这个时候，你就应当把数据存储为共享变量。你可以将数据储存为一个充分小的大块（几个minibatch）在一个共享变量里面，然后在训练的时候使用它。一旦你使用了这个大块，更新它储存的值。这将最小化CPU和GPU的内存交换。
 

README.md

@@ -27,7 +27,6 @@ This is a `Chinese tutorial` which is translated from [DeepLearning 0.1 document
 * Miscellaneous
 
 
-\frac{d}{dx}\sin x=\cos x