TensorFlow上的简单网络

时间:2016-03-05 20:21:02

标签: machine-learning tensorflow deep-learning

我试图在TensorFlow上训练一个非常简单的模型。模型将单个浮点数作为输入,并返回输入概率大于0.我使用了1个隐藏层,其中包含10个隐藏单位。完整代码如下所示:

import tensorflow as tf
import random 

# Graph construction

x = tf.placeholder(tf.float32, shape = [None,1])
y_ = tf.placeholder(tf.float32, shape = [None,1])

W = tf.Variable(tf.random_uniform([1,10],0.,0.1))
b = tf.Variable(tf.random_uniform([10],0.,0.1))

layer1 = tf.nn.sigmoid( tf.add(tf.matmul(x,W), b) )

W1 = tf.Variable(tf.random_uniform([10,1],0.,0.1))
b1 = tf.Variable(tf.random_uniform([1],0.,0.1))

y = tf.nn.sigmoid( tf.add( tf.matmul(layer1,W1),b1) )

loss = tf.square(y - y_)

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

# Training

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    N = 1000
    while N != 0:
        batch = ([],[])
        u = random.uniform(-10.0,+10.0)
        if u >= 0.:
            batch[0].append([u])
            batch[1].append([1.0])
        if  u < 0.:
            batch[0].append([u])
            batch[1].append([0.0])

        sess.run(train_step, feed_dict = {x : batch[0] , y_ : batch[1]} )
        N -= 1

    while(True):
        u = raw_input("Give an x\n")
        print sess.run(y, feed_dict = {x : [[u]]})   

问题是,我得到了非常不相关的结果。模型不学习任何东西并返回不相关的概率。我试图调整学习率并改变变量初始化,但我没有得到任何有用的东西。你有什么建议吗?

1 个答案:

答案 0 :(得分:2)

你只计算一个概率,你想要的是两个类:

  • 大于/等于零。
  • 小于零。

因此,网络的输出将是一个形状二的张量,它将包含每个类的概率。我在您的示例中将y_重命名为labels

labels = tf.placeholder(tf.float32, shape = [None,2])

接下来,我们计算网络结果与预期分类之间的交叉熵。正数的类别为[1.0, 0],负数的类别为[0.0, 1.0]。 损失函数变为:

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, labels)
loss = tf.reduce_mean(cross_entropy)

我将y重命名为logits,因为这是一个更具描述性的名称。

培训此网络10000步:

Give an x
3.0
[[ 0.96353203  0.03686807]]
Give an x
200
[[ 0.97816485  0.02264325]]
Give an x
-20
[[ 0.12095013  0.87537241]]

完整代码:

import tensorflow as tf
import random

# Graph construction

x = tf.placeholder(tf.float32, shape = [None,1])
labels = tf.placeholder(tf.float32, shape = [None,2])

W = tf.Variable(tf.random_uniform([1,10],0.,0.1))
b = tf.Variable(tf.random_uniform([10],0.,0.1))

layer1 = tf.nn.sigmoid( tf.add(tf.matmul(x,W), b) )

W1 = tf.Variable(tf.random_uniform([10, 2],0.,0.1))
b1 = tf.Variable(tf.random_uniform([1],0.,0.1))

logits = tf.nn.sigmoid( tf.add( tf.matmul(layer1,W1),b1) )

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, labels)

loss = tf.reduce_mean(cross_entropy)

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

# Training

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    N = 1000
    while N != 0:
        batch = ([],[])
        u = random.uniform(-10.0,+10.0)
        if u >= 0.:
            batch[0].append([u])
            batch[1].append([1.0, 0.0])
        if  u < 0.:
            batch[0].append([u])
            batch[1].append([0.0, 1.0])

        sess.run(train_step, feed_dict = {x : batch[0] , labels : batch[1]} )

        N -= 1

    while(True):
        u = raw_input("Give an x\n")
        print sess.run(logits, feed_dict = {x : [[u]]})