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Python FilterActs.eval方法代码示例

本文整理汇总了Python中pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs.eval方法的典型用法代码示例。如果您正苦于以下问题:Python FilterActs.eval方法的具体用法?Python FilterActs.eval怎么用?Python FilterActs.eval使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs的用法示例。


在下文中一共展示了FilterActs.eval方法的1个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: make_funcs

# 需要导入模块: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 别名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs import eval [as 别名]
def make_funcs(batch_size, rows, cols, channels, filter_rows, num_filters):
    rng = np.random.RandomState([2012, 10, 9])

    filter_cols = filter_rows

    base_image_value = rng.uniform(-1.0, 1.0, (channels, rows, cols, batch_size)).astype("float32")
    base_filters_value = rng.uniform(-1.0, 1.0, (channels, filter_rows, filter_cols, num_filters)).astype("float32")
    images = shared(base_image_value)
    filters = shared(base_filters_value, name="filters")

    # bench.py should always be run in gpu mode so we should not need a gpu_from_host here
    output = FilterActs()(images, filters)

    output_shared = shared(output.eval())

    cuda_convnet = function([], updates={output_shared: output})
    cuda_convnet.name = "cuda_convnet"

    images_bc01v = base_image_value.transpose(3, 0, 1, 2)
    filters_bc01v = base_filters_value.transpose(3, 0, 1, 2)
    filters_bc01v = filters_bc01v[:, :, ::-1, ::-1]

    images_bc01 = shared(images_bc01v)
    filters_bc01 = shared(filters_bc01v)

    output_conv2d = conv2d(
        images_bc01, filters_bc01, border_mode="valid", image_shape=images_bc01v.shape, filter_shape=filters_bc01v.shape
    )

    output_conv2d_shared = shared(output_conv2d.eval())

    baseline = function([], updates={output_conv2d_shared: output_conv2d})
    baseline.name = "baseline"

    return cuda_convnet, baseline
开发者ID:CandyPythonFlow,项目名称:pylearn2,代码行数:37,代码来源:bench.py


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