po

_posts/2018-03-29-Numpy_axis_intuiation.markdown

@@ -0,0 +1,35 @@
+---
+layout: post
+title:  "Numpy axis intuiation"
+date:  2018-03-29 21:25:50 +0800
+categories: math numpy
+---
+
+
+Axis play a key in Numpy array operations,
+it indicate the dimension of an array(or a tensor), for example, if an array have shape (2,4,6)
+first dimension is axis=0 coresponding to shape 2, second dimension is axis=1 coresponding to shape 4, etc, and for the convience, Numpy also can use axis=-1 to indicate the last dimension.
+
+I made an animation for intuiation.
+
+
+![]({{ site.url }}/assets/article_images/2018-03-29-Numpy_axis_intuiation/s1.gif){:height="75%" width="75%"}
+
+
+Functions in Numpy to operate array can be classified by how to change dimensions.
+1. keep dimensions
+2. collapse dimensions
+3. expand dimensions
+4. switch axis
+
+Here is a animation shows how to operate array along different axis when keep/collapse dimensions.
+
+![s2]({{ site.url }}/assets/article_images/2018-03-29-Numpy_axis_intuiation/s2.gif){:height="75%" width="75%"}
+
+For expand and sitch axis it is almost the same idea. Numpy doc example expain this very well.
+
+
+|||
+|:----------|:----------|
+| [`expand_dims`](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.expand_dims.html#numpy.expand_dims "numpy.expand_dims")(a, axis) | Expand the shape of an array. |
+| [`swapaxes`](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.swapaxes.html#numpy.swapaxes "numpy.swapaxes")(a, axis1, axis2) | Interchange two axes of an array. |

_posts/2018-03-29-Numpy_axis_直观印象.markdown

@@ -0,0 +1,31 @@
+---
+layout: post
+title:  "Numpy axis 直观印象"
+date:  2018-03-29 21:25:50 +0800
+categories: math numpy
+---
+
+Axis在Numpy库的array操作中起了非常关键的作用, 它用于指示一个array(或者说tersor)的维度.
+
+例如: 一个array的shape是(2,4,6), 那么第一个维度也就是axis=0对应的是shape 2, 第二个维度axis=1对应的是shape 4, 以此类推, 另外为了方便, axis=-1是值得倒数第一个维度.
+
+这里有个动画可以直观感受下axis
+
+![]({{ site.url }}/assets/article_images/2018-03-29-Numpy_axis_intuiation/s1.gif){:height="75%" width="75%"}
+
+Numpy中的函数可以根据对dimension的操作分为下面几类
+1. dimension保持不变
+2. dimension减少
+3. dimension增加
+4. axis交换
+
+下面的动画展示了Numpy是如何沿着axis进行保持和减少dimension操作的
+
+![s2]({{ site.url }}/assets/article_images/2018-03-29-Numpy_axis_intuiation/s2.gif){:height="75%" width="75%"}
+
+对于dimension增加和axis交换, 可以看下Numpy doc的例子, 动画太难做了┑(￣Д ￣)┍
+
+|||
+|:----------|:----------|
+| [`expand_dims`](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.expand_dims.html#numpy.expand_dims "numpy.expand_dims")(a, axis) | Expand the shape of an array. |
+| [`swapaxes`](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.swapaxes.html#numpy.swapaxes "numpy.swapaxes")(a, axis1, axis2) | Interchange two axes of an array. |