Anaconda/jupyter aracılığıyla

Question

Hu adamları, MNIST veritabanı alınamıyor vb

Ben piton/anakonda/jupyter/numpy, panda yeniyim, .... bu gerçekten aptalca bir soru eğer öyleyse İzninizle. Anaconda/jupyter kullanarak MNIST veritabanını elde etmeye çalışıyorum. Ama her seferinde sonunda bir 500 hatası alıyorum. Gerçekten bir sunucu problemi mi (500’ün önerisi) yoksa yanlış bir şey mi yapıyorum? jupyter içinde

Girdi:

from sklearn.datasets import fetch_mldata 
mnist = fetch_mldata('MNIST original') 

Sonuç:

--------------------------------------------------------------------------- 
    HTTPError         Traceback (most recent call last) 
    <ipython-input-1-15dc285fb373> in <module>() 
      1 from sklearn.datasets import fetch_mldata 
    ----> 2 mnist = fetch_mldata('MNIST original') 

    e:\ProgramData\Anaconda3\lib\site-packages\sklearn\datasets\mldata.py in fetch_mldata(dataname, target_name, data_name, transpose_data, data_home) 
     140   urlname = MLDATA_BASE_URL % quote(dataname) 
     141   try: 
    --> 142    mldata_url = urlopen(urlname) 
     143   except HTTPError as e: 
     144    if e.code == 404: 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context) 
     221  else: 
     222   opener = _opener 
    --> 223  return opener.open(url, data, timeout) 
     224 
     225 def install_opener(opener): 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in open(self, fullurl, data, timeout) 
     530   for processor in self.process_response.get(protocol, []): 
     531    meth = getattr(processor, meth_name) 
    --> 532    response = meth(req, response) 
     533 
     534   return response 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in http_response(self, request, response) 
     640   if not (200 <= code < 300): 
     641    response = self.parent.error(
    --> 642     'http', request, response, code, msg, hdrs) 
     643 
     644   return response 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in error(self, proto, *args) 
     562    http_err = 0 
     563   args = (dict, proto, meth_name) + args 
    --> 564   result = self._call_chain(*args) 
     565   if result: 
     566    return result 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args) 
     502   for handler in handlers: 
     503    func = getattr(handler, meth_name) 
    --> 504    result = func(*args) 
     505    if result is not None: 
     506     return result 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in http_error_302(self, req, fp, code, msg, headers) 
     754   fp.close() 
     755 
    --> 756   return self.parent.open(new, timeout=req.timeout) 
     757 
     758  http_error_301 = http_error_303 = http_error_307 = http_error_302 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in open(self, fullurl, data, timeout) 
     530   for processor in self.process_response.get(protocol, []): 
     531    meth = getattr(processor, meth_name) 
    --> 532    response = meth(req, response) 
     533 
     534   return response 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in http_response(self, request, response) 
     640   if not (200 <= code < 300): 
     641    response = self.parent.error(
    --> 642     'http', request, response, code, msg, hdrs) 
     643 
     644   return response 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in error(self, proto, *args) 
     568   if http_err: 
     569    args = (dict, 'default', 'http_error_default') + orig_args 
    --> 570    return self._call_chain(*args) 
     571 
     572 # XXX probably also want an abstract factory that knows when it makes 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args) 
     502   for handler in handlers: 
     503    func = getattr(handler, meth_name) 
    --> 504    result = func(*args) 
     505    if result is not None: 
     506     return result 

    e:\ProgramData\Anaconda3\lib\urllib\request.py in http_error_default(self, req, fp, code, msg, hdrs) 
     648 class HTTPDefaultErrorHandler(BaseHandler): 
     649  def http_error_default(self, req, fp, code, msg, hdrs): 
    --> 650   raise HTTPError(req.full_url, code, msg, hdrs, fp) 
     651 
     652 class HTTPRedirectHandler(BaseHandler): 

    HTTPError: HTTP Error 500: INTERNAL SERVER ERROR 

Answer 1

Ben de sizinle aynı hatayı alıyorum. İşte bu sunucu gerektirmeyen bazı olası çözümler. Eğer tensorflow yüklüyse

, aşağıdaki şekilde MNIST veri alabilir:

örneğin len(m.train.images) için Ardından

import tensorflow.examples.tutorials.mnist.input_data as input_data 
m=input_data.read_data_sets("MNIST") 

55000.

size tensorflow yoksa, bu alabilirsiniz yönergeleri here kullanarak veri kümesi. https://github.com/Lasagne/Lasagne/blob/master/examples/mnist.py

O Yan LeCun web sitesinde (http://yann.lecun.com/exdb/mnist/) den veri kümesi indirir:

Answer 2

İşte (https://github.com/ageron/handson-ml/blob/master/03_classification.ipynb başvurulan) MNIST veri kümesini iyi bir çözüm burada Bulunan

from six.moves import urllib 
from sklearn.datasets import fetch_mldata 
try: 
    mnist = fetch_mldata('MNIST original') 
except urllib.error.HTTPError as ex: 
    print("Could not download MNIST data from mldata.org, trying alternative...") 

    # Alternative method to load MNIST, if mldata.org is down 
    from scipy.io import loadmat 
    mnist_alternative_url = "https://github.com/amplab/datascience-sp14/raw/master/lab7/mldata/mnist-original.mat" 
    mnist_path = "./mnist-original.mat" 
    response = urllib.request.urlopen(mnist_alternative_url) 
    with open(mnist_path, "wb") as f: 
     content = response.read() 
     f.write(content) 
    mnist_raw = loadmat(mnist_path) 
    mnist = { 
     "data": mnist_raw["data"].T, 
     "target": mnist_raw["label"][0], 
     "COL_NAMES": ["label", "data"], 
     "DESCR": "mldata.org dataset: mnist-original", 
    } 
    print("Success!") 

Answer 3

indirmek için alternatif bir konumdur.

import os 
from urllib import urlretrieve 

def download(filename, source='http://yann.lecun.com/exdb/mnist/'): 
    print("Downloading %s" % filename) 
    urlretrieve(source + filename, filename) 

# We then define functions for loading MNIST images and labels. 
# For convenience, they also download the requested files if needed. 
import gzip 

def load_mnist_images(filename): 
    if not os.path.exists(filename): 
     download(filename) 
    # Read the inputs in Yann LeCun's binary format. 
    with gzip.open(filename, 'rb') as f: 
     data = np.frombuffer(f.read(), np.uint8, offset=16) 
    # The inputs are vectors now, we reshape them to monochrome 2D images, 
    # following the shape convention: (examples, channels, rows, columns) 
    data = data.reshape(-1, 1, 28, 28) 
    # The inputs come as bytes, we convert them to float32 in range [0,1]. 
    # (Actually to range [0, 255/256], for compatibility to the version 
    # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.) 
    return data/np.float32(256) 

def load_mnist_labels(filename): 
    if not os.path.exists(filename): 
     download(filename) 
    # Read the labels in Yann LeCun's binary format. 
    with gzip.open(filename, 'rb') as f: 
     data = np.frombuffer(f.read(), np.uint8, offset=8) 
    # The labels are vectors of integers now, that's exactly what we want. 
    return data 


X_train = load_mnist_images('train-images-idx3-ubyte.gz') 
y_train = load_mnist_labels('train-labels-idx1-ubyte.gz') 
X_test = load_mnist_images('t10k-images-idx3-ubyte.gz') 
y_test = load_mnist_labels('t10k-labels-idx1-ubyte.gz') 

Answer 4

Aynı hatayı aldım ve güvenlik duvarını kapatmak zorundaydım. Macbook'ta, Sistem Tercihleri> Güvenlik & Gizlilik> Güvenlik Duvarı> Güvenlik Duvarını Kapat'a gidin.

Answer 5

from sklearn.datasets import fetch_mldata 
try: 
    mnist = fetch_mldata('MNIST original') 
except Exception as ex:   
    from six.moves import urllib 
    from scipy.io import loadmat 
    import os 

    mnist_path = os.path.join(".", "datasets", "mnist-original.mat") 

    # download dataset from github. 
    mnist_alternative_url = "https://github.com/amplab/datascience-sp14/raw/master/lab7/mldata/mnist-original.mat" 
    response = urllib.request.urlopen(mnist_alternative_url) 
    with open(mnist_path, "wb") as f: 
     content = response.read() 
     f.write(content) 

    mnist_raw = loadmat(mnist_path) 
    mnist = { 
     "data": mnist_raw["data"].T, 
     "target": mnist_raw["label"][0], 
     "COL_NAMES": ["label", "data"], 
     "DESCR": "mldata.org dataset: mnist-original", 
    } 
    print("Done!")