# -*- coding: utf-8 -*-

import tensorflow as tf
from numpy.random import RandomState

batch_size=10

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)
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))

    STEPS=5000
    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:
            #每隔一段时间计算在所有数据上的交叉熵并输出
           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))
    
   
         
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    

8dd9430cdea7023cf4c4656c40c75b0154a4a94e
<mask token>

<mask token>
with tf.Session() as sess:
    init_op = tf.initialize_all_variables()
    sess.run(init_op)
    print(sess.run(w1))
    print(sess.run(w2))
    STEPS = 5000
    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))

<mask token>
batch_size = 10
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)
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))
    STEPS = 5000
    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))

import tensorflow as tf
from numpy.random import RandomState
batch_size = 10
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)
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))
    STEPS = 5000
    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))

# -*- coding: utf-8 -*-

import tensorflow as tf
from numpy.random import RandomState

batch_size=10

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)
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))

    STEPS=5000
    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:
            #每隔一段时间计算在所有数据上的交叉熵并输出
           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))
    
   
         
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
