线程Thread-2中的异常：Python机器学习错误：列表超出Tensorflow范围

Question

我正在尝试对我的数据进行分类。但是作为机器学习和Python方面的新手，我一直遇到一个奇怪的错误，我无法弄清。我的代码是

CODE

from sklearn.preprocessing import OneHotEncoder
import tensorflow as tf
import numpy as np
import scipy.io as cio
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpg
from random import shuffle
import tflearn
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
import cv2

a = cio.loadmat("D:/compCarsThesisData/data/misc/make_model_name.mat")

images = "D:/compCarsThesisData/data/image/"
IMG_SIZE = 64
MODEL_NAME = 'Classification'
LR = 1e-3

b = a['make_names']

d = []

for i in range(b.size):
  d.append(b[i][0][0])
  print(d)

labels_dic = {v: k for v, k in enumerate(d)}
print(labels_dic)
indices = np.arange(163)
depth = 163

y = tf.one_hot(indices,depth)

sess = tf.Session()

result = sess.run(y)#,feed_dict=None,options=None, run_metadata=labels_dic)
print(result)
labels = []

labels.append((result,labels_dic))
print(labels)


for root, _, files in os.walk(images):
 cdp = os.path.abspath(root)
 for f in files:
     name,ext = os.path.splitext(f)
     if ext == ".jpg":
       cip = os.path.join(cdp,f)
       ci = mpg.imread(cip)
       image = cv2.cv2.resize(ci,(IMG_SIZE,IMG_SIZE))
       image = np.array(image)
       print(image)


 training_data = []

 training_data.append((image,labels))
 shuffle(training_data)
 np.save('training_data_make_model', training_data)

 testing_data = []
 testing_data.append((image,labels))
 print("TestingDATA",testing_data)

 shuffle(testing_data)

# if the data already created
# training_data = np.load('training_data_make_model.npy')
# testing_data = np.load('training_data_make_model.npy')

train = training_data[:-50000]
test = testing_data[-50000:]

X_train = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)

y_train = [i[1] for i in train]

X_test = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)
y_test = [i[1] for i in test]
print("YTEST",y_test)



tf.reset_default_graph()
convnet = input_data(shape=[None,IMG_SIZE,IMG_SIZE,3],name='input')
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log', tensorboard_verbose=0)
model.fit({'input': X_train}, {'targets': y_train}, n_epoch=10,
          validation_set=0.1,
          snapshot_step=500, show_metric=True, run_id=MODEL_NAME)

我的.mat文件的快照是

。mat文件包含单元格数组，就目前而言，我正在使用['make_names']，其中将汽车名称作为标签，并将它们转换为一种热编码形式，并将它们与训练数据一起附加到上面，如您所见。

。mat文件

带有标签和.mat文件的附加数据看起来像这样

带有一个热编码的标签

[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 [0. 0. 0. ... 0. 0. 1.]]
[(array([[1., 0., 0., ..., 0., 0., 0.],
       [0., 1., 0., ..., 0., 0., 0.],
       [0., 0., 1., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 1., 0., 0.],
       [0., 0., 0., ..., 0., 1., 0.],
       [0., 0., 0., ..., 0., 0., 1.]], dtype=float32), {0: 'ABT', 1: 'BAC', 2: 'Conquest', 3: 'DS', 4: 'Dacia', 5: 'Fisker', 6: 'GMC', 7: 'Gumpert', 8: 'Hennessey', 9: 'Icona', 10: 'Jeep', 11: 'KTM', 12: 'MELKUS', 13: 'MG', 14: 'MINI', 15: 'Mazzanti', 16: 'Noble', 17: 'PGO', 18: 'SPIRRA', 19: 'SSC', 20: 'Scion', 21: 'TESLA', 22: 'TVR', 23: 'Tramontana', 24: 'Zenvo', 25: 'smart', 26: 'Yiqi', 27: 'Mitsubishi', 28: 'Shangqidatong', 29: 'Spyker N.V.', 30: 'Dongnan', 31: 'Dongfeng', 32: 'Dongfengxiaokang', 33: 'Dongfengfengdu', 34: 'Dongfengfengshen', 35: 'Dongfengfengxing', 36: 'Zxauto', 37: 'Zhonghua', 38: 'Toyota', 39: 'Zinoro', 40: 'Jiulong', 41: 'Isuzu', 42: 'Wuling', 43: 'AC Chnitzer', 44: 'Zoyte', 45: 'Iveco', 46: 'Bufori', 47: 'Porsche', 48: 'Mitsuoka', 49: 'Chrysler', 50: 'Lamorghini ', 51: 'Kombat', 52: 'Cadillac', 53: 'Buck', 54: 'Lifan', 55: 'Lorinser', 56: 'Rolls-Royce', 57: 'BAW', 58: 'Baihc', 59: 'Beiqiweiwang', 60: 'Beiqihuansu', 61: 'Beiqi New Energy', 62: 'Huapu', 63: 'Huatai', 64: 'Huaqi', 65: 'Carlsson', 66: 'Shuanghuan', 67: 'Shuanglong', 68: 'Geely', 69: 'Venucia', 70: 'Haval', 71: 'Hafei', 72: 'Volkswagen', 73: 'Daihatsu', 74: 'Chrey', 75: 'Besturn', 76: 'Benz', 77: 'Audi', 78: 'Wisemann', 79: 'Wealeak', 80: 'BWM', 81: 'Baojun', 82: 'Bentley', 83: 'Brabus', 84: 'Bugatti', 85: 'Pagani', 86: 'Guangqichuanqi', 87: 'GAC', 88: 'Karry', 89: 'Ciimo', 90: 'CHTC', 91: 'Jaguar', 92: 'Morgan', 93: 'Subaru', 94: 'Skoda', 95: 'Xinkai', 96: 'Nissan', 97: 'Changhe', 98: 'RANZ', 99: 'Honda', 100: 'Lincoln', 101: 'Peugeot', 102: 'Opel', 103: 'Oley', 104: 'BYD', 105: 'Jonway', 106: 'Huizhong', 107: 'Jianghuai', 108: 'Jiangling', 109: 'Vauxhall', 110: 'Volvo', 111: 'Ferrari', 112: 'Haige', 113: 'Haima', 114: 'Haima(Zhengzhou)', 115: 'Cheetah', 116: 'Maserati', 117: 'Hyundai ', 118: 'Everus', 119:
'Ruiqi', 120: 'Fuqiqiteng', 121: 'Ford', 122: 'Futian', 123: 'Fudi', 124: 'Koenigsegg', 125: 'HongQi', 126: 'Luxgen', 127: 'SAAB', 128: 'Denza', 129: 'Yingzhi', 130: 'Infiniti', 131: 'Roewe', 132: 'Lotus', 133: 'FIAT', 134: 'Saab', 135: 'Lancia', 136: 'Seat', 137: 'Qoros', 138: 'Acura', 139: 'KIA', 140: 'Lotus', 141: 'LAND-ROVER', 142: 'McLaren', 143: 'Maybach', 144: 'Dodge', 145: 'Mustang', 146: 'Jinlv', 147: 'Jinbei', 148: 'Suzuki', 149: 'GreatWall', 150: 'Changan Business', 151: 'Changan', 152: 'Alfa Romeo', 153: 'Aston Martin', 154: 'Lufeng', 155: 'Shanqitongjia', 156: 'Chevy', 157: 'Citroen', 158: 'Lexus', 159: 'Renault', 160: 'Shouwang', 161: 'MAZDA', 162: 'Huanghai'})]

但是每次我尝试运行此命令时，都会出现类似的错误。

ERROR

Run id: Classification
Log directory: log/
Exception in thread Thread-2:
Traceback (most recent call last):
  File "C:\Users\zeele\Miniconda3\lib\threading.py", line 916, in _bootstrap_inner

    self.run()
  File "C:\Users\zeele\Miniconda3\lib\threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)                                     1, in fill_batch_ids_queue
  File "C:\Users\zeele\Miniconda3\lib\site-packages\tflearn\data_flow.py", line 201, in fill_batch_ids_queue                                                        5, in next_batch_ids
    ids = self.next_batch_ids()
  File "C:\Users\zeele\Miniconda3\lib\site-packages\tflearn\data_flow.py", line 215, in next_batch_ids
    batch_start, batch_end = self.batches[self.batch_index]
IndexError: list index out of range

Answer 1

参考此：https://github.com/fizyr/keras-retinanet/issues/857您应该在model.fit中使用worker = 0。这也可能是因为依赖项安装不正确。