def run():
pt = Preprocessor()
tr = Trainer()
X_train, y_train = pt.load_data()
X_test, y_test = pt.load_data("mnist_test_data.npz")
x1 = X_train.reshape((-1, 28, 28, 1))
x2 = X_test.reshape((-1, 28, 28, 1))
y1 = keras.utils.to_categorical(y_train, len(np.unique(y_train)))
y2 = keras.utils.to_categorical(y_test, len(np.unique(y_test)))
clf = tr.cnn(x1, y1, x2, y2)
tr.save(clf, "cnn_mnist_keras.h5")
return clf
def run():
pt = Preprocessor()
tr = Trainer()
ts = Tester()
t0 = time.time()
X_train, y_train = pt.load_data()
X_test, y_test = pt.load_data("mnist_test_data.npz")
X_train, y_train = make_shuffle(X_train, y_train)
X_test, y_test = make_shuffle(X_test, y_test)
X_train = X_train.reshape((-1, 1, 28, 28))
X_test = X_test.reshape((-1, 1, 28, 28))
print(time.time() - t0)
t1 = time.time()
clf = tr.net(X_train, y_train)
print(time.time() - t1)
acc = ts.get_acc(clf, X_test, y_test) #acc=97.8%
return clf, acc