我正试图用Python w/TF制作一个植物分类程序，但我遇到了标题中提到的错误。
当我构建&运行程序：
5539
2021-06-02 12:33:37.608529: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll
Traceback (most recent call last):
File "C:UsersUserDesktopAlphaTangomyclassifier.py", line 9, in <module>
(feature, labels) = load_data()
File "C:UsersUserDesktopAlphaTangoutils.py", line 46, in load_data
data = pick.load(pick)
AttributeError: '_io.BufferedReader' object has no attribute 'load'
我还在第9行得到了一个红旗(带有"(feature，labels(=load_data(("的行；，陈述"；文件"；C： \Users\User\Desktop\AlphaTango\myclassifier.py"；，第9行，in(
代码本身相当长，但我将把它粘贴在下面；希望它能帮助你识别问题并帮助我：
from utils import load_data
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
(feature, labels) = load_data()
x_train, x_test, y_train, y_test = train_test_split(feature, labels, test_size = 0.1)
categories = ['black-grass', 'charlock', 'cleavers', 'common chickweed', 'common wheat', 'fat hen', 'loose silky-bent', 'maize', 'scentless mayweed', 'shepherds purse', 'small-flowered cranesbill', 'sugar beet']
input_layer = tf.keras.layers.Input([224, 224, 3])
conv1=tf.kears.layers.Conv2D(filters= 32, kernel_size=(5, 5), padding='Same',
activation ='relu')(input_layer)
pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = tf.keras.layers.Conv2D(filters= 64, kernel_size=(3,3), padding='Same',
activation='relu')(pool1)
pool2 = tf.keras.layers.MaxPooling2D(pool_size(2,2), strides=(2,2))(conv2)
conv3 = tf.keras.layers.Conv2D(filters = 96, kernel_size=(3,3),padding='Same',
activation='relu')(pool2)
pool3 = tf.keras.layers.MaxPooling2D(pool_size=(2,2), strides=(2,2))(conv3)
conv4 = tf.keras.layers.Conv2D(filters = 96, kernel_size= (3,3),padding='Same',
activation= 'relu')(pool3)
pool4 = tf.keras.layers.MaxPooling2D(pool_size(2,2), strides=(2,2))(conv4)
flt1 = tf.keras.layers.Flatten()(pool4)
dn1 = tf.keras.layers.Dense(512, activation='relu')(flt1)
out = tf.kera.layers.Dense(5, activation='softmax')(dn1)
model = tf.keras.Model(input_layer, out)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size = 100, epochs =10)
model.save('mymodel.h5')
编辑：哦，还有一件事；我在构建解决方案时遵循了本教程：https://www.youtube.com/watch?v=POO1gdUJ7yE&t=995s
然而，他的代码可以工作，而我的代码不能
编辑2：添加了其他文件的代码：
import os
import numpy as np
import matplotlib.pyplot as plt
import cv2
import pickle
data_dir = './data/plants'
categories = ['black-grass', 'charlock', 'cleavers', 'common chickweed', 'common wheat', 'fat hen', 'loose silky-bent', 'maize', 'scentless mayweed', 'shepherds purse', 'small-flowered cranesbill', 'sugar beet']
data = []
def make_data():
for category in categories:
path = os.path.join(data_dir, category) # ./data/plants/black-grass"
label = categories.index(category)
for img_name in os.listdir(path):
image_path = os.path.join(path, img_name)
image = cv2.imread(image_path)
try:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (224, 224))
image = np.array(image, dtype=np.float32)
data.append([image, label])
except Exception as e:
pass
print(len(data))
pik = open('data.pickle', 'wb')
pickle.dump(data, pik)
pik.close()
make_data()
def load_data():
pick = open('data.pickle', 'rb')
data = pick.load(pick)
pick.close()
np.random.shuffle(data)
feature = []
labels = []
for img, label in data:
feature.append(img)
labels.append(label)
feature = np.array(feature, dtype=np.float32)
labels = np.array(labels)
feature = feature/255.0
return [feature, labels]
您在此处输入了一个错误：
pick = open('data.pickle', 'rb')
data = pick.load(pick) # this line
您使用的不是pickle来加载文件，而是pick。正确的版本是：
pick = open('data.pickle', 'rb')
data = pickle.load(pick)