设 $F\in\mathbb{R}^{S\times F}$ 为特征矩阵,我想使用带有 autograd 的逻辑回归对它们进行分类 [1]。我使用的代码类似于以下示例 [2] 中的代码。
我唯一想要改变的是,我在 $\mathbb{R}^{F\times L}$ 中有一个额外的权重矩阵 $W$,我想将其应用于每个特征。因此,每个特征都与 $W$ 相乘,然后输入到逻辑回归中。
是否可以使用 autograd 同时训练 $W$ 和逻辑回归的权重?
我尝试了以下代码,不幸的是权重保持在值 0。
import autograd.numpy as np
from autograd import grad
global inputs
def sigmoid(x):
return 0.5 * (np.tanh(x) + 1)
def logistic_predictions(weights, inputs):
# Outputs probability of a label being true according to logistic model.
return sigmoid(np.dot(inputs, weights))
def training_loss(weights):
global inputs
# Training loss is the negative log-likelihood of the training labels.
feature_weights = weights[3:]
feature_weights = np.reshape(feature_weights, (3, 3))
inputs = np.dot(inputs, feature_weights)
preds = logistic_predictions(weights[0:3], inputs)
label_probabilities = preds * targets + (1 - preds) * (1 - targets)
return -np.sum(np.log(label_probabilities))
# Build a toy dataset.
inputs = np.array([[0.52, 1.12, 0.77],
[0.88, -1.08, 0.15],
[0.52, 0.06, -1.30],
[0.74, -2.49, 1.39]])
targets = np.array([True, True, False, True])
# Define a function that returns gradients of training loss using autograd.
training_gradient_fun = grad(training_loss)
# Optimize weights using gradient descent.
weights = np.zeros([3 + 3 * 3])
print "Initial loss:", training_loss(weights)
for i in xrange(100):
print(i)
print(weights)
weights -= training_gradient_fun(weights) * 0.01
print "Trained loss:", training_loss(weights)
[1] https://github.com/HIPS/autograd
[2] https://github.com/HIPS/autograd/blob/master/examples/logistic_regression.py
最佳答案
典型的做法是将所有“向量化”参数连接到决策变量向量中。
如果您更新 logistic_predictions
以包含 W
矩阵,通过类似
def logistic_predictions(weights_and_W, inputs):
'''
Here, :arg weights_and_W: is an array of the form [weights W.ravel()]
'''
# Outputs probability of a label being true according to logistic model.
weights = weights_and_W[:inputs.shape[1]]
W_raveled = weights_and_W[inputs.shape[1]:]
n_W = len(W_raveled)
W = W_raveled.reshape(inputs.shape[1], n_W/inputs.shape[1])
return sigmoid(np.dot(np.dot(inputs, W), weights))
然后只需将 traning_loss
更改为(来自原始源示例)
def training_loss(weights_and_W):
# Training loss is the negative log-likelihood of the training labels.
preds = logistic_predictions(weights_and_W, inputs)
label_probabilities = preds * targets + (1 - preds) * (1 - targets)
return -np.sum(np.log(label_probabilities))
关于python - 线性回归和自动求导,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/43117417/