TensorFlow: "Versuch, nicht initialisierten Wert zu verwenden" bei der Variableninitialisierung
Ich versuche, mit TensorFlow eine multivariate lineare Regression in Python zu implementieren, bin jedoch auf einige logische Probleme und Implementierungsprobleme gestoßen. Mein Code gibt folgenden Fehler aus:
Attempting to use uninitialized value Variable
Caused by op u'Variable/read'
Ideally theweights
output sollte @ se[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)