Create mnist.py

ch03/mnist.py

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+# coding: utf-8
+try:
+    import urllib.request
+except ImportError:
+    raise ImportError('You should use Python 3.x')
+import os.path
+import gzip
+import pickle
+import os
+import numpy as np
+
+
+url_base = 'http://yann.lecun.com/exdb/mnist/'
+key_file = {
+    'train_img':'train-images-idx3-ubyte.gz',
+    'train_label':'train-labels-idx1-ubyte.gz',
+    'test_img':'t10k-images-idx3-ubyte.gz',
+    'test_label':'t10k-labels-idx1-ubyte.gz'
+}
+
+dataset_dir = os.path.dirname(os.path.abspath(__file__))
+save_file = dataset_dir + "/mnist.pkl"
+
+train_num = 60000
+test_num = 10000
+img_dim = (1, 28, 28)
+img_size = 784
+
+
+def _download(file_name):
+    file_path = dataset_dir + "/" + file_name
+
+    if os.path.exists(file_path):
+        return
+
+    print("Downloading " + file_name + " ... ")
+    urllib.request.urlretrieve(url_base + file_name, file_path)
+    print("Done")
+
+def download_mnist():
+    for v in key_file.values():
+       _download(v)
+
+def _load_label(file_name):
+    file_path = dataset_dir + "/" + file_name
+
+    print("Converting " + file_name + " to NumPy Array ...")
+    with gzip.open(file_path, 'rb') as f:
+            labels = np.frombuffer(f.read(), np.uint8, offset=8)
+    print("Done")
+
+    return labels
+
+def _load_img(file_name):
+    file_path = dataset_dir + "/" + file_name
+
+    print("Converting " + file_name + " to NumPy Array ...")    
+    with gzip.open(file_path, 'rb') as f:
+            data = np.frombuffer(f.read(), np.uint8, offset=16)
+    data = data.reshape(-1, img_size)
+    print("Done")
+
+    return data
+
+def _convert_numpy():
+    dataset = {}
+    dataset['train_img'] =  _load_img(key_file['train_img'])
+    dataset['train_label'] = _load_label(key_file['train_label'])    
+    dataset['test_img'] = _load_img(key_file['test_img'])
+    dataset['test_label'] = _load_label(key_file['test_label'])
+
+    return dataset
+
+def init_mnist():
+    download_mnist()
+    dataset = _convert_numpy()
+    print("Creating pickle file ...")
+    with open(save_file, 'wb') as f:
+        pickle.dump(dataset, f, -1)
+    print("Done!")
+
+def _change_one_hot_label(X):
+    T = np.zeros((X.size, 10))
+    for idx, row in enumerate(T):
+        row[X[idx]] = 1
+
+    return T
+
+
+def load_mnist(normalize=True, flatten=True, one_hot_label=False):
+    """MNIST 데이터셋 읽기
+
+    Parameters
+    ----------
+    normalize : 이미지의 픽셀 값을 0.0~1.0 사이의 값으로 정규화할지 정한다.
+    one_hot_label : 
+        one_hot_label이 True면、레이블을 원-핫(one-hot) 배열로 돌려준다.
+        one-hot 배열은 예를 들어 [0,0,1,0,0,0,0,0,0,0]처럼 한 원소만 1인 배열이다.
+    flatten : 입력 이미지를 1차원 배열로 만들지를 정한다. 
+
+    Returns
+    -------
+    (훈련 이미지, 훈련 레이블), (시험 이미지, 시험 레이블)
+    """
+    if not os.path.exists(save_file):
+        init_mnist()
+
+    with open(save_file, 'rb') as f:
+        dataset = pickle.load(f)
+
+    if normalize:
+        for key in ('train_img', 'test_img'):
+            dataset[key] = dataset[key].astype(np.float32)
+            dataset[key] /= 255.0
+
+    if one_hot_label:
+        dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
+        dataset['test_label'] = _change_one_hot_label(dataset['test_label'])    
+
+    if not flatten:
+         for key in ('train_img', 'test_img'):
+            dataset[key] = dataset[key].reshape(-1, 1, 28, 28)
+
+    return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label']) 
+
+
+if __name__ == '__main__':
+    init_mnist()