我想自己完成斯坦福大学CS231n 2017年卷积神经网络课程的任务。
我正在尝试使用NumPy仅使用矩阵乘法和求和广播来计算L2距离。 L2距离为：
我正在尝试使用NumPy仅使用矩阵乘法和求和广播来计算L2距离。 L2距离为：
如果我使用这个公式，我认为我可以做到：
compute_distances_two_loops方法的输出与compute_distances_one_loop方法的输出进行比较，两者相等。但是，如果我将compute_distances_two_loops方法的输出与只使用矩阵乘法和广播求和实现L2距离的compute_distances_no_loops方法的输出进行比较，它们是不同的。def compute_distances_two_loops(self, X):
"""
Compute the distance between each test point in X and each training point
in self.X_train using a nested loop over both the training data and the
test data.
Inputs:
- X: A numpy array of shape (num_test, D) containing test data.
Returns:
- dists: A numpy array of shape (num_test, num_train) where dists[i, j]
is the Euclidean distance between the ith test point and the jth training
point.
"""
num_test = X.shape[0]
num_train = self.X_train.shape[0]
dists = np.zeros((num_test, num_train))
for i in xrange(num_test):
for j in xrange(num_train):
#####################################################################
# TODO: #
# Compute the l2 distance between the ith test point and the jth #
# training point, and store the result in dists[i, j]. You should #
# not use a loop over dimension. #
#####################################################################
#dists[i, j] = np.sqrt(np.sum((X[i, :] - self.X_train[j, :]) ** 2))
dists[i, j] = np.sqrt(np.sum(np.square(X[i, :] - self.X_train[j, :])))
#####################################################################
# END OF YOUR CODE #
#####################################################################
return dists
def compute_distances_one_loop(self, X):
"""
Compute the distance between each test point in X and each training point
in self.X_train using a single loop over the test data.
Input / Output: Same as compute_distances_two_loops
"""
num_test = X.shape[0]
num_train = self.X_train.shape[0]
dists = np.zeros((num_test, num_train))
for i in xrange(num_test):
#######################################################################
# TODO: #
# Compute the l2 distance between the ith test point and all training #
# points, and store the result in dists[i, :]. #
#######################################################################
dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i, :]), axis = 1))
#######################################################################
# END OF YOUR CODE #
#######################################################################
print(dists.shape)
return dists
def compute_distances_no_loops(self, X):
"""
Compute the distance between each test point in X and each training point
in self.X_train using no explicit loops.
Input / Output: Same as compute_distances_two_loops
"""
num_test = X.shape[0]
num_train = self.X_train.shape[0]
dists = np.zeros((num_test, num_train))
#########################################################################
# TODO: #
# Compute the l2 distance between all test points and all training #
# points without using any explicit loops, and store the result in #
# dists. #
# #
# You should implement this function using only basic array operations; #
# in particular you should not use functions from scipy. #
# #
# HINT: Try to formulate the l2 distance using matrix multiplication #
# and two broadcast sums. #
#########################################################################
dists = np.sqrt(-2 * np.dot(X, self.X_train.T) +
np.sum(np.square(self.X_train), axis=1) +
np.sum(np.square(X), axis=1)[:, np.newaxis])
print(dists.shape)
#########################################################################
# END OF YOUR CODE #
#########################################################################
return dists
您可以在这里找到完整的可测试代码。
您知道我在compute_distances_no_loops或其他地方做错了什么吗？
更新：
抛出错误消息的代码是：
dists_two = classifier.compute_distances_no_loops(X_test)
# check that the distance matrix agrees with the one we computed before:
difference = np.linalg.norm(dists - dists_two, ord='fro')
print('Difference was: %f' % (difference, ))
if difference < 0.001:
print('Good! The distance matrices are the same')
else:
print('Uh-oh! The distance matrices are different')
并且错误信息：
Difference was: 372100.327569
Uh-oh! The distance matrices are different
X.dot(self.X_train.T)这样的吧，可能是这个原因吗？ - sharathnatrajnp.allclose(compute_distances_no_loops(Y, Z), compute_distances_one_loop(Y, Z))时，它会返回True。 - Dani Mesejocompute_distances_no_loops方法后，我遇到了错误。 - VansFannel