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https://issues.apache.org/jira/browse/SINGA-444?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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thao p nguyen resolved SINGA-444.
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Resolution: Fixed
> Can not run Model classes 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
> Priority: Critical
>
> 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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