6fd2d43bfeb1195e68693a66e767464835e40abf,keras/backend/theano_backend.py,,batch_normalization,#Any#Any#Any#Any#Any#Any#,432

Before Change

    use_cudnn = ndim < 5 and (dev.startswith("cuda") or dev.startswith("gpu"))
    if use_cudnn:
        try:
            return theano.sandbox.cuda.dnn.dnn_batch_normalization_test(x, gamma, beta, mean, var,
                                                                        "spatial", epsilon)
        except AttributeError:
            pass
    return T.nnet.bn.batch_normalization(x, gamma, beta, mean, sqrt(var + epsilon),
                                         mode="high_mem")

After Change

        try:
            axis = mean.broadcastable.index(False)
            if axis != 1:
                shuffle_pattern = list(range(ndim))
                shuffle_pattern[1] = shuffle_pattern[axis]
                shuffle_pattern[axis] = 1
                x = x.dimshuffle(shuffle_pattern)
                mean = mean.dimshuffle(shuffle_pattern)
                var = var.dimshuffle(shuffle_pattern)
                beta = beta.dimshuffle(shuffle_pattern)
                gamma = gamma.dimshuffle(shuffle_pattern)
            normed = theano.sandbox.cuda.dnn.dnn_batch_normalization_test(x, gamma, beta, mean, var,
                                                                          "spatial", epsilon)
            if axis != 1:
                normed = normed.dimshuffle(shuffle_pattern)
            return normed
        except AttributeError:
            pass
        except ValueError:
            pass

Instances