Would you mind sharing how you fix the problem? I am having the same error,
and I also tried flatten. However it doesn't work. What is the post?
On Friday, May 8, 2015 at 12:32:32 PM UTC-4, JPs wrote:
>
> I fixed this error using flatten on my y np.array as described in a
> different post.
>
> I.e test_raw_y.flatten()
>
>
> On Thursday, May 7, 2015 at 5:31:10 PM UTC-7, JPs wrote:
>>
>> To be more specific, I am getting the same error as the first poster:
>>
>> ... building the model
>> Traceback (most recent call last):
>> File "logistic_sgd.py", line 479, in <module>
>> sgd_optimization_mnist()
>> File "logistic_sgd.py", line 350, in sgd_optimization_mnist
>> y: test_set_y[index * batch_size: (index + 1) * batch_size]
>>
>> TypeError: Cannot convert Type TensorType(int32, matrix) (of Variable
>> Subtensor{int64:int64:}.0) into Type TensorType(int32, vector). You can try
>> to manually convert Subtensor{int64:int64:}.0 into a TensorType(int32,
>> vector).
>>
>>
>>
>> On Thursday, May 7, 2015 at 5:28:32 PM UTC-7, JPs wrote:
>>>
>>> I modified the tutorial to read in from a .csv file. I think the problem
>>> is the I am importing test_set_y as an np.array which automatically creates
>>> a matrix instead of a vector?
>>>
>>> test_raw = list(csv.reader(open("../training.csv","rb"), delimiter=','))
>>> test_raw_y = np.array([map(str, row[-1]) for row in
>>> test_raw[int(0.8*num_rows):int(num_rows)]])
>>> test_raw_y = np.where(test_raw_y == 's', 1,0)
>>>
>>> I also get a warning about fblas
>>>
>>> DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use
>>> scipy.linalg.blas instead DeprecationWarning)
>>>
>>>
>>> On Tuesday, January 20, 2015 at 9:56:23 AM UTC-8, Pascal Lamblin wrote:
>>>>
>>>> On Mon, Jan 19, 2015, bansari shah wrote:
>>>> > I am facing the same problem. But adding this import doesn't help me.
>>>> Can
>>>> > someone please suggest something else?
>>>>
>>>> Which problem exactly?
>>>> The warning message regarding blas?
>>>> Or the TypeError?
>>>>
>>>> Also, did you modify the tutorial at all?
>>>>
>>>> --
>>>> Pascal
>>>>
>>>
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