import tensorflow as tf
from numpy.random import RandomState
#### 1. 定義神經網路的引數，輸入和輸出節點。
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')



#### 2. 定義前向傳播過程，損失函式及反向傳播演算法。"
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)



####  3. 生成模擬資料集。
rdm = RandomState(1)
X = rdm.rand(128,2)
Y = [[int(x1+x2 < 1
 
)] for (x1, x2) in X]




#### 4. 建立一個會話來執行TensorFlow程式。
with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print ("w1:", sess.run(w1))
    print ("w2:", sess.run(w2))



STEPS = 5000
for i in range(STEPS):
    start = (i*batch_size) % 128
    end = (i*batch_size) % 128
 
 + batch_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))

    # 輸出訓練後的引數取值。
    print("\n")
    print("w1:", sess.run(w1))
    print("w2:", sess.run(w2))