비선형회귀 해보다가 잘 안나와서

그냥 ml 로 해보려고 하고 있는데

오류 뜨는거 어디서 나는건지 알수 있을까?

계속 머갈통 쓰니까 안보여

TypeError: object of type 'numpy.float64' has no len()

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# import tensorflow

import numpy as np

import matplotlib.pyplot as plt

from keras import models, layers

import tensorflow as tf

def build_model():

    model = models.Sequential()

    model.add(layers.Dense(2,activation = 'sigmoid', input_shape = (2,)))

    model.add(layers.Dense(1))

    optimizer=tf.keras.optimizers.RMSprop(0.001)

    model.compile(optimizer = optimizer, loss  = 'mse' ,metrics = ['mae','mae'])

    return model

def main():

    traincsvdata = np.loadtxt(r'C:\Users\JRKIM\Downloads\trainset.csv', unpack=True, delimiter=',', skiprows=1)

    num_points = len(traincsvdata[0])

    print("points : ", num_points)

    train_num_colum = len(traincsvdata)

    train_mean = []

    train_std = []

    for i in range(train_num_colum):

        train_mean.append(traincsvdata[i].mean())

        train_std.append(traincsvdata[i].std())

        traincsvdata[i] -= traincsvdata[i].mean()

        traincsvdata[i] /= traincsvdata[i].std()

        x_data = np.array([traincsvdata[0], traincsvdata[1]])

        x_data = np.array(x_data).transpose()

        y_data = traincsvdata[2]

        y_data = np.array(y_data).transpose()

    model = build_model()

    model.fit(x_data, y_data, epochs=500, batch_size=12, verbose=1, shuffle=True)

    w = model.get_weights()

    test_csv_data = np.loadtxt(r'C:\Users\JRKIM\Downloads\testset.csv', unpack=True, delimiter=',', skiprows=1)

    test_csv_data2 = np.loadtxt(r'C:\Users\JRKIM\Downloads\testset.csv', unpack=True, delimiter=',', skiprows=1)

    test_num_colum = len(test_csv_data)

    for i in range(test_num_colum):

        test_csv_data[i] -= train_mean[i]

        test_csv_data[i] /= train_std[i]

    test_x_data = np.array([test_csv_data[0], test_csv_data[1]])

    test_x_data = np.array(test_x_data).transpose()

    prediction = model.predict(test_x_data)

    prediction = train_std[2]

    prediction += train_mean[2]

    plt.plot(range(len(prediction)), prediction, 'k')

    plt.plot(range(len(prediction)), test_csv_data2[2], 'mo')

    plt.show()

    model.add(layers.Dense(1, input_shape=(2,)))

if __name__ == "__main__":

main()