mlutils.py 文件源码

def convolve(patchDim, numFeatures, images, W):
    m = np.shape(images)[3]
    imageDim = np.shape(images)[0]
    imageChannels = np.shape(images)[2]
    convolvedFeatures = np.zeros((numFeatures, m, \
                                  imageDim - patchDim + 1, imageDim - patchDim + 1))
    for imageNum in range(m):
        for featureNum in range(numFeatures):
            convolvedImage = np.zeros((imageDim - patchDim + 1, imageDim - patchDim + 1))
            for channel in range(imageChannels):
                feature = np.zeros((patchDim, patchDim))
                start = channel * patchDim*patchDim
                stop = start + patchDim*patchDim
                feature += np.reshape(W[featureNum, start:stop], (patchDim, patchDim), order="F")
                feature = np.flipud(np.fliplr(feature))
                im = images[:, :, channel, imageNum]
                convolvedImage += sig.convolve2d(im, feature, "valid")
            # sparse filtering activation function
            convolvedImage = np.sqrt(1e-8 + np.multiply(convolvedImage, convolvedImage))
            convolvedFeatures[featureNum, imageNum, :, :] = convolvedImage
    return convolvedFeatures