title: ValueError: Found array with dim 3. Estimator expected <= 2. With own dataset
body: <p>I'm trying to generate my own training data for recognition problem. I have two folders s0 and s1 and the folder containing is data. Images, lables are the two list in which the labels contains the names of the folder.</p>
  
  <pre><code>data
    |—- s0
    |    |—- 1.pgm
    |    |—- 2.pgm
    |    |—- ...
    |—- s1
    |    |—- 1.pgm
    |    |—- 2.pgm
    |    |—- ...
  </code></pre>
  
  <p>Below is the code, it's showing me the following error on line <code>classifier.fit(images, lables)</code></p>
  
  <pre><code> Traceback (most recent call last):
   File "mint.py", line 34, in &lt;module&gt;
     classifier.fit(images, lables)
   File "/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py",  line 150, in fit
    X = check_array(X, accept_sparse='csr', dtype=np.float64, order='C')
   File "/usr/local/lib/python2.7/dist-         packages/sklearn/utils/validation.py", line 396, in check_array
   % (array.ndim, estimator_name)) 
  </code></pre>
  
  <p><code>ValueError: Found array with dim 3. Estimator expected &lt;= 2. here</code></p>
  
  <p>Code:</p>
  
  <pre><code>import os,sys
  import cv2
  import numpy as np
  from sklearn.svm import SVC
  fn_dir ='/home/aquib/Desktop/Natural/data'
  
  # Create a list of images and a list of corresponding names
  (images, lables, names, id) = ([], [], {}, 0)
  for (subdirs, dirs, files) in os.walk(fn_dir):
    for subdir in dirs:
       names[id] = subdir
       mypath = os.path.join(fn_dir, subdir)
      for item in os.listdir(mypath):
        if '.png' in item:  
        label=id
        image = cv2.imread(os.path.join(mypath, item),0)
        r_image = np.resize(image,(30,30))
        if image is not None:
           images.append(r_image)
           lables.append(int(label))
      id += 1  
  # Create a Numpy array from the two lists above
  (images, lables) = [np.array(lis) for lis in [images, lables]]
  classifier = SVC(verbose=0, kernel='poly', degree=3)
  classifier.fit(images, lables)
  </code></pre>
  
  <p>I really don't understand how to correct it in 2 dimension. I am trying  the following, but the error is same:</p>
  
  <pre><code>images = np.array(images)
  im_sq = np.squeeze(images).shape
  images = images.reshape(images.shape[:2]) 
  </code></pre>