我目前正在学习逻辑回归，并且在尝试从训练后的权重中绘制一条线时遇到了困难。我期望得到一个包含3个值的数组，但在打印权重进行检查时，我得到的格式每次都不同，但格式相同：
[array([[ 0.42433906], [-0.67847246]], dtype=float32) array([-0.06681705], dtype=float32)]
我的问题是，为什么权重是以这种两个数组的格式呈现，而不是一个长度为3的数组？以及如何解释这些权重，以便我能够绘制分隔线？
这是我的代码：
from tensorflow import kerasfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.regularizers import L1L2import randomimport numpy as np# return the array data of shape (m, 2) and the array labels of shape (m, 1)def get_random_data(w, b, mu, sigma, m): # slope, y-intercept, mean of the data, standard deviation, size of arrays data = np.empty((m, 2)) labels = np.empty((m, 1)) # fill the arrays with random data for i in range(m): c = (random.random() > 0.5) # 0 with probability 1/2 and 1 with probability 1/2 n = random.normalvariate(mu, sigma) # noise using normal distribution x_1 = random.random() # uniform distribution on [0, 1) x_2 = w * x_1 + b + (-1)**c * n labels[i] = c data[i][0] = x_1 data[i][1] = x_2 # the train set is the first 80% of our data, and the test set is the following 20% train_length = int(round(m * 0.8, 1)) train_data = np.empty((train_length, 2)) train_labels = np.empty((train_length, 1)) test_data = np.empty((m - train_length, 2)) test_labels = np.empty((m - train_length, 1)) for i in range(train_length): train_data[i] = data[i] train_labels[i] = labels[i] for i in range(train_length, m): test_data[i - train_length] = data[i] test_labels[i - train_length] = labels[i] return (train_data, train_labels), (test_data, test_labels)(train_data, train_labels), (test_data, test_labels) = get_random_data(2,3,100,100,200)model = Sequential()model.add(Dense(train_labels.shape[1], activation='sigmoid', kernel_regularizer=L1L2(l1=0.0, l2=0.1), input_dim=(train_data.shape[1]))) model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])model.fit(train_data, train_labels, epochs=100, validation_data=(test_data,test_labels))weights = np.asarray(model.get_weights())print("the weights are " , weights)回答：
数组的第一个索引显示的是系数的权重，第二个数组显示的是偏置项。
因此，您可以得到如下方程：
h(x) = 0.42433906x1 + -0.67847246x2 + -0.06681705
逻辑回归会采用这个方程，并应用 sigmoid 函数将结果压缩在0到1之间。
所以，如果您想绘制一条线的方程，您可以按照我上面解释的方式使用返回的权重来实现。
