CarstenSchelp

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
import matplotlib.transforms as transforms

def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):
    """
    Create a plot of the covariance confidence ellipse of `x` and `y`
    
    See how and why this works: https://carstenschelp.github.io/2018/09/14/Plot_Confidence_Ellipse_001.html

HarshTrivedi

import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
#     Step 1: Construct Vocabulary
#     Step 2: Load indexed data (list of instances, where each instance is list of character indices)

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Example employing Lasagne for digit generation using the MNIST dataset and
Least Squares Generative Adversarial Networks
(LSGANs, see https://arxiv.org/abs/1611.04076 for the paper).

It is based on a WGAN example:
https://gist.github.com/f0k/f3190ebba6c53887d598d03119ca2066

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Example employing Lasagne for digit generation using the MNIST dataset and
Wasserstein Generative Adversarial Networks
(WGANs, see https://arxiv.org/abs/1701.07875 for the paper and
https://github.com/martinarjovsky/WassersteinGAN for the "official" code).

It is based on a DCGAN example:

justheuristic

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Example employing Lasagne for digit generation using the MNIST dataset and
Deep Convolutional Generative Adversarial Networks
(DCGANs, see http://arxiv.org/abs/1511.06434).

It is based on the MNIST example in Lasagne:
http://lasagne.readthedocs.org/en/latest/user/tutorial.html

ebenolson

"""
Functions to create network diagrams from a list of Layers.

Examples:

    Draw a minimal diagram to a pdf file:
        layers = lasagne.layers.get_all_layers(output_layer)
        draw_to_file(layers, 'network.pdf', output_shape=False)

    Draw a verbose diagram in an IPython notebook: