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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/03/11 10:43:41 UTC
[GitHub] [incubator-mxnet] aGiant commented on issue #17814:
mxnet.gluon.data.vision.transforms.Normalize(mean=0.0,
std=1.0) tuple issue within hybird_forward()
aGiant commented on issue #17814: mxnet.gluon.data.vision.transforms.Normalize(mean=0.0, std=1.0) tuple issue within hybird_forward()
URL: https://github.com/apache/incubator-mxnet/issues/17814#issuecomment-597561951
Also tried to reproduce the demo code from link: https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html, but got error.
```
# Do some initial imports used throughout this tutorial
from __future__ import print_function
import mxnet as mx
from mxnet import nd, gluon, autograd
from mxnet.gluon.nn import Dense
mx.random.seed(1)
class NormalizationHybridLayer(gluon.HybridBlock):
def __init__(self, hidden_units, scales):
super(NormalizationHybridLayer, self).__init__()
with self.name_scope():
self.weights = self.params.get('weights',
shape=(hidden_units, 0),
allow_deferred_init=True)
self.scales = self.params.get('scales',
shape=scales.shape,
init=mx.init.Constant(scales.asnumpy()),
differentiable=False)
def hybrid_forward(self, F, x, weights, scales):
normalized_data = F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
weighted_data = F.FullyConnected(normalized_data, weights, num_hidden=self.weights.shape[0], no_bias=True)
scaled_data = F.broadcast_mul(scales, weighted_data)
return scaled_data
def print_params(title, net):
"""
Helper function to print out the state of parameters of NormalizationHybridLayer
"""
print(title)
hybridlayer_params = {k: v for k, v in net.collect_params().items() if 'normalizationhybridlayer' in k }
for key, value in hybridlayer_params.items():
print('{} = {}\n'.format(key, value.data()))
net = gluon.nn.HybridSequential() # Define a Neural Network as a sequence of hybrid blocks
with net.name_scope(): # Used to disambiguate saving and loading net parameters
net.add(Dense(5)) # Add Dense layer with 5 neurons
net.add(NormalizationHybridLayer(hidden_units=5,
scales = nd.array([2]))) # Add our custom layer
net.add(Dense(1)) # Add Dense layer with 1 neurons
net.initialize(mx.init.Xavier(magnitude=2.24)) # Initialize parameters of all layers
net.hybridize() # Create, optimize and cache computational graph
inputs = nd.random_uniform(low=-10, high=10, shape=(5, 2)) # Create 5 random examples with 2 feature each in range [-10, 10]
label = nd.random_uniform(low=-1, high=1, shape=(5, 1))
mse_loss = gluon.loss.L2Loss() # Mean squared error between output and label
trainer = gluon.Trainer(net.collect_params(), # Init trainer with Stochastic Gradient Descent (sgd) optimization method and parameters for it
'sgd',
{'learning_rate': 0.1, 'momentum': 0.9 })
with autograd.record(): # Autograd records computations done on NDArrays inside "with" block
output = net(inputs) # Run forward propogation
print_params("=========== Parameters after forward pass ===========\n", net)
loss = mse_loss(output, label) # Calculate MSE
loss.backward() # Backward computes gradients and stores them as a separate array within each NDArray in .grad field
trainer.step(inputs.shape[0]) # Trainer updates parameters of every block, using .grad field using oprimization method (sgd in this example)
# We provide batch size that is used as a divider in cost function formula
print_params("=========== Parameters after backward pass ===========\n", net)
```
Error raised:
```
TypeError Traceback (most recent call last)
<ipython-input-45-289ed4b30586> in <module>
29
30 with autograd.record(): # Autograd records computations done on NDArrays inside "with" block
---> 31 output = net(inputs) # Run forward propogation
32
33 print_params("=========== Parameters after forward pass ===========\n", net)
~/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():
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in forward(self, x, *args)
913 with x.context as ctx:
914 if self._active:
--> 915 return self._call_cached_op(x, *args)
916
917 try:
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in _call_cached_op(self, *args)
803 def _call_cached_op(self, *args):
804 if self._cached_op is None:
--> 805 self._build_cache(*args)
806
807 args, fmt = _flatten(args, "input")
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in _build_cache(self, *args)
755
756 def _build_cache(self, *args):
--> 757 data, out = self._get_graph(*args)
758 data_names = {data.name : i for i, data in enumerate(data)}
759 params = self.collect_params()
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in _get_graph(self, *args)
747 params = {i: j.var() for i, j in self._reg_params.items()}
748 with self.name_scope():
--> 749 out = self.hybrid_forward(symbol, *grouped_inputs, **params) # pylint: disable=no-value-for-parameter
750 out, self._out_format = _flatten(out, "output")
751
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/nn/basic_layers.py in hybrid_forward(self, F, x)
115 def hybrid_forward(self, F, x):
116 for block in self._children.values():
--> 117 x = block(x)
118 return x
119
~/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():
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in forward(self, x, *args)
928 "HybridBlock requires the first argument to forward be either " \
929 "Symbol or NDArray, but got %s"%type(x)
--> 930 params = {i: j.var() for i, j in self._reg_params.items()}
931 with self.name_scope():
932 return self.hybrid_forward(symbol, x, *args, **params)
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/block.py in <dictcomp>(.0)
928 "HybridBlock requires the first argument to forward be either " \
929 "Symbol or NDArray, but got %s"%type(x)
--> 930 params = {i: j.var() for i, j in self._reg_params.items()}
931 with self.name_scope():
932 return self.hybrid_forward(symbol, x, *args, **params)
~/anaconda3/lib/python3.7/site-packages/mxnet/gluon/parameter.py in var(self)
602 self._var = symbol.var(self.name, shape=self.shape, dtype=self.dtype,
603 lr_mult=self.lr_mult, wd_mult=self.wd_mult,
--> 604 init=self.init, stype=self._stype)
605 return self._var
606
~/anaconda3/lib/python3.7/site-packages/mxnet/symbol/symbol.py in var(name, attr, shape, lr_mult, wd_mult, dtype, init, stype, **kwargs)
2649 if init is not None:
2650 if not isinstance(init, string_types):
-> 2651 init = init.dumps()
2652 attr['__init__'] = init
2653 if stype is not None:
~/anaconda3/lib/python3.7/site-packages/mxnet/initializer.py in dumps(self)
114 '["xavier", {"rnd_type": "uniform", "magnitude": 2.34, "factor_type": "in"}]'
115 """
--> 116 return json.dumps([self.__class__.__name__.lower(), self._kwargs])
117
118 def __call__(self, desc, arr):
~/anaconda3/lib/python3.7/json/__init__.py in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
229 cls is None and indent is None and separators is None and
230 default is None and not sort_keys and not kw):
--> 231 return _default_encoder.encode(obj)
232 if cls is None:
233 cls = JSONEncoder
~/anaconda3/lib/python3.7/json/encoder.py in encode(self, o)
197 # exceptions aren't as detailed. The list call should be roughly
198 # equivalent to the PySequence_Fast that ''.join() would do.
--> 199 chunks = self.iterencode(o, _one_shot=True)
200 if not isinstance(chunks, (list, tuple)):
201 chunks = list(chunks)
~/anaconda3/lib/python3.7/json/encoder.py in iterencode(self, o, _one_shot)
255 self.key_separator, self.item_separator, self.sort_keys,
256 self.skipkeys, _one_shot)
--> 257 return _iterencode(o, 0)
258
259 def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,
~/anaconda3/lib/python3.7/json/encoder.py in default(self, o)
177
178 """
--> 179 raise TypeError(f'Object of type {o.__class__.__name__} '
180 f'is not JSON serializable')
181
TypeError: Object of type ndarray is not JSON serializable
```
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