深度学习 Udacity 课程：Prob 2 assignment 1 (notMNIST)

Question

看完this and taking the courses, I am struggling to solve the second problem in assignment 1 (notMnist):

Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.

这是我尝试过的：

import random
rand_smpl = [ train_datasets[i] for i in sorted(random.sample(xrange(len(train_datasets)), 1)) ]
print(rand_smpl)
filename = rand_smpl[0]
import pickle
loaded_pickle = pickle.load( open( filename, "r" ) )
image_size = 28  # Pixel width and height.
import numpy as np
dataset = np.ndarray(shape=(len(loaded_pickle), image_size, image_size),
                         dtype=np.float32)
import matplotlib.pyplot as plt

plt.plot(dataset[2])
plt.ylabel('some numbers')
plt.show()

但这就是我得到的：

这没有多大意义。老实说，我的代码也可能如此，因为我不太确定如何解决这个问题！

泡菜是这样制作的：

image_size = 28  # Pixel width and height.
pixel_depth = 255.0  # Number of levels per pixel.

def load_letter(folder, min_num_images):
  """Load the data for a single letter label."""
  image_files = os.listdir(folder)
  dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
                         dtype=np.float32)
  print(folder)
  num_images = 0
  for image in image_files:
    image_file = os.path.join(folder, image)
    try:
      image_data = (ndimage.imread(image_file).astype(float) - 
                    pixel_depth / 2) / pixel_depth
      if image_data.shape != (image_size, image_size):
        raise Exception('Unexpected image shape: %s' % str(image_data.shape))
      dataset[num_images, :, :] = image_data
      num_images = num_images + 1
    except IOError as e:
      print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')
    
  dataset = dataset[0:num_images, :, :]
  if num_images < min_num_images:
    raise Exception('Many fewer images than expected: %d < %d' %
                    (num_images, min_num_images))
    
  print('Full dataset tensor:', dataset.shape)
  print('Mean:', np.mean(dataset))
  print('Standard deviation:', np.std(dataset))
  return dataset

函数的调用方式如下：

  dataset = load_letter(folder, min_num_images_per_class)
  try:
    with open(set_filename, 'wb') as f:
      pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)

这里的思路是：

Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.

We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road.

Answer 1

按以下步骤操作：

#define a function to conver label to letter
def letter(i):
    return 'abcdefghij'[i]


# you need a matplotlib inline to be able to show images in python notebook
%matplotlib inline
#some random number in range 0 - length of dataset
sample_idx = np.random.randint(0, len(train_dataset))
#now we show it
plt.imshow(train_dataset[sample_idx])
plt.title("Char " + letter(train_labels[sample_idx]))

您的代码实际上更改了数据集的类型，它不是大小为 (220000, 28,28) 的 ndarray

一般来说，pickle 是一个包含一些对象的文件，而不是数组本身。您应该直接使用 pickle 中的对象来获取训练数据集（使用代码片段中的符号）：

#will give you train_dataset and labels
train_dataset = loaded_pickle['train_dataset']
train_labels = loaded_pickle['train_labels']

更新：

根据@gsarmas 的请求，我对整个 Assignment1 的解决方案 link 位于 here。

代码已注释且大部分不言自明，但如有任何疑问，请随时通过您喜欢的任何方式联系 github

Answer 2

请检查此代码

pickle_file = train_datasets[0]
with open(pickle_file, 'rb') as f:

# unpickle
letter_set = pickle.load(f)  

# pick a random image index
sample_idx = np.random.randint(len(letter_set))

# extract a 2D slice
sample_image = letter_set[sample_idx, :, :]  
plt.figure()

# display it
plt.imshow(sample_image)

Answer 3

使用此代码：

#random select a letter
i = np.random.randint( len(train_datasets) )
plt.title( "abcdefghij"[i] )

#read the file of selected letter
f = open( train_datasets[i], "rb" )
f = pickle.load(f)

#random select an image in the file
j = np.random.randint( len(f) )

#show image
plt.axis('off')
img = plt.imshow( f[ j, :, : ] )

enter image description here

深度学习 Udacity 课程：Prob 2 assignment 1 (notMNIST)

Deep learning Udacity course: Prob 2 assignment 1 (notMNIST)

python

machine-learning

matplotlib

computer-vision

deep-learning