mean（）得到意外的关键字参数“dtype”！

Question

我正在尝试使用Intel Bigdl实现图像分类。它使用mnist数据集进行分类。因为，我不想使用mnist数据集，因此我编写了以下替代方法：

图像实用工具.py

from StringIO import StringIO
from PIL import Image
import numpy as np
from bigdl.util import common
from bigdl.dataset import mnist
from pyspark.mllib.stat import Statistics

def label_img(img):
    word_label = img.split('.')[-2].split('/')[-1]
    print word_label
    # conversion to one-hot array [cat,dog]
    #                            [much cat, no dog]
    if "jobs" in word_label: return [1,0]
    #                             [no cat, very doggo]
    elif "zuckerberg" in word_label: return [0,1]

    # target is start from 0,

def get_data(sc,path):
    img_dir = path
    train = sc.binaryFiles(img_dir + "/train")
    test = sc.binaryFiles(img_dir+"/test")
    image_to_array = lambda rawdata: np.asarray(Image.open(StringIO(rawdata)))

    train_data = train.map(lambda x : (image_to_array(x[1]),np.array(label_img(x[0]))))
    test_data = test.map(lambda x : (image_to_array(x[1]),np.array(label_img(x[0]))))

    train_images = train_data.map(lambda x : x[0])
    test_images = test_data.map((lambda x : x[0]))
    train_labels = train_data.map(lambda x : x[1])
    test_labels = test_data.map(lambda x : x[1])

    training_mean = np.mean(train_images)
    training_std = np.std(train_images)
    rdd_train_images = sc.parallelize(train_images)
    rdd_train_labels = sc.parallelize(train_labels)
    rdd_test_images = sc.parallelize(test_images)
    rdd_test_labels = sc.parallelize(test_labels)

    rdd_train_sample = rdd_train_images.zip(rdd_train_labels).map(lambda (features, label):
                                        common.Sample.from_ndarray(
                                        (features - training_mean) / training_std,
                                        label + 1))
    rdd_test_sample = rdd_test_images.zip(rdd_test_labels).map(lambda (features, label):
                                        common.Sample.from_ndarray(
                                        (features - training_mean) / training_std,
                                        label + 1))

    return (rdd_train_sample, rdd_test_sample)

现在，当我尝试使用以下真实图像获取数据时：

分类.py

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我得到以下错误

TypeError Traceback (most recent call >last) in ()

2 # Get and store MNIST into RDD of Sample, please edit the "mnist_path" accordingly.

3 path = "/home/fusemachine/Hyper/person"

----> 4 (train_data, test_data) = get_data(sc,path)

5 print train_data.count()

6 print test_data.count()

/home/fusemachine/Downloads/dist-spark-2.1.0-scala-2.11.8-linux64-0.1.1-dist/imageUtils.py在get_data（sc，path）中

31 test_labels = test_data.map(lambda x : x[1])

---> 33 training_mean = np.mean(train_images)

34 training_std = np.std(train_images)

35 rdd_train_images = sc.parallelize(train_images)

/opt/anaconda3/lib/python2.7/site-packages/numpy/core/从numeric.pyc输入平均值（a，axis，dtype，out，keepdims）

2884 pass

2885 else:

-> 2886 return mean(axis=axis, dtype=dtype, out=out, **kwargs)

2887

2888 return _methods._mean(a, axis=axis, dtype=dtype,

TypeError:mean（）获得意外的关键字参数“dtype”

我想不出解决办法。还有其他的mnist数据集吗。这样我们就可以直接处理真实图像了？ 谢谢你

Answer 1

列车图像是一个rdd，你不能在rdd上应用numpy mean。一种方法是做collect（）和apply numpy mean

 train_images = train_data.map(lambda x : x[0]).collect()
 training_mean = np.mean(train_images)

或者rdd平均值（）

^{pr2}$