1. Type convertion in Numpy Here is my code:
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import numpy as np a = np.asarray([1, 2]) b = [] c = np.concatenate((a, b)) print(c.dtype) |
Guess what? The type of variable ‘c’ is ‘float64’! Seems Numpy automatically considers a empty array of Python as ‘float64’ type. So the correct code should be:
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import numpy as np a = np.asarray([1, 2]) b = [] c = np.concatenate((a, np.asarray(b, dtype=a.dtype)) |
This time, the type of ‘c’ is ‘int64’ 2. Convert a tensor… Read more »
In the previous article, I reached mAP 0.739 for VOC2007. After about two weeks, I add more tricks to reach mAP 0.740. The most important trick is escalating the expand-scale of augmentation which is made from this patch. Increase the scale range could help the model to detect a smaller… Read more »
Previously, I was using CUB-200 dataset to train my object detection model. But after I used CUB-200-2011 dataset instead, the training loss became ‘nan’.
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iter 10 || Loss: 17.9996 || timer: 0.2171 sec. iter 20 || Loss: nan || timer: 0.2145 sec. iter 30 || Loss: nan || timer: 0.2145 sec. ... |
I tried to reduce the learning rate, change optimizer from SGD to Adam, and use different types of initializer for parameters. None of these solved… Read more »
1. ‘()’ may mean tuple or nothing.
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len(("birds")) # the inner '()' means nothing len(("birds",)) # the inner '()' means tulple because of the comma |
The result is:
2. Unlike TensorFlow’s static graph, PyTorch could run neural network just as the code. This means a lot of conveniences. The first advantage, we could print out any tensor in our program, no matter in prediction or training…. Read more »