TensorFlow: "Attempting to use uninitialized value" in variable initialization

49

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'

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)

This question is tagged with python machine-learning linear-regression tensorflow

~ Asked on 2016-03-15 09:55:38

The Best Answer is


21

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()))

~ Answered on 2016-03-15 15:52:28


68

Run this:

init = tf.global_variables_initializer()
sess.run(init)

Or (depending on the version of TF that you have):

init = tf.initialize_all_variables()
sess.run(init)

~ Answered on 2016-11-20 02:24:03


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