神經網路解決二分類問題

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)
#定義損失函式和反向傳播的演算法
cross_entropy = -tf.reduce_mean(
y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
train_step = tf.train.adadeltaoptimizer(0.001).minimize(cross_entropy)
#通過隨機數生成乙個模擬資料集
rdm = randomstate(1)
dataset_size = 128
x = rdm.rand(dataset_size, 2)
y = [[int(x1+x2 < 1)] for (x1, x2) in x]
with tf.session() as sess:  
init_op = tf.global_variables_initializer()  
sess.run(init_op)  
print(sess.run(w1))   
print(sess.run(w2)) 
#設定訓練的輪數
steps = 5000
for i in range(steps):
#每次選取batch_size個樣本進行訓練
start = (i * batch_size) % dataset_size
end = min(start+batch_size, dataset_size)
#通過選取的樣本訓練神經網路並更新引數
sess.run(train_step, feed_dict=)
if i % 1000 == 0:
total_cross_entropy = sess.run(cross_entropy, feed_dict=)
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))

[[-0.8113182   1.4845988   0.06532937]

[-2.4427042   0.0992484   0.5912243 ]]
[[-0.8113182 ]
[ 1.4845988 ]
[ 0.06532937]]
after 0 training step(s), cross entropy on all data is 0.0677411
after 1000 training step(s), cross entropy on all data is 0.0676762
after 2000 training step(s), cross entropy on all data is 0.0676077
after 3000 training step(s), cross entropy on all data is 0.0675391
after 4000 training step(s), cross entropy on all data is 0.0674705
[[-0.812737    1.48603     0.06671807]
[-2.444031    0.10049716  0.5924467 ]]
[[-0.812682  ]
[ 1.4861072 ]
[ 0.06662364]]

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