Source: vis/regularizers.py#L0

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normalize

normalize(input_tensor, output_tensor)

Normalizes the output_tensor with respect to input_tensor dimensions. This makes regularizer weight factor more or less uniform across various input image dimensions.

Args:

• input_tensor: An tensor of shape: (samples, channels, image_dims...) if image_data_format= channels_first or (samples, image_dims..., channels) if image_data_format=channels_last.
• output_tensor: The tensor to normalize.

Returns:

The normalized tensor.

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TotalVariation

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TotalVariation.__init__

__init__(self, img_input, beta=2.0)

Total variation regularizer encourages blobbier and coherent image structures, akin to natural images. See section 3.2.2 in Visualizing deep convolutional neural networks using natural pre-images for details.

Args:

• img_input: An image tensor of shape: (samples, channels, image_dims...) if image_data_format=channels_firstor(samples, image_dims..., channels)ifimage_data_format=channels_last`.
• beta: Smaller values of beta give sharper but 'spikier' images. Values $\in [1.5, 3.0]$ are recommended as a reasonable compromise. (Default value = 2.)

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TotalVariation.build_loss

build_loss(self)

Implements the N-dim version of function $$TV^{\beta}(x) = \sum_{whc} \left ( \left ( x(h, w+1, c) - x(h, w, c) \right )^{2} + \left ( x(h+1, w, c) - x(h, w, c) \right )^{2} \right )^{\frac{\beta}{2}}$$ to return total variation for all images in the batch.

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LPNorm

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LPNorm.__init__

__init__(self, img_input, p=6.0)

Builds a L-p norm function. This regularizer encourages the intensity of pixels to stay bounded. i.e., prevents pixels from taking on very large values.

Args:

• img_input: 4D image input tensor to the model of shape: (samples, channels, rows, cols) if data_format='channels_first' or (samples, rows, cols, channels) if data_format='channels_last'.
• p: The pth norm to use. If p = float('inf'), infinity-norm will be used.

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LPNorm.build_loss

build_loss(self)