令人费解的语法与theano

Question

我按照教程logistic with theano

进行了操作

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)

但我不知道代码“prediction = p_1＆gt; 0.5”。 当p_1> 0.5，预测=真？要不然 ？

Answer 1

是的，说prediction = p_1 > 0.5等同于：

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