mnist_problem_generator.py 文件源码

def create_dictionary_dl(lmbd, K=100, N=10000, dir_mnist='save_exp/mnist'):

    import os.path as osp
    fname = osp.join(dir_mnist, "D_mnist_K{}_lmbd{}.npy".format(K, lmbd))
    if osp.exists(fname):
        D = np.load(fname)
    else:
        from sklearn.decomposition import DictionaryLearning
        mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
        im = mnist.train.next_batch(N)[0]
        im = im.reshape(N, 28, 28)
        im = [imresize(a, (17, 17), interp='bilinear', mode='L')-.5
              for a in im]
        X = np.array(im).reshape(N, -1)
        print(X.shape)

        dl = DictionaryLearning(K, alpha=lmbd*N, fit_algorithm='cd',
                                n_jobs=-1, verbose=1)
        dl.fit(X)
        D = dl.components_.reshape(K, -1)
        np.save(fname, D)
    return D