Question

这是我在简单神经网络的tensorflow中的代码：

import tensorflow as tf
import numpy as np

class Model:
    def __init__(self,input_neuron=2,hidden_neuron=10,output_neuron=2):
        self.input_neuron = input_neuron
        self.hidden_neuron = hidden_neuron
        self.output_neuron = output_neuron
        self.x = tf.placeholder(tf.float32,[None,self.input_neuron])
        self.y = tf.placeholder(tf.float32,[None,self.output_neuron])
        self.model = self.graph()
        self.sess = tf.InteractiveSession()
        self.sess.run(tf.global_variables_initializer())



    @staticmethod
    def one_hot_encode(y):
        y_ = np.zeros((len(y),2))
        for i in range(len(y)):
            y_[i,y[i][0]]=1

        return y_

    def graph(self):
        w1=tf.Variable(tf.random_normal([self.input_neuron,self.hidden_neuron]))
        l1=tf.nn.relu(tf.matmul(self.x,w1))
        w2=tf.Variable(tf.random_normal([self.hidden_neuron,self.output_neuron]))
        l2=tf.matmul(l1,w2)

        return l2

    def train(self,xTrain,yTrain):
        yTrain = self.one_hot_encode(yTrain)
        loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits_v2(
                             logits=self.model,labels=self.y))
        train = tf.train.AdamOptimizer(0.1).minimize(loss)
        for epoch in range(100):
            self.sess.run(train,feed_dict={self.x:xTrain,self.y:yTrain})

    def predict(self,xTest):
        prediction = tf.argmax(self.model)
        return self.sess.run(prediction,feed_dict={x:xTest})

当我使用以下方式运行时：

model = Model()
xTrain = np.array([[0,0],[0,1],[1,0],[1,1]])
yTrain = np.array([[0],[1],[1],[0]])
model.train(xTrain,yTrain)

我收到此错误：

FailedPreconditionError（参见上面的回溯）：尝试使用未初始化的值beta1_power_18

我做错了什么？

Answer 1

您在self.sess.run(tf.global_variables_initializer())课程的__init__中执行了Model，但只有train()方法才能设置tf.train.AdamOptimizer()。后者还会创建一些需要初始化的变量。移动

self.sess.run(tf.global_variables_initializer())

在

之后
train = tf.train.AdamOptimizer(0.1).minimize(loss)

它会起作用。

完整代码（已测试）：

import tensorflow as tf import numpy as np class Model: def __init__(self,input_neuron=2,hidden_neuron=10,output_neuron=2): self.input_neuron = input_neuron self.hidden_neuron = hidden_neuron self.output_neuron = output_neuron self.x = tf.placeholder(tf.float32,[None,self.input_neuron]) self.y = tf.placeholder(tf.float32,[None,self.output_neuron]) self.model = self.graph() self.sess = tf.InteractiveSession() @staticmethod def one_hot_encode(y): y_ = np.zeros((len(y),2)) for i in range(len(y)): y_[i,y[i][0]]=1 return y_ def graph(self): w1=tf.Variable(tf.random_normal([self.input_neuron,self.hidden_neuron])) l1=tf.nn.relu(tf.matmul(self.x,w1)) w2=tf.Variable(tf.random_normal([self.hidden_neuron,self.output_neuron])) l2=tf.matmul(l1,w2) return l2 def train(self,xTrain,yTrain): yTrain = self.one_hot_encode(yTrain) loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits_v2( logits=self.model,labels=self.y)) train = tf.train.AdamOptimizer(0.1).minimize(loss) self.sess.run(tf.global_variables_initializer()) for epoch in range(100): self.sess.run(train,feed_dict={self.x:xTrain,self.y:yTrain}) print("Training done!") def predict(self,xTest): prediction = tf.argmax(self.model) return self.sess.run(prediction,feed_dict={x:xTest}) model = Model() xTrain = np.array([[0,0],[0,1],[1,0],[1,1]]) yTrain = np.array([[0],[1],[1],[0]]) model.train(xTrain,yTrain)

根据您的评论，如果您不希望在每次调用train()方法时重新初始化整个网络，则需要使用__init__()方法初始化网络并使用tf.report_uninitialized_variables()使所有未初始化的内容仅初始化train()中的内容。我根据this answer编写了Salvador Dali方法initialize_uninitialized()来编写方法import tensorflow as tf import numpy as np class Model: def __init__(self,input_neuron=2,hidden_neuron=10,output_neuron=2): self.input_neuron = input_neuron self.hidden_neuron = hidden_neuron self.output_neuron = output_neuron self.x = tf.placeholder(tf.float32,[None,self.input_neuron]) self.y = tf.placeholder(tf.float32,[None,self.output_neuron]) self.model = self.graph() self.sess = tf.InteractiveSession() self.sess.run(tf.global_variables_initializer()) @staticmethod def one_hot_encode(y): y_ = np.zeros((len(y),2)) for i in range(len(y)): y_[i,y[i][0]]=1 return y_ def graph(self): w1=tf.Variable(tf.random_normal([self.input_neuron,self.hidden_neuron])) l1=tf.nn.relu(tf.matmul(self.x,w1)) w2=tf.Variable(tf.random_normal([self.hidden_neuron,self.output_neuron])) l2=tf.matmul(l1,w2) return l2 def initialize_uninitialized( self ): uninitialized_variables = [v for v in tf.global_variables() if v.name.split(':')[0] in set(self.sess.run(tf.report_uninitialized_variables())) ] self.sess.run( tf.variables_initializer( uninitialized_variables ) ) def train(self,xTrain,yTrain): yTrain = self.one_hot_encode(yTrain) loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits_v2( logits=self.model,labels=self.y)) train = tf.train.AdamOptimizer(0.1).minimize(loss) self.initialize_uninitialized() for epoch in range(100): self.sess.run(train,feed_dict={self.x:xTrain,self.y:yTrain}) print("Training done!") def predict(self,xTest): prediction = tf.argmax(self.model) return self.sess.run(prediction,feed_dict={x:xTest}) model = Model() xTrain = np.array([[0,0],[0,1],[1,0],[1,1]]) yTrain = np.array([[0],[1],[1],[0]]) model.train(xTrain,yTrain)。

完整代码（已测试）：

AlarmManager alarmManager; static PendingIntent pendingIntent; private String TAG = "netwatcher"; private final int alarmId = 0;

Tensorflow Error：尝试使用未初始化的值beta1_power_18

1 个答案: