rasmusbergpalm

class Residual(t.nn.Module):
    def __init__(self, *args: t.nn.Module):
        super().__init__()
        self.delegate = t.nn.Sequential(*args)

    def forward(self, inputs):
        return self.delegate(inputs) + inputs

rasmusbergpalm

import tensorflow as tf


def message_passing(nodes, edges, edge_features, message_fn, edge_keep_prob=1.0):
    """
    Pass messages between nodes and sum the incoming messages at each node.
    Implements equation 1 and 2 in the paper, i.e. m_{.j}^t &= \sum_{i \in N(j)} f(h_i^{t-1}, h_j^{t-1})

    :param nodes: (n_nodes, n_features) tensor of node hidden states.
    :param edges: (n_edges, 2) tensor of indices (i, j) indicating an edge from nodes[i] to nodes[j].

rasmusbergpalm

{
  "source": {
    "source": "show flights saturday evening from st. louis to burbank",
    "date": "1994-09-16"
  },
  "target": {
    "toloc.city_name": "bur",
    "depart_time.time": "18:00",
    "fromloc.city_name": "stl",
    "depart_date": "1994-09-17"

rasmusbergpalm

import random
import string

import matplotlib
import numpy as np
from scipy.spatial.distance import cityblock

matplotlib.use('Agg')
import matplotlib.pyplot as plt

rasmusbergpalm

# Encoder
source = Input(shape=(None,), dtype='int32', name='source')
embedded = Embedding(output_dim=128, input_dim=train.source_vocab_size(), mask_zero=True)(source)
last_hid = LSTM(output_dim=128)(embedded)

# Decoder
repeated = RepeatVector(train.target.padded.shape[1])(last_hid)
decoder = LSTM(output_dim=128, return_sequences=True)(repeated)
output = TimeDistributed(Dense(output_dim=train.target_vocab_size(), activation='softmax'))(decoder)
model = Model([source], output=[output])

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rasmusbergpalm

from lasagne import *
from lasagne.layers import *
from lasagne.random import get_rng
from lasagne.utils import *
import numpy as np
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams


class DropoutLSTMLayer(MergeLayer):