逻辑回归算法的编写

X = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

W = tf.Variable(tf.zeros([feature_num, 1]))
b = tf.Variable([-.3])

db = tf.matmul(X, tf.reshape(W, [-1, 1])) + b
hyp = tf.sigmoid(db)

cost0 = y * tf.log(hyp)
cost1 = (1 - y) * tf.log(1 - hyp)
cost = (cost0 + cost1) / -sample_num

loss = tf.reduce_sum(cost)

optimizer = tf.train.GradientDescentOptimizer(0.1)
train = optimizer.minimize(loss)

转载于:https://www.cnblogs.com/lgx-fighting/p/9397363.html