January 2018
Intermediate to advanced
226 pages
5h 53m
German
Content preview from Praxiseinstieg Deep Learning
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Analog zur Erstellung des Trainingsmodells können wir mit den gleichen Funktionen auch das entsprechende Modell für den Test unserer Testdaten erzeugen:
In [25]: test_model = cnn.CNNModelHelper(
order="NCHW",
name="mnist_test",
init_params=False)
data, label = CreateInput(
test_model,
batch_size=100,
db=os.path.join(base_path, 'mnist-test-nchw-leveldb'),
db_type='leveldb')
softmax = CreateLeNetModel(test_model, data)
CreateAccuracy(test_model, softmax, label)
In [26]: deploy_model = cnn.CNNModelHelper(order="NCHW", name="mnist_deploy_
model", init_params=False)
CreateLeNetModel(deploy_model, "data")
Das eigentliche Training des Modells wird mithilfe eines Caffe2-Workspace realisiert. Die Workspace-Struktur wird dabei mit dem Trainingsmodell ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.