相关疑难解决方法(0)

我编写了python代码,以编程方式生成卷积神经网络(CNN),用于训练和验证.prototxt文件在caffe中.以下是我的功能:

def custom_net(lmdb, batch_size):

    # define your own net!
    n = caffe.NetSpec()

    # keep this data layer for all networks
    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,
                             ntop=2, transform_param=dict(scale=1. / 255))

    n.conv1 = L.Convolution(n.data, kernel_size=6,
                            num_output=48, weight_filler=dict(type='xavier'))
    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)

    n.conv2 = L.Convolution(n.pool1, kernel_size=5,
                            num_output=48, weight_filler=dict(type='xavier'))
    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)

    n.conv3 = L.Convolution(n.pool2, kernel_size=4,
                            num_output=48, weight_filler=dict(type='xavier'))
    n.pool3 = L.Pooling(n.conv3, kernel_size=2, stride=2, pool=P.Pooling.MAX)

    n.conv4 = L.Convolution(n.pool3, kernel_size=2,
                            num_output=48, weight_filler=dict(type='xavier'))
    n.pool4 = L.Pooling(n.conv4, kernel_size=2, stride=2, pool=P.Pooling.MAX)

    n.fc1 = …

我有一个已经在CIFAR-10上训练过的模型,但我没有意识到如何在pycaffe中进行预测.

我从lmdb获得了一个图像,但我不知道如何在网络中加载它并获得预测的类.

我的代码:

net = caffe.Net('acc81/model.prototxt',
                'acc81/cifar10_full_iter_70000.caffemodel.h5',
                 caffe.TEST)
lmdb_env = lmdb.open('cifar10_test_lmdb/')
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.cursor()
for key, value in lmdb_cursor:
    datum = caffe.proto.caffe_pb2.Datum()
    datum.ParseFromString(value)
    image = caffe.io.datum_to_array(datum)
    image = image.astype(np.uint8)

    # What's next with the image variable?
    # If i try:
    # out = net.forward_all(data=np.asarray([image]))
    # I get Exception: Input blob arguments do not match net inputs.

    print("Image class is " + label)