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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/03/14 18:39:22 UTC
[GitHub] szha commented on issue #10101: gluon feature request: proper registration/initialization of layers inside a list (container) for custom (Hybrid)Blocks
szha commented on issue #10101: gluon feature request: proper registration/initialization of layers inside a list (container) for custom (Hybrid)Blocks
URL: https://github.com/apache/incubator-mxnet/issues/10101#issuecomment-373131491
```python
In [1]: import mxnet as mx
In [2]: net = mx.gluon.model_zoo.vision.alexnet()
In [3]: net
Out[3]:
AlexNet(
(features): HybridSequential(
(0): Conv2D(None -> 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=False)
(2): Conv2D(None -> 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(3): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=False)
(4): Conv2D(None -> 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=False)
(8): Flatten
(9): Dense(None -> 4096, Activation(relu))
(10): Dropout(p = 0.5, axes=())
(11): Dense(None -> 4096, Activation(relu))
(12): Dropout(p = 0.5, axes=())
)
(output): Dense(None -> 1000, linear)
)
In [4]: net.features
Out[4]:
HybridSequential(
(0): Conv2D(None -> 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=False)
(2): Conv2D(None -> 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(3): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=False)
(4): Conv2D(None -> 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=False)
(8): Flatten
(9): Dense(None -> 4096, Activation(relu))
(10): Dropout(p = 0.5, axes=())
(11): Dense(None -> 4096, Activation(relu))
(12): Dropout(p = 0.5, axes=())
)
In [5]: net.features[3]
Out[5]: MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=False)
```
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