2021-06-15 07:49:18 +00:00
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---
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id: 5e8f2f13c4cdbe86b5c72d98
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2021-07-20 16:05:24 +00:00
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title: Creare una rete neurale convoluzionale
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2021-06-15 07:49:18 +00:00
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challengeType: 11
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videoId: kfv0K8MtkIc
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2021-10-03 19:24:27 +00:00
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bilibiliIds:
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aid: 420605824
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bvid: BV1p341127wW
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cid: 409131869
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2021-06-15 07:49:18 +00:00
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dashedName: creating-a-convolutional-neural-network
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---
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# --question--
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## --text--
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2021-07-20 16:05:24 +00:00
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Riempi gli spazi vuoti qui sotto per completare l'architettura di una rete neurale convoluzionale:
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2021-06-15 07:49:18 +00:00
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```py
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model = models.__A__()
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model.add(layers.__B__(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
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model.add(layers.__C__(2, 2))
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model.add(layers.__B__(64, (3, 3), activation='relu'))
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model.add(layers.__C__(2, 2))
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model.add(layers.__B__(32, (3, 3), activation='relu'))
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model.add(layers.__C__(2, 2))
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```
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## --answers--
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A: `Sequential`
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B: `add`
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C: `Wrapper`
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---
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A: `keras`
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B: `Cropping2D`
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C: `AlphaDropout`
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---
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A: `Sequential`
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B: `Conv2D`
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C: `MaxPooling2D`
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## --video-solution--
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3
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