與theano

Question

令人費解的語法我跟着教程logistic with theano

import numpy 
import theano 
import theano.tensor as T 
rng = numpy.random 

N = 400         # training sample size 
feats = 784        # number of input variables 



# initialize the bias term 
b = theano.shared(0., name="b") 

print("Initial model:") 
print(w.get_value()) 
print(b.get_value()) 

# Construct Theano expression graph 
p_1 = 1/(1 + T.exp(-T.dot(x, w) - b)) # Probability that target = 1 
prediction = p_1 > 0.5     # The prediction thresholded 
xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function 
cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize 
gw, gb = T.grad(cost, [w, b])    # Compute the gradient of the cost 
             # w.r.t weight vector w and 
             # bias term b 
             # (we shall return to this in a 
             # following section of this tutorial) 

，但我不知道代碼 「預測= P_1> 0.5」。當p_1> 0.5時， ，prediction = True？要不然 ？

Answer 1

是的，說prediction = p_1 > 0.5相當於：

if p_1 > 0.5: 
    prediction = True 
else: 
    prediction = False