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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/03/10 14:56:31 UTC
[GitHub] [incubator-mxnet] aGiant opened a new issue #17800: _MinusScalar
type not supported` ?
aGiant opened a new issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800
```
<ipython-input-92-6fb5c4df8b27> in hybrid_forward(self, F, x)
36 #x = self.normalizer(data)
37 flat_x = self.flatten(x)
---> 38 above = flat_x - self.min
39 under = self.max - self.min
40 x_normalized = above/under
~/anaconda3/lib/python3.7/site-packages/mxnet/symbol/symbol.py in __sub__(self, other)
127 return _internal._MinusScalar(self, scalar=other)
128 else:
--> 129 raise TypeError('type %s not supported' % str(type(other)))
130
131 def __isub__(self, other):
TypeError: type <class 'mxnet.ndarray.ndarray.NDArray'> not supported
```
those are defined in hybrid_forward of official VAE code example.
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597183356
Just tried example from https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html to build **### min-max normalization using saved min-max values**, but more and more errors:
```
--------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-213-cbd52c8ac2cb> in <module>
2 net.collect_params().initialize(mx.init.Xavier(), ctx=model_ctx)
3 #net(mx.nd.random.uniform(shape=(128,feature_n), ctx=model_ctx))
----> 4 print(net.summary(mx.nd.random.uniform(shape=(1,feature_n), ctx=model_ctx)))
5 net.hybridize()
6 trainer = gluon.Trainer(net.collect_params(), 'SGD', {'wd':0.01}) #'learning_rate': .001,
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in summary(self, *inputs)
648 try:
649 self.apply(_register_summary_hook)
--> 650 self(*inputs)
651
652 line_format = '{:>20} {:>42} {:>15}'
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in __call__(self, *args)
546 hook(self, args)
547
--> 548 out = self.forward(*args)
549
550 for hook in self._forward_hooks.values():
<ipython-input-211-58f911007fa2> in forward(self, x)
43 def forward(self,x):
44 self.batch_size = x.shape[0]
---> 45 return gluon.HybridBlock.forward(self, x)
46
47 # https://mxnet.apache.org/api/python/docs/tutorials/extend/custom_layer.html
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in forward(self, x, *args)
923 params = {i: j.data(ctx) for i, j in self._reg_params.items()}
924
--> 925 return self.hybrid_forward(ndarray, x, *args, **params)
926
927 assert isinstance(x, Symbol), \
<ipython-input-211-58f911007fa2> in hybrid_forward(self, F, x)
49 def hybrid_forward(self, F, x):
50 #x = self.normalizer(data)
---> 51 x_normalized = F.broadcast_div(F.broadcast_sub(self.flatten(x), self.min_v), (F.broadcast_sub(self.max_v, self.min_v)))
52 h = self.encoder(x_normalized)
53 #print(h.asnumpy()[0])
~/anaconda3/lib/python3.7/site-packages/mxnet/ndarray/register.py in broadcast_sub(lhs, rhs, out, name, **kwargs)
AssertionError: Argument rhs must have NDArray type, but got Parameter vae54_scales_min (shape=(70,), dtype=<class 'numpy.float32'>)
```
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[GitHub] [incubator-mxnet] leezu commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
leezu commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597264160
No. net(x)
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[GitHub] [incubator-mxnet] aGiant removed a comment on issue #17800:
_MinusScalar type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant removed a comment on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597175578
Tried min-max normalization with given min-max values based on https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html:
```
--------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-175-2d25ca5fd6d4> in <module>
6 model_prefix = 'vae_gluon_{}d{}l{}h.params'.format(n_latent, n_layers, n_hidden)
7
----> 8 net = VAE(n_hidden, n_latent, n_layers, n_output, min_v.values, max_v.values)
<ipython-input-174-2c3888550b32> in __init__(self, n_hidden, n_latent, n_layers, n_output, min_vec, max_vec, act_type, **kwargs)
13 # self.max_v = max_vec #mx.gluon.Constant('max_v', max_vec)
14 print(dir(self))
---> 15 self.min_v = self.params.get('scales_min',
16 shape=scales.shape,
17 init=mx.init.Constant(min_vec),
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in params(self)
268 """Returns this :py:class:`Block`'s parameter dictionary (does not include its
269 children's parameters)."""
--> 270 return self._params
271
272 def collect_params(self, select=None):
AttributeError: 'VAE' object has no attribute '_params'
```
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[GitHub] [incubator-mxnet] aGiant edited a comment on issue #17800:
_MinusScalar type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant edited a comment on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597183356
Just tried example from https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html to build ** min-max normalization using saved min-max values**, but more and more errors:
```
--------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-213-cbd52c8ac2cb> in <module>
2 net.collect_params().initialize(mx.init.Xavier(), ctx=model_ctx)
3 #net(mx.nd.random.uniform(shape=(128,feature_n), ctx=model_ctx))
----> 4 print(net.summary(mx.nd.random.uniform(shape=(1,feature_n), ctx=model_ctx)))
5 net.hybridize()
6 trainer = gluon.Trainer(net.collect_params(), 'SGD', {'wd':0.01}) #'learning_rate': .001,
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in summary(self, *inputs)
648 try:
649 self.apply(_register_summary_hook)
--> 650 self(*inputs)
651
652 line_format = '{:>20} {:>42} {:>15}'
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in __call__(self, *args)
546 hook(self, args)
547
--> 548 out = self.forward(*args)
549
550 for hook in self._forward_hooks.values():
<ipython-input-211-58f911007fa2> in forward(self, x)
43 def forward(self,x):
44 self.batch_size = x.shape[0]
---> 45 return gluon.HybridBlock.forward(self, x)
46
47 # https://mxnet.apache.org/api/python/docs/tutorials/extend/custom_layer.html
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in forward(self, x, *args)
923 params = {i: j.data(ctx) for i, j in self._reg_params.items()}
924
--> 925 return self.hybrid_forward(ndarray, x, *args, **params)
926
927 assert isinstance(x, Symbol), \
<ipython-input-211-58f911007fa2> in hybrid_forward(self, F, x)
49 def hybrid_forward(self, F, x):
50 #x = self.normalizer(data)
---> 51 x_normalized = F.broadcast_div(F.broadcast_sub(self.flatten(x), self.min_v), (F.broadcast_sub(self.max_v, self.min_v)))
52 h = self.encoder(x_normalized)
53 #print(h.asnumpy()[0])
~/anaconda3/lib/python3.7/site-packages/mxnet/ndarray/register.py in broadcast_sub(lhs, rhs, out, name, **kwargs)
AssertionError: Argument rhs must have NDArray type, but got Parameter vae54_scales_min (shape=(70,), dtype=<class 'numpy.float32'>)
```
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[GitHub] [incubator-mxnet] leezu commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
leezu commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597208855
`hybrid_forward(self, F, x, min_v, max_v):` still mean users call the `net(x)`, with `min_v, max_v` being automatically passed and taking the value of the constants.
My point is that you're not using the hybridize API correctly, thus the link to the documentation.
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597207080
> `self.min_v` is a constant parameter. In the current `hybrid_forward` API, you need to `def hybrid_forward(self, F, x, min_v, max_v):` and you'll get the ndarray associated with the parameter as an argument.
>
> The API will be simplified with #17530
>
> You can read https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html
Your solution is based on the assumption that all the min-max values were already prepared or at least accessible. Our problem from production environment is that the final user or platform does not have the access to gather the min-max values.
One possible solution for our problem in production is to save the min-max values default within the NN as constant vectors.
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597210313
> `self.min_v` is a constant parameter. In the current `hybrid_forward` API, you need to `def hybrid_forward(self, F, x, min_v, max_v):` and you'll get the ndarray associated with the parameter as an argument.
>
> The API will be simplified with #17530
>
> You can read https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html
Or in short, our final user just have the x, they do not have any min_v and max_v basedon
> link
Could you pleas point out the errors？ I have already visited that links many times from last month.
Many thanks!
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[GitHub] [incubator-mxnet] aGiant edited a comment on issue #17800:
_MinusScalar type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant edited a comment on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597183356
Just tried example from https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html to build ** min-max normalization using saved min-max values**, but more and more errors:
```
--------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-213-cbd52c8ac2cb> in <module>
2 net.collect_params().initialize(mx.init.Xavier(), ctx=model_ctx)
3 #net(mx.nd.random.uniform(shape=(128,feature_n), ctx=model_ctx))
----> 4 print(net.summary(mx.nd.random.uniform(shape=(1,feature_n), ctx=model_ctx)))
5 net.hybridize()
6 trainer = gluon.Trainer(net.collect_params(), 'SGD', {'wd':0.01}) #'learning_rate': .001,
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in summary(self, *inputs)
648 try:
649 self.apply(_register_summary_hook)
--> 650 self(*inputs)
651
652 line_format = '{:>20} {:>42} {:>15}'
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in __call__(self, *args)
546 hook(self, args)
547
--> 548 out = self.forward(*args)
549
550 for hook in self._forward_hooks.values():
<ipython-input-211-58f911007fa2> in forward(self, x)
43 def forward(self,x):
44 self.batch_size = x.shape[0]
---> 45 return gluon.HybridBlock.forward(self, x)
46
47 # https://mxnet.apache.org/api/python/docs/tutorials/extend/custom_layer.html
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in forward(self, x, *args)
923 params = {i: j.data(ctx) for i, j in self._reg_params.items()}
924
--> 925 return self.hybrid_forward(ndarray, x, *args, **params)
926
927 assert isinstance(x, Symbol), \
<ipython-input-211-58f911007fa2> in hybrid_forward(self, F, x)
49 def hybrid_forward(self, F, x):
50 #x = self.normalizer(data)
---> 51 x_normalized = F.broadcast_div(F.broadcast_sub(self.flatten(x), self.min_v), (F.broadcast_sub(self.max_v, self.min_v)))
52 h = self.encoder(x_normalized)
53 #print(h.asnumpy()[0])
~/anaconda3/lib/python3.7/site-packages/mxnet/ndarray/register.py in broadcast_sub(lhs, rhs, out, name, **kwargs)
AssertionError: Argument rhs must have NDArray type, but got Parameter vae54_scales_min (shape=(70,), dtype=<class 'numpy.float32'>)
```
Saved constant min-max values:
```
self.min_v = self.params.get('scales_min',
shape=min_vec.shape,
init=mx.init.Constant(min_vec),
differentiable=False)
self.max_v = self.params.get('scales_max',
shape=max_vec.shape,
init=mx.init.Constant(max_vec),
differentiable=False)
```
Also tried
```
self.min_v = mx.gluon.Constant('min_v', min_vec)
self.max_v = mx.gluon.Constant('max_v', max_vec)
```
but got similar error:
```
<ipython-input-214-026df9c27e7e> in hybrid_forward(self, F, x)
49 def hybrid_forward(self, F, x):
50 #x = self.normalizer(data)
---> 51 x_normalized = F.broadcast_div(F.broadcast_sub(self.flatten(x), self.min_v), (F.broadcast_sub(self.max_v, self.min_v)))
52 h = self.encoder(x_normalized)
53 #print(h.asnumpy()[0])
~/anaconda3/lib/python3.7/site-packages/mxnet/ndarray/register.py in broadcast_sub(lhs, rhs, out, name, **kwargs)
AssertionError: Argument rhs must have NDArray type, but got Constant min_v (shape=(70,), dtype=<class 'numpy.float32'>)
```
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597273750
By calling net(x):
```
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in forward(self, x, *args)
923 params = {i: j.data(ctx) for i, j in self._reg_params.items()}
924
--> 925 return self.hybrid_forward(ndarray, x, *args, **params)
926
927 assert isinstance(x, Symbol), \
TypeError: hybrid_forward() missing 2 required positional arguments: 'min_v' and 'max_v'
```
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597157035
More errors
```
<ipython-input-133-18747e174c30> in __init__(self, n_hidden, n_latent, n_layers, n_output, min_vec, max_vec, act_type, **kwargs)
11 # https://mxnet.apache.org/api/python/docs/api/gluon/constant.html
12 # self.const = self.params.get_constant('const', [[1,2],[3,4]])
---> 13 self.min_v = mx.gluon.Constant('const_min', min_vec)
14 self.max_v = mx.gluon.Constant('const_max', max_vec)
15 # note to self: requring batch_size in model definition is sad,
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in __setattr__(self, name, value)
732 def __setattr__(self, name, value):
733 """Registers parameters."""
--> 734 super(HybridBlock, self).__setattr__(name, value)
735 if isinstance(value, HybridBlock):
736 self._clear_cached_op()
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in __setattr__(self, name, value)
202 self.register_child(value, name)
203 elif isinstance(value, Parameter):
--> 204 assert name not in self._reg_params, \
205 "Overriding Parameter attribute %s is not allowed. " \
206 "If you want to share parameters between blocks, please set " \
AttributeError: 'VAE' object has no attribute '_reg_params'
```
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597175578
Tried min-max normalization with given min-max values based on https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html:
```
--------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-175-2d25ca5fd6d4> in <module>
6 model_prefix = 'vae_gluon_{}d{}l{}h.params'.format(n_latent, n_layers, n_hidden)
7
----> 8 net = VAE(n_hidden, n_latent, n_layers, n_output, min_v.values, max_v.values)
<ipython-input-174-2c3888550b32> in __init__(self, n_hidden, n_latent, n_layers, n_output, min_vec, max_vec, act_type, **kwargs)
13 # self.max_v = max_vec #mx.gluon.Constant('max_v', max_vec)
14 print(dir(self))
---> 15 self.min_v = self.params.get('scales_min',
16 shape=scales.shape,
17 init=mx.init.Constant(min_vec),
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in params(self)
268 """Returns this :py:class:`Block`'s parameter dictionary (does not include its
269 children's parameters)."""
--> 270 return self._params
271
272 def collect_params(self, select=None):
AttributeError: 'VAE' object has no attribute '_params'
```
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[GitHub] [incubator-mxnet] leezu commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
leezu commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597224296
The parameters (constant or not), are handled special in the current hybrid_forward API.
You can see the implementation:
https://github.com/apache/incubator-mxnet/blob/afb8742e6e1e987833b39c487dc892b5537196a1/python/mxnet/gluon/block.py#L1204-L1206
So you should use `hybrid_forward(self, F, x, min_v, max_v):` and not access `self.min_v` inside the hybrid_forward.
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[GitHub] [incubator-mxnet] leezu commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
leezu commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597203564
`self.min_v` is a constant parameter. In the current `hybrid_forward` API, you need to `def hybrid_forward(self, F, x, min_v, max_v):` and you'll get the ndarray associated with the parameter as an argument.
The API will be simplified with https://github.com/apache/incubator-mxnet/pull/17530
You can read https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html
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[GitHub] [incubator-mxnet] leezu closed issue #17800: _MinusScalar type
not supported` ?
Posted by GitBox <gi...@apache.org>.
leezu closed issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597135956
Maybe I was too old to count the number ;)
```
model_ctx = mx.gpu()
<ipython-input-102-09c5bc45d5b4> in hybrid_forward(self, F, x)
35 def hybrid_forward(self, F, x):
36 #x = self.normalizer(data)
---> 37 x.as_in_context(model_ctx)
38 flat_x = self.flatten(x)
39 above = flat_x - self.min
TypeError: **as_in_context() takes 1 positional argument but 2 were given**
```
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[GitHub] [incubator-mxnet] ptrendx commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
ptrendx commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-606925771
@aGiant You are still using `self.min_v` and `self.max_v` in your `hybrid_forward`. Are you sure you have them set as parameters? Here is a simple working example of using parameters:
```python
class Test(gluon.HybridBlock):
def __init__(self, **kwargs):
super(Test, self).__init__(**kwargs)
with self.name_scope():
self.x_min = self.params.get('scales_min',
shape=(1,),
init=mx.init.Constant(5),
differentiable=False)
self.x_max = self.params.get('scales_max',
shape=(1,),
init=mx.init.Constant(10),
differentiable=False)
def hybrid_forward(self, F, x, x_min, x_max):
return (x * x_min) + x_max
t = Test()
t.initialize()
t(mx.nd.ones((1,)))
```
```python
[15.]
<NDArray 1 @cpu(0)>
```
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[GitHub] [incubator-mxnet] aGiant edited a comment on issue #17800:
_MinusScalar type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant edited a comment on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597183356
Just tried example from https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html to build ** min-max normalization using saved min-max values**, but more and more errors:
```
--------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-213-cbd52c8ac2cb> in <module>
2 net.collect_params().initialize(mx.init.Xavier(), ctx=model_ctx)
3 #net(mx.nd.random.uniform(shape=(128,feature_n), ctx=model_ctx))
----> 4 print(net.summary(mx.nd.random.uniform(shape=(1,feature_n), ctx=model_ctx)))
5 net.hybridize()
6 trainer = gluon.Trainer(net.collect_params(), 'SGD', {'wd':0.01}) #'learning_rate': .001,
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in summary(self, *inputs)
648 try:
649 self.apply(_register_summary_hook)
--> 650 self(*inputs)
651
652 line_format = '{:>20} {:>42} {:>15}'
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in __call__(self, *args)
546 hook(self, args)
547
--> 548 out = self.forward(*args)
549
550 for hook in self._forward_hooks.values():
<ipython-input-211-58f911007fa2> in forward(self, x)
43 def forward(self,x):
44 self.batch_size = x.shape[0]
---> 45 return gluon.HybridBlock.forward(self, x)
46
47 # https://mxnet.apache.org/api/python/docs/tutorials/extend/custom_layer.html
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in forward(self, x, *args)
923 params = {i: j.data(ctx) for i, j in self._reg_params.items()}
924
--> 925 return self.hybrid_forward(ndarray, x, *args, **params)
926
927 assert isinstance(x, Symbol), \
<ipython-input-211-58f911007fa2> in hybrid_forward(self, F, x)
49 def hybrid_forward(self, F, x):
50 #x = self.normalizer(data)
---> 51 x_normalized = F.broadcast_div(F.broadcast_sub(self.flatten(x), self.min_v), (F.broadcast_sub(self.max_v, self.min_v)))
52 h = self.encoder(x_normalized)
53 #print(h.asnumpy()[0])
~/anaconda3/lib/python3.7/site-packages/mxnet/ndarray/register.py in broadcast_sub(lhs, rhs, out, name, **kwargs)
AssertionError: Argument rhs must have NDArray type, but got Parameter vae54_scales_min (shape=(70,), dtype=<class 'numpy.float32'>)
```
Saved constant min-max values:
```
self.min_v = self.params.get('scales_min',
shape=min_vec.shape,
init=mx.init.Constant(min_vec),
differentiable=False)
self.max_v = self.params.get('scales_max',
shape=max_vec.shape,
init=mx.init.Constant(max_vec),
differentiable=False)
```
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597279061
> No. net(x)
```
def hybrid_forward(self, F, x, min_v, max_v):
#x = self.normalizer(data)
#x_normalized = F.broadcast_div(F.broadcast_sub(self.flatten(x), self.min_v), (F.broadcast_sub(self.max_v, self.min_v)))
x_normalized = self.trans(x) - min_v
h = self.encoder(x_normalized)
#print(h.asnumpy()[0])
```
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597227128
> The parameters (constant or not), are handled special in the current hybrid_forward API.
>
> You can see the implementation:
>
> https://github.com/apache/incubator-mxnet/blob/afb8742e6e1e987833b39c487dc892b5537196a1/python/mxnet/gluon/block.py#L1204-L1206
>
> So you should use `hybrid_forward(self, F, x, min_v, max_v):` and not access `self.min_v` inside the hybrid_forward.
The by calling for prediction, our client must use net(x, min_v, max_v), right? But they do not have any access to prepare the min_v and max_v in production.
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597213200
> `hybrid_forward(self, F, x, min_v, max_v):` still mean users call the `net(x)`, with `min_v, max_v` being automatically passed and taking the value of the constants.
>
> My point is that you're not using the hybridize API correctly, thus the link to the documentation.
Because from official link in hybird_forward:
```
return (x - nd.min(x)) / (nd.max(x) - nd.min(x))
```
this works very well, but
```
return (x - self.min_v)
```
did not work.
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[GitHub] [incubator-mxnet] aGiant commented on issue #17800: _MinusScalar
type not supported` ?
Posted by GitBox <gi...@apache.org>.
aGiant commented on issue #17800: _MinusScalar type <class 'mxnet.ndarray.ndarray.NDArray'> not supported` ?
URL: https://github.com/apache/incubator-mxnet/issues/17800#issuecomment-597205465
Then min-max values must be prepared outsides the NN, our plan is to save the min-max as constant within at the initial stage of NN, and all trained model with be saved and transferred to other platform without any preparation of min-max values in production environment such as from Spark.
The problem based on your link was not solved.
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