import tensorflow as tf
a=tf.constant([1.0,2.0])
b=tf.constant([3.0,4.0])
result=a+b
print(result)
#Tensor("add:0", shape=(2,), dtype=float32)

import tensorflow as tf
a=tf.constant([[1.0,2.0]]) #定義一個2 階張量等於[[1.0,2.0]]
b=tf.constant([[3.0],[4.0]])#定義一個2 階張量等於[[3.0],[4.0]]
result=tf.matmul(a,b)#實現xw 矩陣乘法
with tf.Session() as sess:
    print(sess.run(result))
#[[ 11.]]
 

#前向傳播
import tensorflow as tf
#用placeholder實現輸入定義（sess.run中喂一組資料）
x=tf.placeholder(tf.float32,shape=(1,2))
w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))

#定義向前傳播的過程
a=tf.matmul(x,w1)
y=tf.matmul(a,w2)

#用會話計算結果
with tf.Session() as sess:
    init=tf.global_variables_initializer()
    sess.run(init)

    print('w1:\n', sess.run(w1))
    print('w2:\n', sess.run(w2))
    print('y:',sess.run(y,feed_dict={x:[[0.7,0.5]]}))

'''
w1:
 [[-0.81131822  1.48459876  0.06532937]
 [-2.4427042   0.0992484   0.59122431]]
w2:
 [[-0.81131822]
 [ 1.48459876]
 [ 0.06532937]]
y: [[ 3.0904665]]
'''
 

#向前傳播
import tensorflow as tf
x=tf.constant([[0.7,0.5]])
w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))

#定義向前傳播的過程
a=tf.matmul(x,w1)
y=tf.matmul(a,w2)

#用會話計算結果
with tf.Session() as sess:
    init=tf.global_variables_initializer()
    sess.run(init)

    print('w1:\n',sess.run(w1))
    print('w2:\n', sess.run(w2))

    print('a:\n', sess.run(a))
    print('y:\n',sess.run(y))

'''
w1:
 [[-0.81131822  1.48459876  0.06532937]
 [-2.4427042   0.0992484   0.59122431]]
w2:
 [[-0.81131822]
 [ 1.48459876]
 [ 0.06532937]]
a:
 [[-1.78927493  1.08884335  0.34134272]]
y:
 [[ 3.0904665]]
'''
 

import tensorflow as tf
import numpy as np
BATCH_SIZE=8
SEED=23455
#基於seed產生隨機數
rdm=np.random.RandomState(SEED)
#從X這個32行2列的矩陣中 取出一行 判斷如果和小於1 給Y賦值1 如果和不小於1 給Y賦值0
#作為輸入資料集的標籤（正確答案）
X=rdm.rand(32,2)
Y_=[[int((x0+x1)<1)] for (x0,x1) in X]
print ("X:\n",X)
print ("Y_:\n",Y_)

x=tf.placeholder(tf.float32,shape=(None,2))
y_=tf.placeholder(tf.float32,shape=(None,1))
w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
a=tf.matmul(x,w1)
y=tf.matmul(a,w2)

loss=tf.reduce_mean(tf.square(y_-y))
train_step=tf.train.AdamOptimizer(0.001).minimize(loss)

with tf.Session() as sess:
    init=tf.global_variables_initializer()
    sess.run(init)
    print('w1:\n', sess.run(w1))
    print('w2:\n', sess.run(w2))

    #訓練模型
    step=3000
    for i in range(step):
        start=(i*BATCH_SIZE)%32
        end=start+BATCH_SIZE
        sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})
        if i%500==0:
            total_loss=sess.run(loss,feed_dict={x:X,y_:Y_})
            print("After %d training steps, loss_mse on all data is %g" % (i, total_loss))
    print('w1:\n', sess.run(w1))
    print('w2:\n', sess.run(w2))

'''
X:
 [[ 0.83494319  0.11482951]
 [ 0.66899751  0.46594987]
 [ 0.60181666  0.58838408]
 [ 0.31836656  0.20502072]
 [ 0.87043944  0.02679395]
 [ 0.41539811  0.43938369]
 [ 0.68635684  0.24833404]
 [ 0.97315228  0.68541849]
 [ 0.03081617  0.89479913]
 [ 0.24665715  0.28584862]
 [ 0.31375667  0.47718349]
 [ 0.56689254  0.77079148]
 [ 0.7321604   0.35828963]
 [ 0.15724842  0.94294584]
 [ 0.34933722  0.84634483]
 [ 0.50304053  0.81299619]
 [ 0.23869886  0.9895604 ]
 [ 0.4636501   0.32531094]
 [ 0.36510487  0.97365522]
 [ 0.73350238  0.83833013]
 [ 0.61810158  0.12580353]
 [ 0.59274817  0.18779828]
 [ 0.87150299  0.34679501]
 [ 0.25883219  0.50002932]
 [ 0.75690948  0.83429824]
 [ 0.29316649  0.05646578]
 [ 0.10409134  0.88235166]
 [ 0.06727785  0.57784761]
 [ 0.38492705  0.48384792]
 [ 0.69234428  0.19687348]
 [ 0.42783492  0.73416985]
 [ 0.09696069  0.04883936]]
Y_:
 [[1], [0], [0], [1], [1], [1], [1], [0], [1], [1], [1], [0], [0], [0], [0], [0], [0], [1], [0], [0], [1], [1], [0], [1], [0], [1], [1], [1], [1], [1], [0], [1]]

w1:
 [[-0.81131822  1.48459876  0.06532937]
 [-2.4427042   0.0992484   0.59122431]]
w2:
 [[-0.81131822]
 [ 1.48459876]
 [ 0.06532937]]
After 0 training steps, loss_mse on all data is 5.20999
After 500 training steps, loss_mse on all data is 0.617026
After 1000 training steps, loss_mse on all data is 0.392288
After 1500 training steps, loss_mse on all data is 0.386432
After 2000 training steps, loss_mse on all data is 0.384254
After 2500 training steps, loss_mse on all data is 0.383676
w1:
 [[-0.40074909  1.02251101  1.00135291]
 [-2.13084817 -0.23977895  1.12739885]]
w2:
 [[-0.4457432 ]
 [ 1.04927158]
 [-0.5386759 ]]

'''