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, in evaluate_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, in deco
return f()
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, in f
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] for x in i], 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, in perform
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, in amax
out=out, **kwargs)
File
"/home/PRODUITS/COTS/python2.7.9/lib/python2.7/site-packages/numpy/core/_methods.py"
, line 26, in _amax
return umr_maximum(a, axis, None, out, keepdims)
TypeError: only integer scalar arrays can be converted to a scalar index
Apply node that caused the error: MaxAndArgmax(Softmax.0, TensorConstant{(1
,) of 1})
Inputs shapes: [(100, 2), (1,)]
Inputs strides: [(16, 8), (8,)]
Inputs types: [TensorType(float64, matrix), TensorType(int64, (True,))]
Debugprint of the apply node:
MaxAndArgmax.0 [@A] <TensorType(float64, vector)> 'max'
MaxAndArgmax.1 [@A] <TensorType(int64, vector)> 'argmax'
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).
class LogisticRegression(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]
def negative_log_likelihood(self, y):
# the loss function
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
def errors(self, y):
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError(
'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
if y.dtype.startswith('int'):
# compute error
return T.mean(T.neq(self.y_pred, y))
else:
print("NotImplementedError for LogisticRegression::errors()")
raise NotImplementedError()
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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