我正在尝试使用TensorFlow在Python中实现多元线性回归，但遇到了逻辑和实现方面的问题。我的代码抛出了以下错误：
Attempting to use uninitialized value VariableCaused by op u'Variable/read'理想情况下，weights输出应该是[2, 3]
def hypothesis_function(input_2d_matrix_trainingexamples, output_matrix_of_trainingexamples, initial_parameters_of_hypothesis_function, learning_rate, num_steps): # 计算属性数量和样本数量 number_of_attributes = len(input_2d_matrix_trainingexamples[0]) number_of_trainingexamples = len(input_2d_matrix_trainingexamples) #图形输入 x = [] for i in range(0, number_of_attributes, 1): x.append(tf.placeholder("float")) y_input = tf.placeholder("float") # 创建模型并设置模型权重 parameters = [] for i in range(0, number_of_attributes, 1): parameters.append( tf.Variable(initial_parameters_of_hypothesis_function[i])) #构建线性模型 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])) # 最小化均方误差 loss = tf.reduce_mean(tf.square(y - y_input)) optimizer = tf.train.GradientDescentOptimizer(learning_rate) train = optimizer.minimize(loss) #初始化变量 init = tf.initialize_all_variables() # 启动图形 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)回答：
从代码示例中不完全清楚，但如果initial_parameters_of_hypothesis_function列表是tf.Variable对象的列表，那么session.run(init)这一行将会失败，因为TensorFlow（目前）还不足以智能地解析变量初始化的依赖关系。为了解决这个问题，你应该更改创建parameters的循环，使用initial_parameters_of_hypothesis_function[i].initialized_value()，这会添加必要的依赖关系：
parameters = []for i in range(0, number_of_attributes, 1): parameters.append(tf.Variable( initial_parameters_of_hypothesis_function[i].initialized_value()))