# coding:utf-8
# 随机产生32组生产出的零件的体积和重量，训练3000轮，没500轮输出一次损失函数
# 导入tensorflow模块，生成随机数据集
import tensorflow as tf
# 导入numpy模块，numpy是python的科学计算模块
import numpy as np
# 一次喂入神经网络多少组数据，数值不可以过大
BATCH_SIZE = 8
seed = 23455

# 基于seed产生随机数
rng = np.random.RandomState(seed)
# 随机数返回32行2列的矩阵 表示32组 体积和重量 作为输入数据集
X = rng.rand(32, 2)
# 从32行2列的矩阵中 去除一行判断如果和小于1 给Y赋值1 如果和不小于1 给Y赋值0
# 作为输入数据集的标签（正确答案）
Y = [[int(x0 + x1 <1)] for (x0, x1) in X]
print("X:\n", X)
print("Y:\n", Y)

# 定义神经网络的输入，参数和输出，定义前向传播过程
x = tf.placeholder(tf.float32, shape=(None, 2))
# y_即为0或1
y_ = tf.placeholder(tf.float32, shape=(None, 1))

# w1为2行3列
w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))

# matmul矩阵相乘
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

# 定义损失函数及反向传播方法
loss = tf.reduce_mean(tf.square(y-y_))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
# 其他优化方法
# train_step = tf.train.GMomentumOptimizer(0.001, 0.9).minimize(loss)
# train_step = tf.train.AdamOptimizer(0.001).minimize(loss)

# 生成会话，训练STEPS轮
with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    # 输出目前（未经训练）的参数取值
    print("w1:\n", sess.run(w1))
    print("w2:\n", sess.run(w2))
    print("\n")

    # 训练模型3000轮
    STEPS = 3000
    for i in range(STEPS):
        start = (i*BATCH_SIZE) % 32
        end = start + BATCH_SIZE
        sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})
        # 没500轮打印一次loss值
        if i % 500 == 0:
            total_loss = sess.run(loss, feed_dict={x: X, y_: Y})
            print("After %d training step(s), loss on all data is %g" %(i, total_loss))

    # 输出训练后的参数取值
    print("\n")
    print("w1:\n", sess.run(w1))
    print("w2:\n", sess.run(w2))