[jira] [Created] (SINGA-444) Can not run Models' examples on Singa documentation

["thao p nguyen (JIRA)" <ji...@apache.org>]

thao p nguyen created SINGA-444:
-----------------------------------

             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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