[python] TensorFlow: "Attempting to use uninitialized value" in variable initialization

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,
                        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):
    y_input = tf.placeholder("float")

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

    #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()
    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 related to python machine-learning linear-regression tensorflow

The answer is

Run this:

init = tf.global_variables_initializer()

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

init = tf.initialize_all_variables()

run both:



There is another the error happening which related to the order when calling initializing global variables. I've had the sample of code has similar error FailedPreconditionError (see above for traceback): Attempting to use uninitialized value W

def linear(X, n_input, n_output, activation = None):
    W = tf.Variable(tf.random_normal([n_input, n_output], stddev=0.1), name='W')
    b = tf.Variable(tf.constant(0, dtype=tf.float32, shape=[n_output]), name='b')
    if activation != None:
        h = tf.nn.tanh(tf.add(tf.matmul(X, W),b), name='h')
        h = tf.add(tf.matmul(X, W),b, name='h')
    return h

from tensorflow.python.framework import ops
g = tf.get_default_graph()
print([op.name for op in g.get_operations()])
with tf.Session() as sess:
    # RUN INIT
    # But W hasn't in the graph yet so not know to initialize 
    # EVAL then error
    print(linear(np.array([[1.0,2.0,3.0]]).astype(np.float32), 3, 3).eval())

You should change to following

from tensorflow.python.framework import ops
g = tf.get_default_graph()
print([op.name for op in g.get_operations()])
with tf.Session() as 
    l = linear(np.array([[1.0,2.0,3.0]]).astype(np.float32), 3, 3)
    # RUN INIT
    print([op.name for op in g.get_operations()])

I want to give my resolution, it work when i replace the line [session = tf.Session()] with [sess = tf.InteractiveSession()]. Hope this will be useful to others.

Normally there are two ways of initializing variables, 1) using the sess.run(tf.global_variables_initializer()) as the previous answers noted; 2) the load the graph from checkpoint.

You can do like this:

sess = tf.Session(config=config)
saver = tf.train.Saver(max_to_keep=3)
    saver.restore(sess, tf.train.latest_checkpoint(FLAGS.model_dir))
    # start from the latest checkpoint, the sess will be initialized 
    # by the variables in the latest checkpoint
except ValueError:
    # train from scratch
    init = tf.global_variables_initializer()

And the third method is to use the tf.train.Supervisor. The session will be

Create a session on 'master', recovering or initializing the model as needed, or wait for a session to be ready.

sv = tf.train.Supervisor([parameters])
sess = sv.prepare_or_wait_for_session()

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