You seem to be using Theano 0.6.0.
Only version 1.0.1 is still supported.
Does the error still occur with 1.0.1?
On 2018-02-21 11:26 AM, Loïc wrote:
Hi,
I have downloaded an open source code which uses theano
(https://www.repository.cam.ac.uk/handle/1810/263961)
When I try running the learning step I get an error (I have set theano
verbosity to high).
python train_bnn.py -i lytroPatches_30x30.pkl.gz
|
Traceback(most recent call last):
File"train_bnn.py",line 525,in<module>
evaluate_dcnn(dataset=dataset_file,nkerns=kernels)
File"train_bnn.py",line 440,inevaluate_dcnn
print("res = {}".format(validate_model(0)))
File"/home/PRODUITS/COTS/python2.7.9/lib/python2.7/site-packages/Theano-0.6.0-py2.7.egg/theano/compile/function_module.py",line
579,in__call__
outputs =self.fn()
File"/home/PRODUITS/COTS/python2.7.9/lib/python2.7/site-packages/Theano-0.6.0-py2.7.egg/theano/compile/debugmode.py",line
2030,indeco
returnf()
File"/home/PRODUITS/COTS/python2.7.9/lib/python2.7/site-packages/Theano-0.6.0-py2.7.egg/theano/compile/debugmode.py",line
1790,inf
thunk_py()
File"/home/PRODUITS/COTS/python2.7.9/lib/python2.7/site-packages/Theano-0.6.0-py2.7.egg/theano/compile/debugmode.py",line
1623,in<lambda>
n=node:p(n,[x[0]forx ini],o))
File"/home/PRODUITS/COTS/python2.7.9/lib/python2.7/site-packages/Theano-0.6.0-py2.7.egg/theano/tensor/basic.py",line
1418,inperform
max[0]=theano._asarray(numpy.max(x,axis),
File"/home/PRODUITS/COTS/python2.7.9/lib/python2.7/site-packages/numpy/core/fromnumeric.py",line
2320,inamax
out=out,**kwargs)
File"/home/PRODUITS/COTS/python2.7.9/lib/python2.7/site-packages/numpy/core/_methods.py",line
26,in_amax
returnumr_maximum(a,axis,None,out,keepdims)
TypeError:only integer scalar arrays can be converted to a scalar index
Applynode that caused the
error:MaxAndArgmax(Softmax.0,TensorConstant{(1,)of 1})
Inputsshapes:[(100,2),(1,)]
Inputsstrides:[(16,8),(8,)]
Inputstypes:[TensorType(float64,matrix),TensorType(int64,(True,))]
Debugprintof the apply node:
MaxAndArgmax.0[@A]<TensorType(float64,vector)>'max'
MaxAndArgmax.1[@A]<TensorType(int64,vector)>'argmax'
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Now I am really new to Theano and do not even have dnn experience which
makes it hard to understand the messages I get from Theano and to debug
this code...
As far as I know this could even be not related to Theano (though I hope
I am not wasting your time).
What I think I have understood is that the train_model theano.function
goes well. But the validate_model and test_model theano.function do not.
I think this is due to the errors() method of the LogisticRegression
layer (the negative_log_likelihood is used what I guess is called the
cost function for the train_model, while the errors one is the function
that is supposed to test how well the dnn has learnt so far).
|
classLogisticRegression(object):
''' Logistic regression layer
'''
# initialization
def__init__(self,input,n_in,n_out):
# initialize the weight matrix to zeros
self.W =theano.shared(value=np.zeros((n_in,n_out),
dtype=theano.config.floatX),
name='W',borrow=True)
# initialize the bias term
self.b =theano.shared(value=np.zeros((n_out,),
dtype=theano.config.floatX),
name='b',borrow=True)
# define symbolic expressions for probability and prediction
self.p_y_given_x =T.nnet.softmax(T.dot(input,self.W)+self.b)
self.y_pred =T.argmax(self.p_y_given_x,axis=1)
self.params=[self.W,self.b]
defnegative_log_likelihood(self,y):
# the loss function
return-T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
deferrors(self,y):
# check if y has same dimension of y_pred
ify.ndim !=self.y_pred.ndim:
raiseTypeError(
'y should have the same shape as self.y_pred',
('y',y.type,'y_pred',self.y_pred.type)
)
# check if y is of the current data type
ify.dtype.startswith('int'):
# compute error
returnT.mean(T.neq(self.y_pred,y))
else:
print("NotImplementedError for LogisticRegression::errors()")
raiseNotImplementedError()
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So here I come to you hopping that maybe you will have hints or clues to
help me find out where this error comes from.
Could it be related to the shapes of the tensors ? How can I access them
? I have truble playing with tensors so far...
I really thank you for the time you will spend reading this and
answering it.
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