caffenet.py 文件源码

def caffenet(lmdb, batch_size=256, include_acc=False):
    data, label = L.Data(source=lmdb, backend=P.Data.LMDB,
                         batch_size=batch_size, ntop=2)

    # the net itself
    conv1, relu1 = conv_relu(data, 11, 96, stride=4)
    pool1 = max_pool(relu1, 3, stride=2)
    norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
    conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
    pool2 = max_pool(relu2, 3, stride=2)
    norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
    conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
    conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
    conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
    pool5 = max_pool(relu5, 3, stride=2)
    fc6, relu6 = fc_relu(pool5, 4096)
    drop6 = L.Dropout(relu6, in_place=True)
    fc7, relu7 = fc_relu(drop6, 4096)
    drop7 = L.Dropout(relu7, in_place=True)
    fc8 = L.InnerProduct(drop7, num_output=1000)
    loss = L.SoftmaxWithLoss(fc8, label)

    if include_acc:
        acc = L.Accuracy(fc8, label)
        return to_proto(loss, acc)
    else:
        return to_proto(loss)