tensorflow计算模型数据模型会话模型与简单神经网络样例

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１．tf计算模型——计算图

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＃定义两个不同的图，说明不同图中张量无关

import tensorflow as tf

g1 = tf.Graph()
with g1.as_default():
    v = tf.get_variable("v", [1], initializer = tf.zeros_initializer) # 设置初始值为0

g2 = tf.Graph()
with g2.as_default():
    v = tf.get_variable("v", [1], initializer = tf.ones_initializer())  # 设置初始值为1
    
with tf.Session(graph = g1) as sess:
    tf.global_variables_initializer().run()
    with tf.variable_scope("", reuse=True):
        print(sess.run(tf.get_variable("v")))

with tf.Session(graph = g2) as sess:
    tf.global_variables_initializer().run()
    with tf.variable_scope("", reuse=True):
        print(sess.run(tf.get_variable("v")))

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２．tf数据模型——张量

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import tensorflow as tf
a = tf.constant([1.0, 2.0], name="a")
b = tf.constant([2.0, 3.0], name="b")
result = a + b
print result

sess = tf.InteractiveSession ()
print(result.eval())
sess.close()

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３．tf运行模型——会话*

sess = tf.Session()
with sess.as_default():
     print(result.eval())

４．tf实现完整的神经网络

import tensorflow as tf
from numpy.random import RandomState
#定义神经网络的参数，输入和输出节点。
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')
#定义前向传播过程，损失函数及反向传播算法
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)
#生成模拟数据集
rdm = RandomState(1)
X = rdm.rand(128,2)
Y = [[int(x1+x2 < 1)] for (x1, x2) in X]
#创建一个会话来运行TensorFlow程序。
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:", sess.run(w1)
    print "w2:", sess.run(w2)
    print "\n"
    
    # 训练模型。
    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))
    
    # 输出训练后的参数取值。
    print "\n"
    print "w1:", sess.run(w1)
    print "w2:", sess.run(w2)



 #结果如下
    w1: [[-0.81131822  1.48459876  0.06532937]
 [-2.44270396  0.0992484   0.59122431]]
w2: [[-0.81131822]
 [ 1.48459876]
 [ 0.06532937]]


After 0 training step(s), cross entropy on all data is 0.0674925
After 1000 training step(s), cross entropy on all data is 0.0163385
After 2000 training step(s), cross entropy on all data is 0.00907547
After 3000 training step(s), cross entropy on all data is 0.00714436
After 4000 training step(s), cross entropy on all data is 0.00578471


w1: [[-1.9618274   2.58235407  1.68203783]
 [-3.4681716   1.06982327  2.11788988]]
w2: [[-1.8247149 ]
 [ 2.68546653]
 [ 1.41819501]]

5.pycharm
pycharm打开方法：cd pycharm/bin
source pycharm.sh
.pynb的打开及jupyter的安装：https://blog.csdn.net/gamedr/article/details/72803234