TensorFlow:“尝试使用未初始化的值".在变量初始化中 [英] TensorFlow: "Attempting to use uninitialized value" in variable initialization

问题描述

我正在尝试使用TensorFlow在Python中实现多元线性回归，但是遇到了一些逻辑和实现问题.我的代码抛出以下错误:

I am trying to implement multivariate linear regression in Python using TensorFlow, but have run into some logical and implementation issues. My code throws the following error:

Attempting to use uninitialized value Variable
Caused by op u'Variable/read'

理想情况下，weights输出应为[2, 3]

Ideally the weights output should be [2, 3]

def hypothesis_function(input_2d_matrix_trainingexamples,
                        output_matrix_of_trainingexamples,
                        initial_parameters_of_hypothesis_function,
                        learning_rate, num_steps):
    # calculate num attributes and num examples
    number_of_attributes = len(input_2d_matrix_trainingexamples[0])
    number_of_trainingexamples = len(input_2d_matrix_trainingexamples)

    #Graph inputs
    x = []
    for i in range(0, number_of_attributes, 1):
        x.append(tf.placeholder("float"))
    y_input = tf.placeholder("float")

    # Create Model and Set Model weights
    parameters = []
    for i in range(0, number_of_attributes, 1):
        parameters.append(
            tf.Variable(initial_parameters_of_hypothesis_function[i]))

    #Contruct linear model
    y = tf.Variable(parameters[0], "float")
    for i in range(1, number_of_attributes, 1):
        y = tf.add(y, tf.multiply(x[i], parameters[i]))

    # Minimize the mean squared errors
    loss = tf.reduce_mean(tf.square(y - y_input))
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
    train = optimizer.minimize(loss)

    #Initialize the variables
    init = tf.initialize_all_variables()

    # launch the graph
    session = tf.Session()
    session.run(init)
    for step in range(1, num_steps + 1, 1):
        for i in range(0, number_of_trainingexamples, 1):
            feed = {}
            for j in range(0, number_of_attributes, 1):
                array = [input_2d_matrix_trainingexamples[i][j]]
                feed[j] = array
            array1 = [output_matrix_of_trainingexamples[i]]
            feed[number_of_attributes] = array1
            session.run(train, feed_dict=feed)

    for i in range(0, number_of_attributes - 1, 1):
        print (session.run(parameters[i]))

array = [[0.0, 1.0, 2.0], [0.0, 2.0, 3.0], [0.0, 4.0, 5.0]]
hypothesis_function(array, [8.0, 13.0, 23.0], [1.0, 1.0, 1.0], 0.01, 200)

推荐答案

从代码示例中尚不能百分百清楚，但是如果列表initial_parameters_of_hypothesis_function是tf.Variable对象的列表，则行session.run(init)将之所以失败，是因为TensorFlow还不够聪明，无法找出变量初始化中的依赖项.要解决此问题，您应该更改创建parameters的循环以使用 initial_parameters_of_hypothesis_function[i].initialized_value() ，它添加了必要的依赖项:

It's not 100% clear from the code example, but if the list initial_parameters_of_hypothesis_function is a list of tf.Variable objects, then the line session.run(init) will fail because TensorFlow isn't (yet) smart enough to figure out the dependencies in variable initialization. To work around this, you should change the loop that creates parameters to use initial_parameters_of_hypothesis_function[i].initialized_value(), which adds the necessary dependency:

parameters = []
for i in range(0, number_of_attributes, 1):
    parameters.append(tf.Variable(
        initial_parameters_of_hypothesis_function[i].initialized_value()))

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