main.py 文件源码

def show_result():
    from keras.models import load_model
    model = load_model(MODEL_PATH)
    # model.fit(x_train,y_train,validation_data=(x_train[:10],y_train[:10]),epochs=5,callbacks=[TensorBoard()],batch_size=1)

    SAMPLE_NUM = 315

    a = np.zeros(SAMPLE_NUM)
    b = np.zeros(SAMPLE_NUM)
    c = np.zeros(SAMPLE_NUM)

    real_a = np.zeros(SAMPLE_NUM)
    real_b = np.zeros(SAMPLE_NUM)
    real_c = np.zeros(SAMPLE_NUM)

    for index, y_dat in enumerate(y):
        print('Run prediction on %s' % (index))
        # model.fit(np.array([x[index]]), y_dat.reshape(1, 3),
        #           validation_data=(np.array([x[index]]), y_dat.reshape(1, 3)), epochs=10, callbacks=[TensorBoard()])
        x_pred = model.predict(np.array([x[index]]))
        print(x_pred,y_dat)
        print(x_pred.shape,y_dat.shape)
        real_a[index] = y_dat.reshape(1,3)[0][0]
        real_b[index] = y_dat.reshape(1,3)[0][1]
        real_c[index] = y_dat.reshape(1,3)[0][2]

        a[index] = x_pred[0][0]
        b[index] = x_pred[0][1]
        c[index] = x_pred[0][2]

    import matplotlib.pyplot as plt

    plt.plot(np.arange(SAMPLE_NUM), a, label='a')
    plt.plot(np.arange(SAMPLE_NUM), real_a, label='real_a')
    plt.title('A')
    plt.legend()
    plt.show()

    plt.plot(np.arange(SAMPLE_NUM), b, label='b')
    plt.plot(np.arange(SAMPLE_NUM), real_b, label='real_b')
    plt.title('B')
    plt.legend()
    plt.show()

    plt.plot(np.arange(SAMPLE_NUM), c, label='c')
    plt.plot(np.arange(SAMPLE_NUM), real_c, label='real_c')
    plt.title('C')
    plt.legend()
    plt.show()