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:
Attempting to use uninitialized value Variable
Caused by op u'Variable/read'
理想情况下,weights
输出应为[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)
从代码示例中并不能100%清楚,但是如果列表initial_parameters_of_hypothesis_function
是tf.Variable
对象列表,则该行将session.run(init)
失败,因为TensorFlow还不够聪明,无法弄清变量初始化中的依赖项。要解决此问题,您应该将创建的循环更改parameters
为use initial_parameters_of_hypothesis_function[i].initialized_value()
,这会添加必要的依赖项:
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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我来说两句