thao p nguyen created SINGA-444:
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Summary: Can not run Models' examples on Singa documentation
Key: SINGA-444
URL: https://issues.apache.org/jira/browse/SINGA-444
Project: Singa
Issue Type: Bug
Components: Documentation
Environment: - python 3.6.8
- Ubuntu 18.10
Reporter: thao p nguyen
Following the Singa documentation, the API code for running models' example
does not work. Below are messages:
1) FeedForward Net
>>> from singa import tensor
>>> from singa import loss
>>> x = tensor.Tensor((3, 5))
>>> x.uniform(0, 1) # randomly genearte the prediction activation
>>> y = tensor.from_numpy(np.array([0, 1, 3], dtype=np.int)) # set the truth
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'np' is not defined
>>> f = loss.SoftmaxCrossEntropy()
>>> l = f.forward(True, x, y) # l is tensor with 3 loss values
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'y' is not defined
>>> g = f.backward() # g is a tensor containing all gradients of x w.r.t l
Segmentation fault (core dumped)
2) Loss
>>> from singa import tensor
>>> from singa import loss
>>>
>>> x = tensor.Tensor((3, 5))
>>> x.uniform(0, 1) # randomly genearte the prediction activation
>>> y = tensor.from_numpy(np.array([0, 1, 3], dtype=np.int)) # set the truth
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'np' is not defined
>>>
>>> f = loss.SoftmaxCrossEntropy()
>>> l = f.forward(True, x, y) # l is tensor with 3 loss values
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'y' is not defined
>>> g = f.backward() # g is a tensor containing all gradients of x w.r.t l
3) >>> from singa import tensor
>>> from singa import metric
>>>
>>> x = tensor.Tensor((3, 5))
>>> x.uniform(0, 1) # randomly genearte the prediction activation
>>> x = tensor.SoftMax(x) # normalize the prediction into probabilities
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'singa.tensor' has no attribute 'SoftMax'
>>> y = tensor.from_numpy(np.array([0, 1, 3], dtype=np.int)) # set the truth
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'np' is not defined
>>>
>>> f = metric.Accuracy()
>>> acc = f.evaluate(x, y) # averaged accuracy over all 3 samples in x
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