accelbrainbase.samplabledata.noisesampler._mxnet package

Submodules

accelbrainbase.samplabledata.noisesampler._mxnet.normal_noise_sampler module

class accelbrainbase.samplabledata.noisesampler._mxnet.normal_noise_sampler.NormalNoiseSampler(loc=0.0, scale=1.0, batch_size=40, seq_len=0, channel=3, height=96, width=96, ctx=cpu(0))

Bases: accelbrainbase.samplabledata.noise_sampler.NoiseSampler

The class to draw fake samples from Gaussian distributions, generating from a mxnet.ndarray.random.

References

• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).

draw()

Draw samples from distribtions.

Returns:Tuple of `mx.nd.array`s.

accelbrainbase.samplabledata.noisesampler._mxnet.uniform_noise_sampler module

class accelbrainbase.samplabledata.noisesampler._mxnet.uniform_noise_sampler.UniformNoiseSampler(low=0.0, high=1.0, batch_size=40, seq_len=0, channel=3, height=96, width=96, ctx=gpu(0))

Bases: accelbrainbase.samplabledata.noise_sampler.NoiseSampler

The class to draw fake samples from uniform distributions, generating from a mxnet.ndarray.random.

References

• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).

draw()

Draw samples from distribtions.

Returns:Tuple of `mx.nd.array`s.

Module contents