StudyAdversarials/utils.py at master · erogol/StudyAdversarials · GitHub

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import numpy as np

def load_data():
    x_train = np.load('data/x_train.npy')
    y_train = np.load('data/y_train.npy')
    x_test = np.load('data/x_test.npy')
    y_test = np.load('data/y_test.npy')
    x_advs = np.load('data/adversarials.npy')
    return x_train, y_train, x_test, y_test, x_advs

from matplotlib import pylab as plt
# %matplotlib inline
# set display defaults
plt.rcParams['figure.figsize'] = (10, 10)        # large images
plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels
plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap

# Plotting function
# plt.rc('font', **{'family' : 'sans-serif',
#                          'weight' : 'bold',
#                          'size'   : 18})
# plt.rc('text', **{'usetex' : True})

def plot_mnist(X, y, X_embedded, name, min_dist=10.0):
    fig = plt.figure(figsize=(10, 10))
    ax = plt.axes(frameon=False)
    plt.title("\\textbf{MNIST dataset} -- Two-dimensional "
          "embedding of 70,000 handwritten digits with %s" % name)
    plt.setp(ax, xticks=(), yticks=())
    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=0.9,
                    wspace=0.0, hspace=0.0)
    plt.scatter(X_embedded[:, 0], X_embedded[:, 1],
            c=y, marker="x")

    if min_dist is not None:
        from matplotlib import offsetbox
        shown_images = np.array([[15., 15.]])
        indices = np.arange(X_embedded.shape[0])
        np.random.shuffle(indices)
        for i in indices[:5000]:
            dist = np.sum((X_embedded[i] - shown_images) ** 2, 1)
            if np.min(dist) < min_dist:
                continue
            shown_images = np.r_[shown_images, [X_embedded[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(X[i].reshape(28, 28),
                                      cmap=plt.cm.gray_r), X_embedded[i])
            ax.add_artist(imagebox)