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Update 4.2机器学习（AI）快速入门（quick start）.md

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2023-04-20 00:00:16 +08:00
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4.人工智能/4.2机器学习（AI）快速入门（quick start）.md
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@@ -84,19 +84,19 @@
```python
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
price = 0<em> # In my area, the average house costs $200 per sqft</em>
price = 0 # In my area, the average house costs $200 per sqft
price_per_sqft = 200 if neighborhood == "hipsterton":
<em> # but some areas cost a bit more</em>
# but some areas cost a bit more
price_per_sqft = 400 elif neighborhood == "skid row":
<em> # and some areas cost less</em>
price_per_sqft = 100 <em># start with a base price estimate based on how big the place is</em>
price = price_per_sqft * sqft <em># now adjust our estimate based on the number of bedrooms</em>
# and some areas cost less
price_per_sqft = 100 # start with a base price estimate based on how big the place is
price = price_per_sqft * sqft # now adjust our estimate based on the number of bedrooms
if num_of_bedrooms == 0:
<em># Studio apartments are cheap</em>
# Studio apartments are cheap
price = price — 20000
else:
<em> # places with more bedrooms are usually</em>
<em> # more valuable</em>
# places with more bedrooms are usually
# more valuable
price = price + (num_of_bedrooms * 1000) return price
```
@@ -212,9 +212,9 @@ def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
```python
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
price = 0# a little pinch of this
price += num_of_bedrooms * <strong>0.123</strong># and a big pinch of that
price += sqft * <strong>0.41</strong># maybe a handful of this
price += neighborhood * <strong>0.57</strong>
price += num_of_bedrooms * 0.123# and a big pinch of that
price += sqft * 0.41# maybe a handful of this
price += neighborhood * 0.57
return price
```
@@ -328,10 +328,10 @@ print('y_pred=',y_test.data)
请注意，我们的神经网络现在有了两个输出（而不仅仅是一个房子的价格）。第一个输出会预测图片是「8」的概率，而第二个则输出不是「8」的概率。概括地说，我们就可以依靠多种不同的输出，利用神经网络把要识别的物品进行分组。
```python
<strong>model = Sequential([Dense(32, input_shape=(784,)), </strong>
<strong> Activation('relu'),Dense(10),Activation('softmax')])</strong># 你也可以通过 .add() 方法简单地添加层：<strong> model = Sequential() </strong>
<strong> model.add(Dense(32, input_dim=784)) </strong>
<strong> model.add(Activation('relu'))# 激活函数，你可以理解为加上这个东西可以让他效果更好</strong>
model = Sequential([Dense(32, input_shape=(784,)),
Activation('relu'),Dense(10),Activation('softmax')])# 你也可以通过 .add() 方法简单地添加层： model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))# 激活函数，你可以理解为加上这个东西可以让他效果更好
```
虽然我们的神经网络要比上次大得多（这次有 324 个输入，上次只有 3 个！），但是现在的计算机一眨眼的功夫就能够对这几百个节点进行运算。当然，你的手机也可以做到。