用神经网络解决二分类问题_神经网络paddle预测 二分类(0,1)问题

#https://blog.csdn.net/weixin_34365635/article/details/86942414
 
import tensorflow as tf
from numpy.random import RandomState
 
batch_size = 8
w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
x = tf.placeholder(tf.float32,shape=(None,2),name='x-input')
y_= tf.placeholder(tf.float32,shape=(None,1),name='y-input')
a=tf.matmul(x,w1)
y=tf.matmul(a,w2)
cross_entropy = -tf.reduce_mean(
    y_*tf.log(tf.clip_by_value(y,1e-10,1.0))
)
 
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
 
rdm = RandomState(1)
dataset_size = 128
X=rdm.rand(dataset_size,2)
# for (x1,x2) in X:
#     print(x1+x2)
#     print(int(x1+x2 < 1))
Y=[[int(x1+x2 < 1)] for (x1,x2) in X]
 
with tf.Session() as sess:
    init_op = tf.initialize_all_variables()
    sess.run(init_op)
    print(sess.run(w1))
    print(sess.run(w2))
sess.close()
 
# sess = tf.Session()
# init_op = tf.initialize_all_variables()
# sess.run(init_op)
# STEPS = 500
# for i in range(STEPS):
#     start = (i * batch_size) % dataset_size
#     end = min(start + batch_size, dataset_size)
#     sess.run(train_step,feed_dict={x: X[start:end], y_: Y[start:end]})
#     if i % 1000 == 0:
#         total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})
#         print("After %d training step(s),cross entropy on all data is %g" % (i, total_cross_entropy))
#     print(sess.run(w1))
#     print(sess.run(w2))
 

结果：

[[-0.8113182   1.4845988   0.06532937]
 [-2.4427042   0.0992484   0.5912243 ]]
[[-0.8113182 ]
 [ 1.4845988 ]
 [ 0.06532937]]