def new_acc(nn, data, label, thr = 0.5): predict = [] for i in range(len(data)): print nnS.forward(nn,data[i]) if np.argmax(nnS.forward(nn,data[i])) == label[i]: predict.append(True) else: predict.append(False) return 100 * np.double(len(np.where(np.array(predict) == False)[0])) / np.double(len(predict))
def accuracy(nn, data, label, thr = 0.5): #print "start" #print np.int8(nnS.forward(nn,data[1500].T) > .05) #print label[1500] acc = 0 for i in range(len(data)): x = np.int8(nnS.forward(nn,data[i].T) > thr) if np.array_equal(x, label_clean(label[i])): acc += 1 return float(acc) / float(len(data))
def accuracy(nn, data, label, thr=0.5): #print "start" #print np.int8(nnS.forward(nn,data[1500].T) > .05) #print label[1500] acc = 0 for i in range(len(data)): x = np.int8(nnS.forward(nn, data[i].T) > thr) if np.array_equal(x, label_clean(label[i])): acc += 1 return float(acc) / float(len(data))