jakevdp

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

spro

import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
from torch import optim
import numpy as np
import math, random

# Generating a noisy multi-sin wave 

silgon

# code to solve https://stackoverflow.com/q/47932589/2237916
import numpy as np
import tflearn
from random import shuffle

# parameters
n_input=100
n_train=2000
n_test = 500
# generate data

dvgodoy

## Gist originally developed by @craffel and improved by @ljhuang2017

import matplotlib.pyplot as plt
import numpy as np

def draw_neural_net(ax, left, right, bottom, top, layer_sizes, coefs_, intercepts_, n_iter_, loss_):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage: