def __init__(self, vocab_size, embed_size, hidden_size, out_size):
super(GRU_SentenceClassifier,
self).__init__(xe=L.EmbedID(vocab_size, embed_size),
eh=L.GRU(embed_size, hidden_size),
eh2=L.GRU(hidden_size, hidden_size),
hh=L.Linear(hidden_size, hidden_size),
hy=L.Linear(hidden_size, out_size))
def __init__(self, src_vocab, args, src_w2v):
super(biGRU_encoder, self).__init__()
with self.init_scope():
self.word2embed = chainLinks.EmbedID(src_vocab.size,
args.edim,
ignore_label=-1)
self.embed2hiddenf = chainLinks.GRU(args.edim, args.nhid)
self.embed2hiddenb = chainLinks.GRU(args.edim, args.nhid)
#embedding weight is intialized by w2v.
if src_w2v is not None:
for i in range(src_vocab.size):
word = src_vocab.id2word[i]
if word in src_w2v:
self.word2embed.W.data[i] = src_w2v[word]
self.vocab_size = src_vocab.size
self.embedding_size = args.edim
self.hidden_size = args.nhid
if args.useDropout:
self.use_dropout = args.useDropout
self.dropoutr = args.dlr
else:
self.use_dropout = None
self.dropoutr = 0
util.trace('{}'.format(chainer.global_config.__dict__))
def __init__(self, in_size, n_units, out_size, train=True):
super(BiGRU, self).__init__()
with self.init_scope():
self.embed = L.EmbedID(in_size, n_units)
self.hf = L.GRU(n_units, n_units)
self.hb = L.GRU(n_units, n_units)
self.l3 = L.Linear(n_units * 2, out_size)
for param in self.params():
param.data[...] = np.random.uniform(-0.1, 0.1, param.data.shape)
def __init__(self, in_size, out_size):
log.info("Creating GRUCell(%i, %i)" % (in_size, out_size))
super(GRUCell, self).__init__(gru=L.GRU(out_size, in_size), )
self.add_param("initial_state", (1, out_size))
self.initial_state.data[...] = self.xp.random.randn(out_size)
self.out_size = out_size
self.in_size = in_size
def __init__(self,
vocab_size=10,
word_size=10,
hidden_state=10,
class_num=10,
dtype=np.float32,
maxLength=10,
layer_num=1):
super(Decoder, self).__init__()
with self.init_scope():
self.word_size = word_size
self.beginToken = 0
self.endToken = 1
self.dtype = dtype
self.maxLength = maxLength
W = initializers.HeNormal(1 / np.sqrt(2), self.dtype)
self.embed = L.EmbedID(vocab_size, word_size)
self.embed_proj = L.Linear(word_size, hidden_state, initialW=W)
self.out = L.Linear(in_size=hidden_state,
out_size=class_num,
initialW=W)
self.attention = GlobalAttention(hidden_size=hidden_state)
self.layer_num = layer_num
for layer_id in range(self.layer_num):
setattr(self, "rnn_cell_" + str(layer_id),
L.GRU(in_size=hidden_state, out_size=hidden_state))
def __init__(self,
hidden_dim,
out_dim,
n_layers,
n_atom_types=MAX_ATOMIC_NUM,
concat_hidden=False,
weight_tying=True):
super(GGNN, self).__init__()
n_readout_layer = 1 if concat_hidden else n_layers
n_message_layer = 1 if weight_tying else n_layers
with self.init_scope():
# Update
self.embed = EmbedAtomID(out_size=hidden_dim, in_size=n_atom_types)
self.message_layers = chainer.ChainList(*[
GraphLinear(hidden_dim, self.NUM_EDGE_TYPE * hidden_dim)
for _ in range(n_message_layer)
])
self.update_layer = links.GRU(2 * hidden_dim, hidden_dim)
# Readout
self.i_layers = chainer.ChainList(*[
GraphLinear(2 * hidden_dim, out_dim)
for _ in range(n_readout_layer)
])
self.j_layers = chainer.ChainList(*[
GraphLinear(hidden_dim, out_dim)
for _ in range(n_readout_layer)
])
self.out_dim = out_dim
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.concat_hidden = concat_hidden
self.weight_tying = weight_tying
def __init__(self, hidden_dim=16, num_edge_type=4):
super(GGNNUpdate, self).__init__()
with self.init_scope():
self.graph_linear = GraphLinear(hidden_dim,
num_edge_type * hidden_dim)
self.update_layer = links.GRU(2 * hidden_dim, hidden_dim)
self.num_edge_type = num_edge_type
def __init__(self, hidden_dim=16, nn=None):
super(MPNNUpdate, self).__init__()
with self.init_scope():
self.message_layer = EdgeNet(out_channels=hidden_dim, nn=nn)
self.update_layer = links.GRU(2 * hidden_dim, hidden_dim)
self.hidden_dim = hidden_dim
self.nn = nn
def __init__(self, n_units, n_out, layers=2, dropout=0):
super(GRU, self).__init__()
self.layers = layers
self.dropout = dropout
with self.init_scope():
for l in range(layers):
setattr(self, "layer_{}".format(l), L.GRU(None, n_units))
self.out = L.Linear(None, n_out)
def __init__(self, hidden_dim=16, nn=None):
# type: (int, Optional[chainer.Link]) -> None
super(MPNNUpdate, self).__init__()
with self.init_scope():
self.message_layer = EdgeNet(out_channels=hidden_dim, nn=nn)
self.update_layer = links.GRU(2 * hidden_dim, hidden_dim)
self.hidden_dim = hidden_dim
self.nn = nn
def __init__(self, vocab_size, in_size, hidden_size, out_size):
super(Char_Rnn, self).__init__(xh=L.EmbedID(vocab_size,
in_size,
ignore_label=-1),
bn1=L.BatchNormalization(hidden_size),
hh=L.GRU(in_size, hidden_size),
hy=L.Linear(hidden_size, out_size),
bn2=L.BatchNormalization(out_size))
def __init__(self, n_out=1):
super(NPGRUPopulation, self).__init__()
self.n_out = n_out
with self.init_scope():
self.l1 = L.GRU(None, n_out)
def __init__(
self,
out_dim,
hidden_dim=16,
n_layers=4,
n_atom_types=MAX_ATOMIC_NUM,
concat_hidden=False,
dropout_rate=0.0,
batch_normalization=False,
weight_tying=True,
update_tying=True,
self_loop=False,
):
super(GGNN, self).__init__()
n_readout_layer = n_layers if concat_hidden else 1
n_message_layer = 1 if weight_tying else n_layers
n_update_layer = 1 if update_tying else n_layers
self.n_readout_layer = n_readout_layer
self.n_message_layer = n_message_layer
self.out_dim = out_dim
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.concat_hidden = concat_hidden
self.dropout_rate = dropout_rate
self.batch_normalization = batch_normalization
self.weight_tying = weight_tying
self.update_tying = update_tying
self.self_loop = self_loop
with self.init_scope():
# Update
self.embed = EmbedAtomID(out_size=hidden_dim, in_size=n_atom_types)
self.message_layers = chainer.ChainList(*[
GraphLinear(hidden_dim, self.NUM_EDGE_TYPE * hidden_dim)
for _ in range(n_message_layer)
])
self.update_layers_0 = chainer.ChainList(*[
links.Linear(2 * hidden_dim, 2 * hidden_dim)
for _ in range(n_update_layer)
])
self.update_layer = links.GRU(2 * hidden_dim, hidden_dim)
if self_loop:
self.self_loop_layer = GraphLinear(hidden_dim, hidden_dim)
# Readout
self.i_layers = chainer.ChainList(*[
GraphLinear(2 * hidden_dim, out_dim)
for _ in range(n_readout_layer)
])
self.j_layers = chainer.ChainList(*[
GraphLinear(hidden_dim, out_dim)
for _ in range(n_readout_layer)
])
def __init__(self, size_vocab, size_embed, size_hidden):
'''
: arg size_vocab : 使われる単語の語彙数
: arg size_embed : 単語をベクトル表現した時のサイズ
: arg size_hidden : 隠れ層のサイズ
: arg size_batch : バッチのサイズ
'''
super(Seq2SeqVAE, self).__init__()
self.size_vocab = size_vocab
with self.init_scope():
# encoder
self.enc_embed = L.EmbedID(size_vocab, size_embed, ignore_label=-1)
self.enc_rnn = L.GRU(size_embed, size_hidden)
self.h_mu = L.Linear(size_hidden, size_hidden)
self.h_ln = L.Linear(size_hidden, size_hidden)
# decoder
self.dec_embed = L.EmbedID(size_vocab, size_embed, ignore_label=-1)
self.dec_rnn = L.GRU(size_embed, size_hidden)
self.dec_out = L.Linear(size_hidden, size_vocab)
def __init__(self, input_size, output_size, hidden_size=32):
self.input_size = input_size
self.output_size = output_size
self.hidden_size = hidden_size
self.v = Variable(np.zeros((1, input_size), dtype=np.float32))
self.h = Variable(np.zeros((1, hidden_size), dtype=np.float32))
self.p = Variable(np.zeros((1, output_size), dtype=np.float32))
super(RNN, self).__init__(
# Use LSTM or GRU in the RNN
# l0=L.LSTM(input_size, hidden_size),
l0=L.GRU(input_size, hidden_size),
l1=L.Linear(hidden_size, output_size)
)
def __init__(self, in_channels=None, hidden_channels=16,
out_channels=None, n_edge_types=4, **kwargs):
if out_channels is None:
out_channels = hidden_channels
super(GGNNUpdate, self).__init__()
if in_channels is None:
gru_in_channels = None
else:
gru_in_channels = in_channels + hidden_channels
with self.init_scope():
self.graph_linear = GraphLinear(
in_channels, n_edge_types * hidden_channels)
self.update_layer = links.GRU(gru_in_channels, out_channels)
self.n_edge_types = n_edge_types
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.out_channels = out_channels
def __init__(self,
hidden_dim=16,
n_layers=4,
n_atom_types=MAX_ATOMIC_NUM,
num_edge_type=4,
weight_tying=True):
super(GGNNUpdate, self).__init__()
n_layer = 1 if weight_tying else n_layers
with self.init_scope():
self.graph_linears = chainer.ChainList(*[
GraphLinear(hidden_dim, num_edge_type * hidden_dim)
for _ in range(n_layer)
])
self.update_layer = links.GRU(2 * hidden_dim, hidden_dim)
self.n_layers = n_layers
self.num_edge_type = num_edge_type
self.weight_tying = weight_tying
def __init__(self,
in_channels=None,
hidden_channels=16,
out_channels=None,
nn=None,
**kwargs):
if out_channels is None:
out_channels = hidden_channels
if in_channels is None:
# Current `EdgeNet` hidden_channels must be same with input `h` dim.
in_channels = out_channels
super(MPNNUpdate, self).__init__()
with self.init_scope():
self.message_layer = EdgeNet(out_channels=hidden_channels, nn=nn)
self.update_layer = links.GRU(2 * hidden_channels, out_channels)
self.in_channels = in_channels # currently it is not used...
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.nn = nn
def __init__(self, params):
self.__dict__.update(params)
super(GRU_Baseline, self).__init__(
embed=L.EmbedID(self.n_vocab + 3, self.word_dim),
gru=L.GRU(n_units=self.n_units, n_inputs=self.word_dim),
# l2v = L.Linear(
# in_size = self.n_units,
# out_size = self.n_vocab + 2
# )
# blackout = L.BlackOut(
# in_size = self.n_units,
# counts = self.vocab_counts,
# sample_size = self.blackout_sample_size
# )
# bottleneck = L.Linear(
# in_size = self.n_units,
# out_size = 128
# ),
bh_softmax=L.BinaryHierarchicalSoftmax(in_size=self.n_units,
tree=self.huffman_tree))
def __init__(self, src_vocab, tgt_vocab, args):
super().__init__()
with self.init_scope():
self.word2embed = chainLinks.EmbedID(src_vocab.size,
args.nhid,
ignore_label=-1)
# W_1, U
self.embed2hidden = chainLinks.GRU(args.nhid, args.nhid)
# W_2
self.W_2 = chainLinks.Linear(args.nhid, tgt_vocab.size)
self.vocab_size = src_vocab.size
self.hidden_size = args.nhid
if args.useDropout:
self.use_dropout = args.useDropout
self.dropoutr = args.dlr
else:
self.use_dropout = None
self.dropoutr = 0
util.trace('{}'.format(chainer.global_config.__dict__))
def __init__(self, n_voc, emb_dim, hid_dim, seq_len, gpu_num):
super(Generator, self).__init__()
self.n_voc = n_voc
self.hid_dim = hid_dim
self.emb_dim = emb_dim
self.seq_len = seq_len
self.gpu_num = gpu_num
self._gpu_id = None
self.voc_size = xp.array([i for i in range(n_voc)])
w = I.Normal(1.)
with self.init_scope():
self.embeddings = L.EmbedID(n_voc, emb_dim, initialW=w)
self.gru = L.GRU(emb_dim,
hid_dim,
init=w,
inner_init=w,
bias_init=I.Zero())
self.gru2out = L.Linear(hid_dim,
n_voc,
initialW=w,
initial_bias=I.Zero())
def __init__(self,
hidden_dim,
out_dim,
n_atom_types,
radius,
concat_hidden=False,
weight_tying=True):
super(GGNN, self).__init__()
num_layer = 1 if weight_tying else radius
with self.init_scope():
self.embed = L.EmbedID(n_atom_types, hidden_dim)
self.edge_layer = L.Linear(hidden_dim,
self.NUM_EDGE_TYPE * hidden_dim)
self.update_layer = L.GRU(2 * hidden_dim, hidden_dim)
self.i_layers = chainer.ChainList(
*[L.Linear(2 * hidden_dim, out_dim) for _ in range(num_layer)])
self.j_layers = chainer.ChainList(
*[L.Linear(hidden_dim, out_dim) for _ in range(num_layer)])
self.out_dim = out_dim
self.hidden_dim = hidden_dim
self.radius = radius
self.concat_hidden = concat_hidden
self.weight_tying = weight_tying
def __init__(self, embed_size, hidden_size):
super(Encoder, self).__init__(
GRU = links.GRU(embed_size, hidden_size),
)
def __init__(self, embed_size, hidden_size, num_layers):
super(MultiLayerGRUEncoder, self).__init__()
self.add_link(links.GRU(hidden_size,embed_size))
for i in num_layers:
self.add_link(links.GRU(hidden_size,hidden_size))
self.num_layers = num_layers
def setUp(self):
self.gru = links.GRU(10, 10)
def __init__(self, in_size, hidden_size, out_size):
super(LSTM, self).__init__(xh=L.Linear(in_size, hidden_size),
hh=L.GRU(hidden_size, hidden_size),
hy=L.Linear(hidden_size, out_size))
def setUp(self):
self.link = links.GRU(8)
self.x = numpy.random.uniform(-1, 1, (3, 8)).astype(numpy.float32)
self.h = numpy.random.uniform(-1, 1, (3, 8)).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (3, 8)).astype(numpy.float32)
def __init__(self, hidden_dim):
super(GruRnn, self).__init__(gru=L.GRU(hidden_dim, hidden_dim), )
self.hidden_dim = hidden_dim
def __init__(self, hidden_dim=16, hidden_dim_super=16,
n_layers=4, n_heads=8,
dropout_ratio=0.5,
concat_hidden=False,
tying_flag=False,
gpu=-1):
super(GWM, self).__init__()
num_layer = n_layers
if tying_flag:
num_layer = 1
with self.init_scope():
#
# for Transmitter unit
#
self.F_super = chainer.ChainList(
*[L.Linear(in_size=hidden_dim_super, out_size=hidden_dim_super)
for _ in range(num_layer)]
)
self.V_super = chainer.ChainList(
*[L.Linear(hidden_dim * n_heads, hidden_dim * n_heads)
for _ in range(num_layer)]
)
self.W_super = chainer.ChainList(
*[L.Linear(hidden_dim * n_heads, hidden_dim_super)
for _ in range(num_layer)]
)
self.B = chainer.ChainList(
*[GraphLinear(n_heads * hidden_dim, n_heads * hidden_dim_super)
for _ in range(num_layer)]
)
#
# for Warp Gate unit
#
self.gate_dim = hidden_dim
self.H_local = chainer.ChainList(
*[GraphLinear(in_size=hidden_dim, out_size=self.gate_dim)
for _ in range(num_layer)]
)
self.G_local = chainer.ChainList(
*[GraphLinear(in_size=hidden_dim_super, out_size=self.gate_dim)
for _ in range(num_layer)]
)
self.gate_dim_super = hidden_dim_super
self.H_super = chainer.ChainList(
*[L.Linear(in_size=hidden_dim, out_size=self.gate_dim_super)
for _ in range(num_layer)]
)
self.G_super = chainer.ChainList(
*[L.Linear(in_size=hidden_dim_super, out_size=self.gate_dim_super)
for _ in range(num_layer)]
)
# GRU's. not layer-wise (recurrent through layers)
self.GRU_local = L.GRU(in_size=hidden_dim, out_size=hidden_dim)
self.GRU_super = L.GRU(in_size=hidden_dim_super, out_size=hidden_dim_super)
# end init_scope-with
self.hidden_dim = hidden_dim
self.hidden_dim_super = hidden_dim_super
self.n_layers = n_layers
self.n_heads = n_heads
self.dropout_ratio = dropout_ratio
self.concat_hidden = concat_hidden
self.tying_flag = tying_flag
def __init__(self,
hidden_dim=16,
hidden_dim_super=16,
n_layers=4,
n_heads=8,
dropout_ratio=-1,
tying_flag=False,
activation=functions.relu,
wgu_activation=functions.sigmoid,
gtu_activation=functions.tanh):
super(GWM, self).__init__()
n_use_layers = 1 if tying_flag else n_layers
with self.init_scope():
self.update_super = chainer.ChainList(*[
links.Linear(in_size=hidden_dim_super,
out_size=hidden_dim_super)
for _ in range(n_use_layers)
])
# for Transmitter unit
self.super_transmitter = chainer.ChainList(*[
SuperNodeTransmitterUnit(hidden_dim=hidden_dim,
hidden_dim_super=hidden_dim_super,
dropout_ratio=dropout_ratio)
for _ in range(n_use_layers)
])
self.graph_transmitter = chainer.ChainList(*[
GraphTransmitterUnit(hidden_dim=hidden_dim,
hidden_dim_super=hidden_dim_super,
n_heads=n_heads,
dropout_ratio=dropout_ratio,
activation=gtu_activation)
for _ in range(n_use_layers)
])
# for Warp Gate unit
self.wgu_local = chainer.ChainList(*[
WarpGateUnit(output_type='graph',
hidden_dim=hidden_dim,
dropout_ratio=dropout_ratio,
activation=wgu_activation)
for _ in range(n_use_layers)
])
self.wgu_super = chainer.ChainList(*[
WarpGateUnit(output_type='super',
hidden_dim=hidden_dim_super,
dropout_ratio=dropout_ratio,
activation=wgu_activation)
for _ in range(n_use_layers)
])
# Weight tying: not layer-wise but recurrent through layers
self.GRU_local = links.GRU(in_size=hidden_dim, out_size=hidden_dim)
self.GRU_super = links.GRU(in_size=hidden_dim_super,
out_size=hidden_dim_super)
# end init_scope-with
self.hidden_dim = hidden_dim
self.hidden_dim_super = hidden_dim_super
self.n_layers = n_layers
self.n_heads = n_heads
self.dropout_ratio = dropout_ratio
self.tying_flag = tying_flag
self.activation = activation
self.wgu_activation = wgu_activation
