2020-10-19 21:08:55

docs/da/122.md

@@ -12,7 +12,7 @@
 
 In [1]:
 
-```
+```py
 %matplotlib inline
 from matplotlib import pyplot as plt
 import numpy as np
@@ -21,7 +21,7 @@ from theano import tensor as T
 
 ```
 
-```
+```py
 Using gpu device 0: GeForce GTX 850M
 
 ```
@@ -32,7 +32,7 @@ Using gpu device 0: GeForce GTX 850M
 
 In [2]:
 
-```
+```py
 a = T.scalar()
 b = T.scalar()
 
@@ -44,7 +44,7 @@ y = a * b
 
 In [3]:
 
-```
+```py
 multiply = theano.function(inputs=[a, b], outputs=y)
 
 ```
@@ -53,13 +53,13 @@ multiply = theano.function(inputs=[a, b], outputs=y)
 
 In [4]:
 
-```
+```py
 print multiply(3, 2) # 6
 print multiply(4, 5) # 20
 
 ```
 
-```
+```py
 6.0
 20.0
 
@@ -71,7 +71,7 @@ print multiply(4, 5) # 20
 
 In [5]:
 
-```
+```py
 train_X = np.linspace(-1, 1, 101)
 train_Y = 2 * train_X + 1 + np.random.randn(train_X.size) * 0.33
 
@@ -81,7 +81,7 @@ train_Y = 2 * train_X + 1 + np.random.randn(train_X.size) * 0.33
 
 In [6]:
 
-```
+```py
 plt.scatter(train_X, train_Y)
 plt.show()
 
@@ -209,7 +209,7 @@ RU5ErkJggg==
 
 In [7]:
 
-```
+```py
 X = T.scalar()
 Y = T.scalar()
 
@@ -219,7 +219,7 @@ Y = T.scalar()
 
 In [8]:
 
-```
+```py
 X.name = 'x'
 Y.name = 'y'
 
@@ -229,7 +229,7 @@ Y.name = 'y'
 
 In [9]:
 
-```
+```py
 def model(X, w, b):
     return X * w + b
 
@@ -241,7 +241,7 @@ def model(X, w, b):
 
 In [10]:
 
-```
+```py
 w = theano.shared(np.asarray(0., dtype=theano.config.floatX))
 w.name = 'w'
 b = theano.shared(np.asarray(0., dtype=theano.config.floatX))
@@ -253,7 +253,7 @@ b.name = 'b'
 
 In [11]:
 
-```
+```py
 Y_bar = model(X, w, b)
 
 theano.pp(Y_bar)
@@ -262,7 +262,7 @@ theano.pp(Y_bar)
 
 Out[11]:
 
-```
+```py
 '((x * HostFromGpu(w)) + HostFromGpu(b))'
 ```
 
@@ -270,7 +270,7 @@ Out[11]:
 
 In [12]:
 
-```
+```py
 cost = T.mean(T.sqr(Y_bar - Y))
 grads = T.grad(cost=cost, wrt=[w, b])
 
@@ -280,7 +280,7 @@ grads = T.grad(cost=cost, wrt=[w, b])
 
 In [13]:
 
-```
+```py
 lr = 0.01
 updates = [[w, w - grads[0] * lr],
            [b, b - grads[1] * lr]]
@@ -293,7 +293,7 @@ updates = [[w, w - grads[0] * lr],
 
 In [14]:
 
-```
+```py
 train_model = theano.function(inputs=[X,Y],
                               outputs=cost,
                               updates=updates,
@@ -307,7 +307,7 @@ train_model = theano.function(inputs=[X,Y],
 
 In [15]:
 
-```
+```py
 for i in xrange(100):
     for x, y in zip(train_X, train_Y):
         train_model(x, y)
@@ -318,7 +318,7 @@ for i in xrange(100):
 
 In [16]:
 
-```
+```py
 print w.get_value()  # 接近 2
 print b.get_value()  # 接近 1
 
@@ -329,7 +329,7 @@ plt.show()
 
 ```
 
-```
+```py
 1.94257426262
 1.00938093662