def genDataLayer(train_val, number):
    layer_str = 'name: "Resnet{number}"\nlayer {{\n  name: "data"\n  type: "Data"\n  top: "data"\n  top: "label"\n  data_param {{\n    source: "examples/imagenet/ilsvrc12_train_lmdb"\n    backend: LMDB\n    batch_size: 32\n  }}\n  transform_param {{\n    crop_size: 224\n    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"\n    mirror: true\n  }}\n  include: {{ phase: TRAIN }}\n}}\nlayer {{\n  name: "data"\n  type: "Data"\n  top: "data"\n  top: "label"\n  data_param {{\n    source: "examples/imagenet/ilsvrc12_val_lmdb"\n    backend: LMDB\n    batch_size: 32\n  }}\n  transform_param {{\n    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"\n    crop_size: 224\n    mirror: false\n  }}\n  include: {{ phase: TEST }}\n}}'.format(number=number)
    train_val += layer_str
    return (train_val, 'data')
def genDataLayer(train_val, number):
    layer_str = 'name: "Resnet{number}"\nlayer {{\n  name: "data"\n  type: "Data"\n  top: "data"\n  top: "label"\n  data_param {{\n    source: "examples/imagenet/ilsvrc12_train_lmdb"\n    backend: LMDB\n    batch_size: 32\n  }}\n  transform_param {{\n    crop_size: 224\n    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"\n    mirror: true\n  }}\n  include: {{ phase: TRAIN }}\n}}\nlayer {{\n  name: "data"\n  type: "Data"\n  top: "data"\n  top: "label"\n  data_param {{\n    source: "examples/imagenet/ilsvrc12_val_lmdb"\n    backend: LMDB\n    batch_size: 32\n  }}\n  transform_param {{\n    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"\n    crop_size: 224\n    mirror: false\n  }}\n  include: {{ phase: TEST }}\n}}'.format(number=number)
    train_val += layer_str
    return (train_val, 'data')