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 related to
python
machine-learning
linear-regression
tensorflow
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')
else:
h = tf.add(tf.matmul(X, W),b, name='h')
return h
from tensorflow.python.framework import ops
ops.reset_default_graph()
g = tf.get_default_graph()
print([op.name for op in g.get_operations()])
with tf.Session() as sess:
# RUN INIT
sess.run(tf.global_variables_initializer())
# 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
ops.reset_default_graph()
g = tf.get_default_graph()
print([op.name for op in g.get_operations()])
with tf.Session() as
# NOT RUNNING BUT ASSIGN
l = linear(np.array([[1.0,2.0,3.0]]).astype(np.float32), 3, 3)
# RUN INIT
sess.run(tf.global_variables_initializer())
print([op.name for op in g.get_operations()])
# ONLY EVAL AFTER INIT
print(l.eval(session=sess))
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)
try:
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()
sess.run(init)
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()
run both:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
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)
Source: Stackoverflow.com