TypeError：保存numpy文件时无法腌制_thread.lock对象

Question

我在 np.save 上收到此错误。请让我知道原因以及如何解决此问题。 以下是我的代码：

import cv2
import numpy as np
import os
from random import shuffle
from tqdm import tqdm
import tflearn
import tensorflow as tf
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout,fully_connected
from tflearn.layers.estimator import regression


TRAIN_DIR = "/Users/preetisingh/Documents/PlantsDataset/train"
TEST_DIR = "/Users/preetisingh/Documents/PlantsDataset/test"
IMG_SIZE = 50
LR = 1e-3

MODEL_NAME = "plants-{}-{}.model".format(LR, "2conv-basic")

label_dict = {
"Abiotic Sunburn": 0,
"Alternaria Brown Spot": 1,
"Anthracnose of Banana": 2,
"Anthracnose of Cotton": 3,
"Bacterial Blight of Rice": 4,
"Bacterial Blight of Cotton": 5,
"Anthracnose of Grape": 6,
"Bacterial Canker": 7,
"Banana Bract Mosaic": 8,
}


def label_img(img):
    label = label_dict[img]
    return label


def create_train_data():
    training_data = []
    for direc in tqdm(os.listdir(TRAIN_DIR)):
        j = 0
        label = direc
        disease = TRAIN_DIR + "/" + label
        if label == ".DS_Store" or label == ".git":
            continue
        for img in os.listdir(disease):
            labeldata = label_img(label)
            labeldata = tf.one_hot(labeldata, 9)
            if img == ".DS_Store":
                continue
            path = os.path.join(disease, img)
            img = cv2.resize(
                cv2.imread(path, cv2.IMREAD_GRAYSCALE), (IMG_SIZE, IMG_SIZE)
            )
            training_data.append([np.array(img), np.array(labeldata)])
            j += 1
        shuffle(training_data)
    np.save("plants_data.npy", training_data)
    return training_data

def create_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR, img)
        img_num = img.split(".")[0]
        img = cv2.resize(cv2.imread(path, cv2.IMREAD_GRAYSCALE), (IMG_SIZE, IMG_SIZE))
        testing_data.append([np.array(img), img_num])
    return testing_data


train_data = create_train_data()
test_data = create_test_data()

train = train_data[:-11]
test = train_data[-11:]
X = (
np.array([i[0] for i in train])
.reshape(-1, IMG_SIZE, IMG_SIZE, 1)
.astype(np.float32)
)
Y = [i[1] for i in train]
test_x = (
np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1).astype(np.float32)
)
test_y = [i[1] for i in test]

convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name="input")
input_layer = tf.reshape(X, [-1, IMG_SIZE, IMG_SIZE, 1])
convnet = conv_2d(input_layer, 32, 2, activation="relu")
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 2, activation="relu")
convnet = max_pool_2d(convnet, 2)
convnet = fully_connected(convnet, 32, activation="relu")
convnet = dropout(convnet, 0.4)
logits = tf.layers.dense(inputs=convnet, units=9)

convnet = fully_connected(logits, 9, activation="softmax")
convnet = regression(
logits,
optimizer="adam",
learning_rate=LR,
loss="categorical_crossentropy",
name="targets",
)
model = tflearn.DNN(logits, tensorboard_dir="log")

if os.path.exists("{}.meta".format(MODEL_NAME)):
    model.load(MODEL_NAME)
    print("model loaded")


try:
    model.fit(
    {"input": X},
    {"targets": Y},
    n_epoch=3,
    validation_set=({"input": test_x}, {"targets": test_y}),
    snapshot_step=500,
    show_metric=False,
    run_id=MODEL_NAME,
    )

except ValueError as e:
    print(e)

我是机器学习的新手，正在尝试对 9 种植物病害进行图像分类。 我在 X 中有图像张量，在 Y 中有来自 label_dict 字典的目标标签值。

这是我得到的错误：

Traceback (most recent call last):
File "hello.py", line 72, in <module>
train_data = create_train_data()
File "hello.py", line 58, in create_train_data
np.save("plants_data.npy", training_data)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/lib/npyio.py", line 521, in save
pickle_kwargs=pickle_kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/lib/format.py", line 593, in write_array
pickle.dump(array, fp, protocol=2, **pickle_kwargs)
TypeError: can't pickle _thread.lock objects

谁能帮我解决这个错误？

Answer 1

我解决了这个错误。 我对 labeldata 变量的 one_hot 编码错误，所以我更正了它。