Question

我是Tensorflow的新手，我想知道为什么在每个时期都得到成本，W和b的nan值？我正在设置一个交通游戏，我想训练一个模型，以便能够根据以前的奖励和以前的绿灯持续时间来预测最佳的绿灯持续时间。我尝试按照this guide进行设置，但似乎没有用。有任何想法吗？这应该可以复制我遇到的问题，并且我添加了许多印刷品可以帮助比我更有经验的人。

import numpy as np
import random
import matplotlib.pyplot as plt
import tensorflow as tf
import warnings

warnings.simplefilter(action='once', category=FutureWarning) # future warnings annoy me

# add in a couple of rewards and light durations
current_reward = [-1000,-900,-950]
current_green = [10,12,12]

current_reward = np.array(current_reward)
current_green = np.array(current_green)

# Pass in reward and green_light
def green_light_duration_new(current_reward, current_green):
    # Predicting the best light duration based on previous rewards.
    # predict the best duration based on previous step's reward value, using simple linear regression model
    x = current_reward
    y = current_green
    n = len(x)
    # Plot of Training Data  
    plt.scatter(x, y) 
    plt.xlabel('Reward') 
    plt.ylabel('Green Light Duration') 
    plt.title("Training Data") 
    plt.show() 

    X = tf.placeholder("float") 
    Y = tf.placeholder("float") 
    W = tf.Variable(np.random.randn(), name = "W") 
    b = tf.Variable(np.random.randn(), name = "b") 
    learning_rate = 0.01
    training_epochs = 500
    # Hypothesis 
    y_pred = tf.add(tf.multiply(X, W), b) 
    print('y_pred : ', y_pred)
    print('y_pred dtype : ', y_pred.dtype)
    # Mean Squared Error Cost Function 
    cost = tf.reduce_sum(tf.pow(y_pred-Y, 2)) / (2 * n)
    print('cost : ', cost)
    print('cost dtype: ', cost.dtype)
    # Gradient Descent Optimizer 
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)    
    # Global Variables Initializer 
    init = tf.global_variables_initializer()
    # Starting the Tensorflow Session 
    with tf.Session() as sess: 
        # Initializing the Variables 
        sess.run(init) 
        # Iterating through all the epochs 
        for epoch in range(training_epochs): 
            # Feeding each data point into the optimizer using Feed Dictionary 
            for (_x, _y) in zip(x, y): 
                print('_x : ',_x)
                print('_y : ',_y)
                sess.run(optimizer, feed_dict = {X : _x, Y : _y}) 
            # Displaying the result after every 50 epochs 
            if (epoch + 1) % 50 == 0: 
                # Calculating the cost a every epoch 
                c = sess.run(cost, feed_dict = {X : x, Y : y}) 
                print('c : ', c)
                print('c dtype : ', c.dtype)
                print("Epoch", (epoch + 1), ": cost =", c, "W =", sess.run(W), "b =", sess.run(b)) 
        # Storing necessary values to be used outside the Session 
        training_cost = sess.run(cost, feed_dict ={X: x, Y: y}) 
        print('training_cost : ', training_cost)
        print('training_cost dtype : ', training_cost.dtype)
        weight = sess.run(W)
        print('weight : ', weight)
        print('weight : ', weight.dtype)
        bias = sess.run(b)
        print('bias : ', bias)
        print('biad dtype : ', bias.dtype)
    # Calculating the predictions 
    green_light_duration_new = weight * x + bias 
    print("Training cost =", training_cost, "Weight =", weight, "bias =", bias, '\n')
    # Plotting the Results 
    plt.plot(x, y, 'ro', label ='Original data') 
    plt.plot(x, green_light_duration_new, label ='Fitted line') 
    plt.title('Linear Regression Result') 
    plt.legend() 
    plt.show() 
    return green_light_duration_new

# Go to the training function
new_green_dur = green_light_duration_new(current_reward, current_green)

# Append the predicted green light to its list
current_green.append(new_green_dur)

# Go on to run the rest of the simulation with the new green light duration,
# and append its subsequent reward to current_reward list to run again later.

通过以下解决方案更新图片 使用下面提供的解决方案，它仅绘制一个数据点，而不是三个I输入，并且没有最佳拟合线，并且第二个绘图底部的轴坐标不能反映一个数据点的真实位置。

此外，当您在连接之后print(current_green)结束时，数组是否只是3个零？不应该是4吗？首先输入3，然后输入最新的预测值？

我不明白这里发生了什么。为什么要缩放数据？我想要的是能够向回归器提供先前运行的X值（奖励）的新列表，并使其返回/预测10到120秒之间的最佳绿灯持续时间，且缩放比例与原值相同in。之后，应将持续时间添加到current_green列表中。非常感谢，我还是新手。绘图是一个很好的功能，但并非完全必要，我只是想看看它在按预期的方式工作。

Answer 1

首先有两个错误，请使用MinMaxScaler缩放数据。在计算过程中，当数字超出范围时，会弹出NAN 2. Append在numpy数组中不起作用。

这里是您问题的完整解决方案：

th

设置简单的Tensorflow线性回归并返回NaN值？

1 个答案: