## 点乘和矩阵乘的区别：

#### 1）点乘（即“ * ”） ---- 各个矩阵对应元素做乘法

w的列数只能为 1 与x的列数相等（即n），w的行数与x的行数相等 才能进行乘法运算。

## 1. numpy

#### 1）点乘

1 import numpy as np
2
3 w = np.array([[0.4], [1.2]])
4 x = np.array([range(1,6), range(5,10)])
5
6 print w
7 print x
8 print w*x

#### 2）矩阵乘

1 import numpy as np
2
3 w = np.array([[0.4, 1.2]])
4 x = np.array([range(1,6), range(5,10)])
5
6 print w
7 print x
8 print np.dot(w,x)

## 2. tensorflow

#### 1）点乘

 1 import tensorflow as tf
2
3 w = tf.Variable([[0.4], [1.2]], dtype=tf.float32) # w.shape: [2, 1]
4 x = tf.Variable([range(1,6), range(5,10)], dtype=tf.float32) # x.shape: [2, 5]
5 y = w * x     # 等同于 y = tf.multiply(w, x)   y.shape: [2, 5]
6
7 sess = tf.Session()
8 init = tf.global_variables_initializer()
9 sess.run(init)
10
11 print sess.run(w)
12 print sess.run(x)
13 print sess.run(y)

运行结果如下：

#### 2）矩阵乘

 1 # coding:utf-8
2 import tensorflow as tf
3
4 w = tf.Variable([[0.4, 1.2]], dtype=tf.float32) # w.shape: [1, 2]
5 x = tf.Variable([range(1,6), range(5,10)], dtype=tf.float32) # x.shape: [2, 5]
6 y = tf.matmul(w, x) # y.shape: [1, 5]
7
8 sess = tf.Session()
9 init = tf.global_variables_initializer()
10 sess.run(init)
11
12 print sess.run(w)
13 print sess.run(x)
14 print sess.run(y)

运行结果如下：

posted on 2018-07-19 14:40  刘[小]倩  阅读(...)  评论(...编辑  收藏