TensorFlow笔记（一）TensorFlow入门

一、创建会话的两种方式

  sess = tf.Session()
  sess.run()
  sess.cloes()

with tf.Session() as sess

sess.run()

2.不用调用“sess.close()”来防止资源泄漏。

二、Placeholder 相当于定义了一个位置，数据类型必须要指定，但是维度可以不给出。

import tensorflow as tf

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.constant([[0.7, 0.9]])
x = tf.placeholder(tf.float32, name="input")

a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

with tf.Session() as sess:
    # sess.run(w1.initializer)
    # sess.run(w2.initializer)
    init_op = tf.initialize_all_variables()
    sess.run(init_op)
    print(sess.run(y,feed_dict={x: [[0.7, 0.9], [0.1, 0.4]]}))

tf.placeholder(类型（必须），维度（可省略），名字（？？？）)

运行结果如下：

[[ 3.95757794]
 [ 1.15376544]]

三、构建网络实现二分类问题
一个完整的程序

import tensorflow as tf

from numpy.random import RandomState

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')

a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

cross_entopy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))

train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entopy)

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))

结果如下：

[[-0.81131822  1.48459876  0.06532937]
 [-2.4427042   0.0992484   0.59122431]]
[[-0.81131822]
 [ 1.48459876]
 [ 0.06532937]]

加入循环迭代的整个程序：

import tensorflow as tf

from numpy.random import RandomState

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')

a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

cross_entopy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))

train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entopy)

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))

# 设定训练的步数
    STEP = 5000
    for i in range(STEP):
       start = (i * batch_size) % dataset_size
       end = min(dataset_size, start + 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_entopy, 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))

运行的结果：

[[-0.81131822  1.48459876  0.06532937]
 [-2.4427042   0.0992484   0.59122431]]
[[-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
[[-1.9618274   2.58235407  1.68203783]
 [-3.46817183  1.06982327  2.11789012]]
[[-1.82471502]
 [ 2.68546653]
 [ 1.41819513]]

TensorFlow笔记（一）TensorFlow入门

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