Question

我已经实现了一个lambda函数来将图像从28x28x1调整为224x224x3。我需要从所有通道中减去VGG均值。当我尝试这个时，我得到一个错误

TypeError：＆＃39; Tensor＆＃39;对象不支持项目分配

def try_reshape_to_vgg(x):
    x = K.repeat_elements(x, 3, axis=3)
    x = K.resize_images(x, 8, 8, data_format="channels_last")
    x[:, :, :, 0] = x[:, :, :, 0] - 103.939
    x[:, :, :, 1] = x[:, :, :, 1] - 116.779
    x[:, :, :, 2] = x[:, :, :, 2] - 123.68
    return x[:, :, :, ::-1]

推荐的解决方案是做元素减法的张量？

Answer 1

You can use keras.applications.imagenet_utils.preprocess_input on tensors after Keras 2.1.2. It will subtract the VGG mean from x under the default mode 'caffe'.

from keras.applications.imagenet_utils import preprocess_input

def try_reshape_to_vgg(x):
    x = K.repeat_elements(x, 3, axis=3)
    x = K.resize_images(x, 8, 8, data_format="channels_last")
    x = preprocess_input(x)
    return x

If you would like to stay in an older version of Keras, maybe you can check how it is implemented in Keras 2.1.2, and extract useful lines into try_reshape_to_vgg.

def _preprocess_symbolic_input(x, data_format, mode):
    global _IMAGENET_MEAN

    if mode == 'tf':
        x /= 127.5
        x -= 1.
        return x

    if data_format == 'channels_first':
        # 'RGB'->'BGR'
        if K.ndim(x) == 3:
            x = x[::-1, ...]
        else:
            x = x[:, ::-1, ...]
    else:
        # 'RGB'->'BGR'
        x = x[..., ::-1]

    if _IMAGENET_MEAN is None:
        _IMAGENET_MEAN = K.constant(-np.array([103.939, 116.779, 123.68]))
    # Zero-center by mean pixel
    if K.dtype(x) != K.dtype(_IMAGENET_MEAN):
        x = K.bias_add(x, K.cast(_IMAGENET_MEAN, K.dtype(x)), data_format)
    else:
        x = K.bias_add(x, _IMAGENET_MEAN, data_format)
    return x

如何在keras中减去频道明智的意思？

1 个答案: