34c9172185fc79e6a971919f910fbbb7e945f5e5,art/classifiers/keras_unittest.py,TestKerasClassifier,setUpClass,#Any#,24

Before Change

        im_shape = x_train[0].shape

        // Create basic CNN on MNIST; architecture from Keras examples
        model = Sequential()
        model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=im_shape))
        model.add(Conv2D(64, (3, 3), activation="relu"))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Flatten())
        model.add(Dense(128, activation="relu"))
        model.add(Dense(10, activation="softmax"))

        model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(),
                      metrics=["accuracy"])

        model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=3)
        cls.model_mnist = model

        import requests
        import tempfile

After Change

        cls.mnist = (x_train, y_train), (x_test, y_test)

        // Load small Keras model
        cls.model_mnist, _ = get_classifier_kr()
        cls.functional_model = cls.functional_model()

        import requests

Instances