Dimension Error on Training the NN

[jonmilson]

Hi,

I tried to implement the same code, but changed the layers as shown:

    self.inputLayerSize = 3 
    self.outputLayerSize = 5 
    self.hiddenLayerSize = 5

This is because the data set shape is:
X = (4162, 3)
Y = (4162,)

However, after executing T.train(X, Y), I get the following error:

---

ValueError Traceback (most recent call last)
in ()
----> 1 T.train(X, Y)

in train(self, X, y)
26
27 options = {'maxiter': 200, 'disp' : True}
---> 28 _res = optimize.minimize(self.costFunctionWrapper, params0, jac=True, method='BFGS', args=(X, y), options=options, callback=self.callbackF)
29
30 self.N.setParams(_res.x)

//anaconda/lib/python2.7/site-packages/scipy/optimize/_minimize.pyc in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
439 return _minimize_cg(fun, x0, args, jac, callback, **options)
440 elif meth == 'bfgs':
--> 441 return _minimize_bfgs(fun, x0, args, jac, callback, **options)
442 elif meth == 'newton-cg':
443 return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,

//anaconda/lib/python2.7/site-packages/scipy/optimize/optimize.pyc in _minimize_bfgs(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)
845 else:
846 grad_calls, myfprime = wrap_function(fprime, args)
--> 847 gfk = myfprime(x0)
848 k = 0
849 N = len(x0)

//anaconda/lib/python2.7/site-packages/scipy/optimize/optimize.pyc in function_wrapper(*wrapper_args)
287 def function_wrapper(wrapper_args):
288 ncalls[0] += 1
--> 289 return function((wrapper_args + args))
290
291 return ncalls, function_wrapper

//anaconda/lib/python2.7/site-packages/scipy/optimize/optimize.pyc in derivative(self, x, *args)
69 return self.jac
70 else:
---> 71 self(x, *args)
72 return self.jac
73

//anaconda/lib/python2.7/site-packages/scipy/optimize/optimize.pyc in call(self, x, *args)
61 def call(self, x, *args):
62 self.x = numpy.asarray(x).copy()
---> 63 fg = self.fun(x, *args)
64 self.jac = fg[1]
65 return fg[0]

in costFunctionWrapper(self, params, X, y)
10 def costFunctionWrapper(self, params, X, y):
11 self.N.setParams(params)
---> 12 cost = self.N.costFunction(X, y)
13 grad = self.N.computeGradients(X,y)
14

in costFunction(self, X, y)
29 #Compute cost for given X,y, use weights already stored in class.
30 self.yHat = self.forward(X)
---> 31 J = 0.5*np.sum((y-self.yHat)**2)
32 return J
33

ValueError: operands could not be broadcast together with shapes (4162,) (4162,5)

Would appreciate the help!

[AnuragNagare]

I have added a new file named "Updated Part 7 Overfitting, Testing, and Regularization" in which i have updated the cost function for this problem run the code and let me know