def main():
train_data, val_data, test_data = data.data_loader()
#eta2 = np.abs([np.random.randn(sizes[0],1)])
eta2 =np.abs([ 0.8 for x in range(sizes[0])])
p1 = map(add,test_data[0][0],eta2)
p2 = map(add,test_data[1][0],eta2)
p = [(p1,test_data[0][1]), (p2,test_data[1][1])]
q1 = test_data[0][0]
q2 = test_data[1][0]
q = [(q1,test_data[0][1]), (q2,test_data[1][1])]
qq = np.reshape(q1,(28,28))
plt.imshow(qq, cmap = cm.Greys_r)
plt.show()
pp = np.reshape(p1,(28,28))
plt.imshow(pp, cmap = cm.Greys_r)
plt.show()
#plt.imshow(pp, cmap = cm.Greys_r)
#plt.show()
SGD(train_data, epochs, mini_batch_size, eta, val_data)
n_test = len(p)
print "Original result {0} / {1}".format(evaluate(q), n_test)
print "Adversarial {0} / {1}".format(evaluate(p), n_test)
def main():
train_data, val_data, test_data = data.data_loader()
SGD(train_data, epochs, mini_batch_size, eta, test_data)
def main():
train_data, val_data, test_data = data.data_loader()
SGD(train_data, epochs, mini_batch_size, eta, test_data)
