44fee976b70e197d0c7bf131a7bd72a888a0e4da,cleverhans/utils_mnist.py,,model_mnist,#Any#Any#Any#Any#Any#Any#,43

Before Change

    
    model = Sequential()

    model.add(Dropout(0.2, input_shape=(1, img_rows, img_cols)))
    model.add(Convolution2D(nb_filters, 8, 8,
                            subsample=(2, 2),
                            dim_ordering="th",
                            border_mode="same"
                            ))
    model.add(Activation("relu"))
    model.add(Convolution2D(nb_filters * 2, 6, 6, subsample=(2, 2),
        dim_ordering="th", border_mode="valid"))
    model.add(Activation("relu"))
    model.add(Convolution2D(nb_filters *2, 5, 5, subsample=(1, 1),
        dim_ordering="th"))
    model.add(Activation("relu"))
    model.add(Dropout(0.5))

    model.add(Flatten())

After Change

              Flatten(),
              Dense(nb_classes)]
    for layer in layers:
        model.add(layer)
    if logits:
        logits_tensor = model(input_ph)
    model.add(Activation("softmax"))

Instances