神经网络输入层4个神经元，隐藏层4个神经元，输出层1个神经元，这段代码报错数据维度错误，应该怎么改def result_visualization(x_test, y_test, result): cols = y_test.shape[1] y = [] pre = [] # 反转换类别的独热编码 for i in range(cols): if y_test[0][i] == 0: y.append('setosa') elif y_test[0][i] == 1: y.append('versicolor') else: y.append('virginica') for j in range(cols): if result[0][j] == 0: pre.append('setosa') elif result[0][j] == 1: pre.append('versicolor') else: pre.append('virginica') # 将特征和类别矩阵拼接起来 real = np.column_stack((x_test.T, y)) prediction = np.column_stack((x_test.T, pre)) # 转换成DataFrame类型，并添加columns df_real = pd.DataFrame(real, index=None, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']) df_prediction = pd.DataFrame(prediction, index=None, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']) # 将特征列转换为float类型，否则radviz会报错 df_real[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']] = df_real[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']].astype(float) df_prediction[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']] = df_prediction[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']].astype(float) # 绘图 plt.figure('真实分类') radviz(df_real, 'Species', color=['blue', 'green', 'red']) plt.figure('预测分类') radviz(df_prediction, 'Species', color=['blue', 'green', 'red']) plt.show()

可能是因为y_test的维度不是二维的，而是一维的，导致使用shape属性获取y_test的列数出错。可以在代码中加入以下语句，将y_test转换为二维的形式：

if len(y_test.shape) == 1:
    y_test = y_test.reshape(-1, 1)

这段代码的作用是：如果y_test的维度是一维的，将其转换为二维的形式，行数自动计算。如果y_test的维度已经是二维的，不做任何操作。修改后的完整代码如下：

def result_visualization(x_test, y_test, result):
    if len(y_test.shape) == 1:
        y_test = y_test.reshape(-1, 1)
    cols = y_test.shape[1]
    y = []
    pre = []
    # 反转换类别的独热编码
    for i in range(cols):
        if y_test[0][i] == 0:
            y.append('setosa')
        elif y_test[0][i] == 1:
            y.append('versicolor')
        else:
            y.append('virginica')
    for j in range(cols):
        if result[0][j] == 0:
            pre.append('setosa')
        elif result[0][j] == 1:
            pre.append('versicolor')
        else:
            pre.append('virginica')
    # 将特征和类别矩阵拼接起来
    real = np.column_stack((x_test.T, y))
    prediction = np.column_stack((x_test.T, pre))
    # 转换成DataFrame类型，并添加columns
    df_real = pd.DataFrame(real, index=None, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Species'])
    df_prediction = pd.DataFrame(prediction, index=None, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Species'])
    # 将特征列转换为float类型，否则radviz会报错
    df_real[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']] = df_real[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']].astype(float)
    df_prediction[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']] = df_prediction[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']].astype(float)
    # 绘图
    plt.figure('真实分类')
    radviz(df_real, 'Species', color=['blue', 'green', 'red'])
    plt.figure('预测分类')
    radviz(df_prediction, 'Species', color=['blue', 'green', 'red'])
    plt.show()