Question

我尝试构建一个tensorflow模型。这是代码。有三个python文件。（1）opt4_8generateData.py

#coding:utf-8
import matplotlib.pyplot as plt
import numpy as np
seed=2

def generates():
    rdm=np.random.RandomState(seed)
    X=rdm.randn(300,2)
    Y_=[int(x0*x0+x1*x1<2) for (x0,x1) in X]
    Y_c=[['red' if y else 'blue'] for y in Y_]
    #对数据集进行整理，第一个元素为-1表示跟随第二列计算，第二个元素表示多少     #列
    X=np.vstack(X).reshape(-1,2)
    Y_=np.vstack(X).reshape(-1,1)
    return X,Y_,Y_c

（2）opt4_8forward.py

#coding:utf-8
import tensorflow as tf

def get_weight(shape,rugalarizer):
    w=tf.Variable(tf.random_normal(shape),dtype=tf.float32)
    tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

def get_bias(shape):
    b=tf.Variable(tf.constant(0.01,shape=shape))
    return b

def forward(x,regularizer):
    w1=get_weight([2,11],regularizer)
    b1=get_bias([11])
    y1=tf.nn.relu(tf.matmul(x,w1)+b1)

    w2=get_weight([11,1],regularizer)
    b2=get_bias([1])
    y=tf.matmul(y1,w2)+b2
    return y

（3）opt4_8backward.py

#coding:utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import opt4_8generateData
import opt4_8forward

STEPS=40000
BATCH_SIZE=30
LEARNING_RATE_BASE=0.001
LEARNING_RATE_DECAY=0.999
REGULARIZER=0.01


def backward():
    x=tf.placeholder(tf.float32,shape=(None,2))
    y_=tf.placeholder(tf.float32,shape=(None,1))
    X,Y_,Y_c=opt4_8generateData.generates()
    y=opt4_8forward.forward(x,REGULARIZER)

    global_step=tf.Variable(0,trainable=False)

    learning_rate=tf.train.exponential_decay(
    LEARNING_RATE_BASE,
    global_step,
    300/BATCH_SIZE,
    LEARNING_RATE_DECAY,
    staircase=True)

    loss_mse=tf.reduce_mean(tf.square(y-y_))
    loss_total=loss_mse+tf.add_n(tf.get_collection("losses"))

    #定义反向传播方法：包含正则化
    train_step=tf.train.AdamOptimizer(learning_rate).minimize(loss_total)

    with tf.Session() as sess:
        init_op=tf.global_variables_initializer()
        sess.run(init_op)
        for i in range(STEPS):
            start=(i*BATCH_SIZE)%300
            end=start+BATCH_SIZE
            sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})
            if i%2000==0:
                loss_y=sess.run(loss_total,feed_dict={x:X,y_:Y_})                 
                print "After %d steps,loss is:%f" %(i,loss_y)

        xx,yy=np.mgrid[-3:3:.01,-3:3:.01]
        grid=np.c_[xx.ravel(),yy.ravel()]
        probs=sess.run(y,feed_dict={x:grid})
        probs=probs.reshape(xx.shape)

    plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c))
    plt.contour(xx,yy,probs,levels=[.5])
    plt.show()

if __name__=='__main__':
    backward()

当我运行＆＃34; python opt4_8backward.py＆＃34;时，它出错了：

Traceback (most recent call last):
  File "opt4_8backward.py", line 57, in <module>
    backward() 
  File "opt4_8backward.py", line 19, in backward
    y=opt4_8forward.forward(x,REGULARIZER)
  File "/home/yuan/tf/opt4_8forward.py", line 14, in forward
    w1=get_weight([2,11],regularizer)
  File "/home/yuan/tf/opt4_8forward.py", line 6, in get_weight
    tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
NameError: global name 'regularizer' is not defined

如何修复此错误？非常感谢。我检查了我的代码。似乎我和老师之间没有区别。

Answer 1

在opt4_8forward.py中，get_weight函数定义为：

def get_weight(shape,rugalarizer):
    w=tf.Variable(tf.random_normal(shape),dtype=tf.float32)
    tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

这里，函数参数名为rugalarizer，在同一函数中有以下代码行：

tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))

因此，regularizer未定义。 IIUC，功能必须定义为：

def get_weight(shape,regularizer):

在Tensorflow中，我如何修复＆＃34的错误;未定义全局名称＆＃34;？

1 个答案: