我最近开始学习Tensorflow，并设法编写了一些简单的代码来预测房价。我是机器学习的新手，因此还有很多需要学习的地方，这就是为什么我需要您的帮助。该程序的预测不准确，而且当我尝试使用均方误差（MSE）而不是交叉熵（Cross entropy）来最小化损失时，我得到了NaN和无限值。您能告诉我我犯了什么错误吗？

这是我的代码：

import tensorflow as tfimport numpy as npimport pandas as pdfrom sklearn.metrics import mean_squared_errorLEARNING_RATE = 0.003home_input = pd.read_csv("kc_house_data.csv")features = ["bedrooms", "bathrooms", "sqft_living", "sqft_lot", "floors", "waterfront", "view", "condition", "grade", "sqft_above", "sqft_basement", "yr_built", "yr_renovated", "zipcode", "lat", "long", "sqft_living15", "sqft_lot15"]label = ["price"]X = tf.placeholder(tf.float32, [None, 18])Y = tf.placeholder(tf.float32, [None, 1])W = tf.Variable(tf.ones([18, 1]))b = tf.Variable(1.)Y_ = tf.matmul(X, W) + bcross_entropy = -tf.reduce_sum(Y*tf.log(Y_))loss = tf.reduce_sum(tf.square(Y_ - Y))optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE)train = optimizer.minimize(loss)init = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)    j = 1    for i in range(1000):        j = i * 10        x_data = np.array(home_input[features][j:(j+10)])        y_data = np.array(home_input[label][j:(j+10)])        sess.run(train, feed_dict={X: x_data, Y: y_data})        print(sess.run(Y_, feed_dict={X: x_data, Y: y_data}))

回答：

标准化您的数据，然后将其传递给您的网络可以解决这个问题。为此，StandardScaler 或 MinMaxScaler 可能会对您有所帮助。

from sklearn.preprocessing import StandardScalerdata = [[0, 0], [0, 0], [1, 1], [1, 1]]scaler = StandardScaler()new_data = scaler.fit_transform(data)...# feed new_data to the neural network