gradient checking

check_grad.py

def checkGradient(nn_initial_params,nn_backprop_Params,input_layer_size, hidden_layer_size, num_labels,myX,myy,mylambda=0.):
    myeps = 0.0001
    flattened = nn_initial_params
    flattenedDs = nn_backprop_Params
    n_elems = len(flattened) 
    #Pick ten random elements, compute numerical gradient, compare to respective D's
    for i in range(10):
        x = int(np.random.rand()*n_elems)
        epsvec = np.zeros((n_elems,1))
        epsvec[x] = myeps

        cost_high = nnCostFunc(flattened + epsvec.flatten(),input_layer_size, hidden_layer_size, num_labels,myX,myy,mylambda)
        cost_low  = nnCostFunc(flattened - epsvec.flatten(),input_layer_size, hidden_layer_size, num_labels,myX,myy,mylambda)
        mygrad = (cost_high - cost_low) / float(2*myeps)
        print("Element: {0}. Numerical Gradient = {1:.9f}. BackProp Gradient = {2:.9f}.".format(x,mygrad,flattenedDs[x]))