from PIL import Image
import glob
import numpy as np
import matplotlib as plt
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
X = []
y = []
classes = [r"Anthracnose", r"Leaf Crinkcle", r"Powdery Mildew", r"Yellow Mosaic", r"Healthy"]
for i in range(5):
for filename in glob.glob(r"C:\-\-\-\-\-\-\\" + classes[i] + r"/*.jpg"):
image = Image.open(filename)
matrix_temp = np.array(image)
X.append(matrix_temp)
y.append(i)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)
X_train = np.array(X_train, dtype=object).reshape(-1,1)
y_train = np.array(y_train).reshape(-1,1)
X_test = np.array(X_test, dtype=object).reshape(-1,1)
y_test = np.array(y_test).reshape(-1,1)
model = MLPClassifier()
model.fit(X_train, y_train)错误：
TypeError: only size-1 arrays can be converted to Python scalars
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "c:\path_to_file\plants.py", line 32, in <module>
model.fit(X_train, y_train)
File "C:\path_to_file\_multilayer_perceptron.py", line 762, in fit
return self._fit(X, y, incremental=False)
File "C:\path_to_file\_multilayer_perceptron.py", line 394, in _fit
X, y = self._validate_input(X, y, incremental, reset=first_pass)
File "C:\path_to_file\_multilayer_perceptron.py", line 1109, in _validate_input
X, y = self._validate_data(
File "C:\path_to_file\base.py", line 596, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "C:\path_to_file\validation.py", line 1074, in check_X_y
X = check_array(
File "C:\path_to_file\validation.py", line 856, in check_array
array = np.asarray(array, order=order, dtype=dtype)
ValueError: setting an array element with a sequence.我对这个错误的含义感到困惑，不知道该尝试什么。希望得到帮助！
1条答案
按热度按时间sqxo8psd1#
看起来问题出在
X_train的形状上。X在变换之前的大小为：（number_of_samples，height，width）。MLPClassifier's拟合函数需要以下形状：（number_of_samples，number_of_features）。因此，需要通过例如连接行来将2D图像重新整形为1D向量（特征），使得输入X为：（* 样本数，高度x宽度 ）。但是，在您的示例中，您使用
reshape(-1, 1)转换X_train，这将导致以下形状：（ 样本数x高度x宽度 *）示例：假设您有两个大小为3x3的图像，我们的目标是将它们转换为1x9而不是3x3。例如，您可以使用numpy的reshape方法：
最后，请注意，每个图像必须具有相同的大小。使用不同的高度和宽度也会导致错误。
希望能帮上忙。