ImageNet分类:np.testing.assert_almost_equal(pred_robas.sum().a

Question

对于使用GloonCV模型的Mxnet中的图像分类，我使用通过网络转换的图像来获得所有ImageNet类的预测概率。

def predict_probabilities(network, data):
"""
Should return the predicted probabilities of ImageNet classes for the given image.
:param network: pre-trained image classification model
:type network: mx.gluon.Block
:param data: batch of transformed images of shape (1, 3, 224, 224)
:type data: mx.nd.NDArray
:return: array of probabilities of shape (1000,)
:rtype: mx.nd.NDArray
"""
# YOUR CODE HERE
data=transform_image("")
pred_probas= network(data)
pred_probas=pred_probas[0]
return pred_probas

我必须满足这些断言：

assert pred_probas.shape == (1000,)
np.testing.assert_almost_equal(pred_probas.sum().asscalar(), 1, decimal=5)
assert pred_probas.dtype == np.float32

虽然我得到了这个错误：

AssertionError                            Traceback (most recent call last)
<ipython-input-10-70779066d528> in <module>
1 pred_probas =
predict_probabilities(network, transformed_test_output)
2 assert
pred_probas.shape == (1000,)
----> 3
np.testing.assert_almost_equal(pred_probas.sum().asscalar(), 1, decimal=5)
4 assert pred_probas.dtype == np.float32
/usr/local/lib/python3.7/dist-
packages/numpy/testing/_private/utils.py in assert_almost_equal(actual, desired,
decimal, err_msg, verbose)
599         pass
600     if abs(desired -
actual) >= 1.5 * 10.0**(-decimal):
--> 601         raise
AssertionError(_build_err_msg())
602 
603 
AssertionError: 
Arrays are
not almost equal to 5 decimals
ACTUAL: 314.64026
DESIRED: 1

我该如何克服这一点？

Answer 1

通过预先训练的网络(即pred_probas= network(data)(对图像数据进行前向传递后

您必须通过softmax 将预测值映射到概率

prob = mx.nd.softmax(pred_probas)

然后返回prob[0]

由于应用了softmax，输出将在0:1的范围内，因此np.testing.assert_almost_equal(pred_probas.sum().asscalar(), 1, decimal=5)将满足，因为pred_probas.sum().asscalar()几乎等于1