def build_discriminator():
input_line = Input(shape=IMG_SHAPE)
input_shade = Input(shape=IMG_SHAPE)
input_cond = Input((3, ))
input_img = Composite()([input_line, input_shade])
x = CoordinateChannel2D()(PixelwiseConcat()([input_img, input_cond]))
x = residual_block_downscaling(x, (8, 8, 32))
x = residual_block(x, (8, 8, 32))
x = residual_block_downscaling(x, (16, 16, 64))
x = residual_block(x, (16, 16, 64))
x = residual_block_downscaling(x, (32, 32, 128))
x = residual_block(x, (32, 32, 128))
x = SelfAttention()(x)
x = residual_block_downscaling(x, (64, 64, 256))
x = residual_block(x, (64, 64, 256))
x = SelfAttention()(x)
x = residual_block_downscaling(x, (128, 128, 512))
x = residual_block(x, (128, 128, 512))
x = GlobalAvgPool2D()(x)
features = Dropout(0.3)(x)
x = Dense(256)(features)
validity = Dense(1, activation='sigmoid')(x)
return Model([input_cond, input_line, input_shade], validity)
def model(x_train, num_labels, LSTM_units, num_conv_filters, batch_size, F, D):
"""
The proposed model with CNN layer, LSTM RNN layer and self attention layers.
Inputs:
- x_train: required for creating input shape for RNN layer in Keras
- num_labels: number of output classes (int)
- LSTM_units: number of RNN units (int)
- num_conv_filters: number of CNN filters (int)
- batch_size: number of samples to be processed in each batch
- F: the attention length (int)
- D: the length of the output (int)
Returns
- model: A Keras model
"""
cnn_inputs = Input(shape=(x_train.shape[1], x_train.shape[2], 1), batch_size=batch_size, name='rnn_inputs')
cnn_layer = Conv2D(num_conv_filters, kernel_size = (1, x_train.shape[2]), strides=(1, 1), padding='valid', data_format="channels_last")
cnn_out = cnn_layer(cnn_inputs)
sq_layer = Lambda(lambda x: K.squeeze(x, axis = 2))
sq_layer_out = sq_layer(cnn_out)
rnn_layer = LSTM(LSTM_units, return_sequences=True, name='lstm', return_state=True) #return_state=True
rnn_layer_output, _, _ = rnn_layer(sq_layer_out)
encoder_output, attention_weights = SelfAttention(size=F, num_hops=D, use_penalization=False, batch_size = batch_size)(rnn_layer_output)
dense_layer = Dense(num_labels, activation = 'softmax')
dense_layer_output = dense_layer(encoder_output)
model = Model(inputs=cnn_inputs, outputs=dense_layer_output)
print (model.summary())
return model
def _build_eneityencoder(self, embedding_layer, name):
"""The main function to create news encoder of NRMS.
Args:
embedding_layer(obj): embedding layer. # LP modified
Return:
obj: the news encoder of NRMS.
"""
hparams = self.hparams
sequences_input_title = keras.Input(shape=(hparams['title_size'], ),
dtype="int32",
name='sequences_input_title')
embedded_sequences_title = embedding_layer(
sequences_input_title) # TODO shape可能有问题
y = layers.Dropout(hparams['dropout'])(embedded_sequences_title)
y = SelfAttention(hparams['head_num'],
hparams['head_dim'],
seed=self.seed)([y, y, y])
y = layers.Dropout(hparams['dropout'])(y)
pred_title = AttLayer2(hparams['attention_hidden_dim'],
seed=self.seed)(y)
model = keras.Model(sequences_input_title, pred_title, name=name)
return model
def __init__(self, num_classes: int):
super(BertAttentionClassifier, self).__init__()
self.bert = BertModel.from_pretrained('bb_lm_ft/')
self.num_classes = num_classes
self.linear1 = nn.Linear(self.bert.config.hidden_size, 256)
self.self_attention = SelfAttention(256,
batch_first=True,
non_linearity="tanh")
self.out = nn.Linear(256, num_classes)
def __init__(self, bert_weights: str):
super(BertBinaryClassifier, self).__init__()
self.bert = BertModel.from_pretrained(bert_weights)
# Freeze Bert Params
for param in list(self.bert.parameters())[:-10]:
param.requires_grad = False
self.dropout = nn.Dropout(p=.10)
self.linear1 = nn.Linear(self.bert.config.hidden_size, 512)
self.attention = SelfAttention(512, batch_first=True)
self.clf = nn.Linear(512, 2)
def _build_newsencoder(self, embedding_layer):
hparams = self.hparams
sequences_input_title = keras.Input(shape=(hparams.doc_size, ),
dtype="int32")
#embedded_sequences_title = embedding_layer(sequences_input_title)
input_ids = sequences_input_title
input_mask = keras.Input(shape=(hparams.doc_size, ), dtype="int32")
segment_ids = keras.Input(shape=(hparams.doc_size, ), dtype="int32")
input_len = keras.Input(shape=(1, ), dtype="int32")
# bert_inputs = dict(
# input_ids=input_ids,
# input_mask=input_mask,
# segment_ids=segment_ids)
bert_inputs = [input_ids, input_mask, segment_ids]
# bert_path='https://tfhub.dev/google/small_bert/bert_uncased_L-12_H-128_A-2/1'
# mybert = hub.Module(
# bert_path,
# trainable=True,
# #name="{}_module".format(self.name)
# )
# bert_inputs = dict(
# input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids
# )
# bert_output = mybert(inputs=bert_inputs, signature="tokens", as_dict=True)['sequence_output']
#print('???sequences_input_title: ',sequences_input_title)
#bert_inputs = [sequences_input_title[:,x,:] for x in range(3)]
#bert_inputs = sequences_input_title[:,0]
#print("???bert_inputs: ",bert_inputs)
bert_output = BertLayer(n_fine_tune_layers=6)(bert_inputs)
embedded_sequences_title = bert_output
y = layers.Dropout(hparams.dropout)(embedded_sequences_title)
y = SelfAttention(hparams.head_num, hparams.head_dim,
seed=self.seed)([y, y, y, input_len, input_len])
y = layers.Dropout(hparams.dropout)(y)
pred_title = AttLayer2(hparams.attention_hidden_dim,
seed=self.seed)(y, input_len)
self.test1 = keras.Model(
[sequences_input_title, input_mask, segment_ids],
bert_output,
name="test1")
#self.test1=K.function([sequences_input_title,input_mask,segment_ids], [bert_output])
model = keras.Model(
[sequences_input_title, input_mask, segment_ids, input_len],
pred_title,
name="news_encoder")
# model = keras.Model([sequences_input_title], pred_title, name="news_encoder")
#model = keras.Model([sequences_input_title], bert_inputs, name="news_encoder")
return model
def __init__(self, embeddings, nclasses=3, **kwargs):
"""
Define the layer of the model and perform the initializations
of the layers (wherever it is necessary)
Args:
embeddings (numpy.ndarray): the 2D ndarray with the word vectors
nclasses ():
"""
super(AttentiveRNN, self).__init__()
########################################################
# Optional Parameters
########################################################
rnn_size = kwargs.get("rnn_size", 100)
rnn_layers = kwargs.get("rnn_layers", 1)
bidirectional = kwargs.get("bidirectional", False)
noise = kwargs.get("noise", 0.)
dropout_words = kwargs.get("dropout_words", 0.2)
dropout_rnn = kwargs.get("dropout_rnn", 0.2)
trainable_emb = kwargs.get("trainable_emb", False)
########################################################
# define the embedding layer, with the corresponding dimensions
self.embedding = nn.Embedding(num_embeddings=embeddings.shape[0],
embedding_dim=embeddings.shape[1])
# initialize the weights of the Embedding layer,
# with the given pre-trained word vectors
self.init_embeddings(embeddings, trainable_emb)
# the dropout "layer" for the word embeddings
self.drop_emb = nn.Dropout(dropout_words)
# the gaussian noise "layer" for the word embeddings
self.noise_emb = GaussianNoise(noise)
# the RNN layer (or layers)
self.rnn = nn.LSTM(input_size=embeddings.shape[1],
hidden_size=rnn_size,
num_layers=rnn_layers,
bidirectional=bidirectional,
dropout=dropout_rnn,
batch_first=True)
# the dropout "layer" for the output of the RNN
self.drop_rnn = nn.Dropout(dropout_rnn)
if self.rnn.bidirectional:
rnn_size *= 2
self.attention = SelfAttention(rnn_size, batch_first=True)
# the final Linear layer which maps the representation of the sentence,
# to the classes
self.linear = nn.Linear(in_features=rnn_size, out_features=nclasses)
def _build_userencoder(self, titleencoder):
hparams = self.hparams
his_input_title = keras.Input(shape=(hparams.his_size,
hparams.doc_size),
dtype="int32")
h_input_masks = keras.Input(shape=(hparams.his_size, hparams.doc_size),
dtype="int32")
h_segments = keras.Input(shape=(hparams.his_size, hparams.doc_size),
dtype="int32")
h_length = keras.Input(shape=(hparams.his_size, 1), dtype="int32")
all_his = keras.Input(shape=(1, ), dtype="int32")
# his_input_title_reshape=layers.Reshape((hparams.doc_size,))(
# his_input_title
# )
# h_input_masks_reshape=layers.Reshape((hparams.doc_size,))(
# h_input_masks
# )
# h_segments_reshape=layers.Reshape((hparams.doc_size,))(
# h_segments
# )
his_input_title_reshape = K.reshape(his_input_title,
(-1, hparams.doc_size))
h_input_masks_reshape = K.reshape(h_input_masks,
(-1, hparams.doc_size))
h_segments_reshape = K.reshape(h_segments, (-1, hparams.doc_size))
h_length_reshape = K.reshape(h_length, (-1, 1))
#click_title_presents = layers.TimeDistributed(titleencoder)([his_input_title,h_input_masks,h_segments])
click_title_presents1 = titleencoder([
his_input_title_reshape, h_input_masks_reshape, h_segments_reshape,
h_length_reshape
])
print('???1: ', click_title_presents1)
click_title_presents = K.reshape(
click_title_presents1,
(-1, hparams.his_size, click_title_presents1.shape[-1]))
print('???2: ', click_title_presents)
y = SelfAttention(hparams.head_num, hparams.head_dim,
seed=self.seed)([click_title_presents] * 3)
user_present = AttLayer2(hparams.attention_hidden_dim,
seed=self.seed)(y, all_his)
model = keras.Model(
[his_input_title, h_input_masks, h_segments, h_length, all_his],
user_present,
name="user_encoder")
return model
def __init__(self, noise_len, chunks, embedding_dim, cbn_mlp_dim,
batch_size):
super(GeneratorNetwork, self).__init__()
self.split_len = noise_len // chunks
self.concat_len = self.split_len + embedding_dim
self.batch_size = batch_size
self.dense1 = spectral_norm(nn.Linear(self.split_len, 1024))
# self.dropout = nn.Dropout(p=0.5)
# inp, cbn_in, emb_size, cbn_hidden, batch_size, out
self.resblock1 = ResBlockUp(256, self.concat_len, cbn_mlp_dim, 1,
batch_size, 256)
self.resblock2 = ResBlockUp(256, self.concat_len, cbn_mlp_dim, 1,
batch_size, 128)
self.resblock3 = ResBlockUp(128, self.concat_len, cbn_mlp_dim, 1,
batch_size, 128)
self.resblock4 = ResBlockUp(128, self.concat_len, cbn_mlp_dim, 1,
batch_size, 64)
self.resblock5 = ResBlockUp(64, self.concat_len, cbn_mlp_dim, 1,
batch_size, 32)
self.resblock6 = ResBlockUp(32, self.concat_len, cbn_mlp_dim, 1,
batch_size, 16)
self.resblock7 = ResBlockUp(16, self.concat_len, cbn_mlp_dim, 1,
batch_size, 16)
self.resblocks = [
self.resblock1,
self.resblock2,
self.resblock3,
self.resblock4,
self.resblock5,
self.resblock6,
self.resblock7,
]
self.self_attention = SelfAttention(64)
self.penultimate_activation = nn.ReLU()
self.conv = spectral_norm(
nn.Conv2d(in_channels=16,
out_channels=1,
kernel_size=3,
padding=1,
bias=False))
self.bn = nn.BatchNorm2d(1)
self.final_activation = nn.Tanh()
def _build_bodyencoder(self):
hparams = self.hparams
sequences_input_body = keras.Input(shape=(hparams['title_size'], ),
dtype="int32")
embedded_sequences_body = self.bert_model(sequences_input_body)
y = layers.Dropout(hparams['dropout'])(embedded_sequences_body)
y = SelfAttention(hparams['head_num'],
hparams['head_dim'],
seed=self.seed)([y, y, y])
y = layers.Dropout(hparams['dropout'])(y)
pred_body = AttLayer2(hparams['attention_hidden_dim'],
seed=self.seed)(y)
pred_body = layers.Reshape((1, hparams['filter_num']))(pred_body)
model = keras.Model(sequences_input_body,
pred_body,
name="body_encoder")
return model
def __init__(self,
num_classes: int,
bert_weights: str,
dropout: float=.10):
super(BertCRFClassifier, self).__init__()
self.bert = BertModel.from_pretrained(bert_weights)
for param in list(self.bert.parameters())[:-5]:
param.requires_grad = False
hidden_size = self.bert.config.hidden_size
self.span_clf_head = nn.Linear(hidden_size, num_classes)
self.binary_clf_head = nn.Linear(hidden_size, 2)
self.attention = SelfAttention(hidden_size, batch_first=True)
self.dropout = nn.Dropout(p=dropout)
self.crf = CRF(num_tags=num_classes)
def __init__(self, config):
super(ContextAware, self).__init__()
self.config = config
self.word_emb = nn.Embedding(config.data_word_vec.shape[0],
config.data_word_vec.shape[1])
self.word_emb.weight.data.copy_(torch.from_numpy(config.data_word_vec))
self.word_emb.weight.requires_grad = False
self.ner_emb = nn.Embedding(7, config.entity_type_size, padding_idx=0)
self.coref_embed = nn.Embedding(config.max_length,
config.coref_size,
padding_idx=0)
# self.char_emb = nn.Embedding(config.data_char_vec.shape[0], config.data_char_vec.shape[1])
# self.char_emb.weight.data.copy_(torch.from_numpy(config.data_char_vec))
# char_dim = config.data_char_vec.shape[1]
# char_hidden = 100
# self.char_cnn = nn.Conv1d(char_dim, char_hidden, 5)
hidden_size = 128
input_size = config.data_word_vec.shape[
1] + config.coref_size + config.entity_type_size #+ char_hidden
self.rnn = EncoderLSTM(input_size, hidden_size, 1, True, True,
1 - config.keep_prob, False)
self.linear_re = nn.Linear(hidden_size * 2, hidden_size)
self.bili = torch.nn.Bilinear(hidden_size, hidden_size, hidden_size)
self.self_att = SelfAttention(hidden_size, 1.0)
self.bili = torch.nn.Bilinear(hidden_size + config.dis_size,
hidden_size + config.dis_size,
hidden_size)
self.dis_embed = nn.Embedding(20, config.dis_size, padding_idx=10)
self.linear_output = nn.Linear(hidden_size * 2, config.relation_num)
def __init__(self, embeddings, num_classes, **kwargs):
super(RNN, self).__init__()
rnn_hidden_size = kwargs.get("rnn_size", 150)
num_rnn_layers = kwargs.get("num_rnn_layers", 2)
bidirectional = kwargs.get("bidirectional", True)
noise = kwargs.get("noise", 0.5)
dropout_embeds = kwargs.get("dropout_embeds", 0.5)
dropout_rnn = kwargs.get("dropout_rnn", 0.5)
trainable_emb = kwargs.get("trainable_emb", False)
self.embedding = nn.Embedding(num_embeddings=embeddings.shape[0],
embedding_dim=embeddings.shape[1])
self.noise_emb = GaussianNoise(noise)
self.init_embeddings(embeddings, trainable_emb)
self.dropout_embeds = nn.Dropout(dropout_embeds)
self.dropout_rnn = nn.Dropout(dropout_rnn)
self.batch_size = 128
self.seed = 1111
self.shared_lstm = nn.LSTM(input_size=embeddings.shape[1],
hidden_size=rnn_hidden_size,
num_layers=num_rnn_layers,
bidirectional=bidirectional,
dropout=dropout_rnn,
batch_first=True)
if bidirectional:
rnn_hidden_size *= 4
else:
rnn_hidden_size *= 2
self.attention = SelfAttention(attention_size=rnn_hidden_size,
batch_first=True)
self.linear = nn.Linear(rnn_hidden_size, num_classes)
def __init__(self):
super(DiscriminatorNetwork, self).__init__()
self.resblock1 = ResBlockDown(1, 16)
self.resblock2 = ResBlockDown(16, 16)
self.resblock3 = ResBlockDown(16, 32)
self.resblock4 = ResBlockDown(32, 64)
self.resblock5 = ResBlockDown(64, 128)
self.resblock6 = ResBlockDown(128, 128)
self.resblock7 = ResBlockDown(128, 256)
self.resblock8 = ResBlock(256, 256)
self.resdownblocks = [
self.resblock1,
self.resblock2,
self.resblock3,
self.resblock4,
self.resblock5,
self.resblock6,
self.resblock7,
]
self.self_attention = SelfAttention(32)
self.global_sum_pooling = nn.LPPool2d(norm_type=1, kernel_size=(1, 4))
self.dense = spectral_norm(nn.Linear(256, 1))
X = Input(shape=(sequence_length, ), batch_size=batch_size)
# Word-Embedding Layer
embedded = Embedding(input_dim=vocabulary_size, output_dim=embedding_dims)(X)
# Recurrent Layers
if config != 0:
encoder_output, hidden_state, cell_state = CuDNNLSTM(
units=128, return_sequences=True, return_state=True)(embedded)
attention_input = [encoder_output, hidden_state]
else:
encoder_output = CuDNNLSTM(units=128)(embedded)
# Optional Attention Mechanisms
if config == 1:
encoder_output, attention_weights = SelfAttention(
size=128, num_hops=10, use_penalization=False)(encoder_output)
elif config == 2:
encoder_output, attention_weights = Attention(
context='many-to-one', alignment_type='global')(attention_input)
encoder_output = Flatten()(encoder_output)
elif config == 3:
encoder_output, attention_weights = Attention(
context='many-to-one',
alignment_type='local-p*',
window_width=100,
score_function='scaled_dot')(attention_input)
encoder_output = Flatten()(encoder_output)
# Prediction Layer
Y = Dense(units=num_categories, activation='softmax')(encoder_output)
def _def_layers(self):
# word embeddings
if self.use_pretrained_embedding:
self.word_embedding = Embedding(embedding_size=self.word_embedding_size,
vocab_size=self.word_vocab_size,
id2word=self.word_vocab,
dropout_rate=self.embedding_dropout,
load_pretrained=True,
trainable=self.word_embedding_trainable,
embedding_oov_init="random",
pretrained_embedding_path=self.pretrained_embedding_path)
else:
self.word_embedding = Embedding(embedding_size=self.word_embedding_size,
vocab_size=self.word_vocab_size,
trainable=self.word_embedding_trainable,
dropout_rate=self.embedding_dropout)
# node embeddings
self.node_embedding = Embedding(embedding_size=self.node_embedding_size,
vocab_size=self.node_vocab_size,
trainable=self.node_embedding_trainable,
dropout_rate=self.embedding_dropout)
# relation embeddings
self.relation_embedding = Embedding(embedding_size=self.relation_embedding_size,
vocab_size=self.relation_vocab_size,
trainable=self.relation_embedding_trainable,
dropout_rate=self.embedding_dropout)
self.word_embedding_prj = torch.nn.Linear(self.word_embedding_size, self.block_hidden_dim, bias=False)
self.encoder = torch.nn.ModuleList([EncoderBlock(conv_num=self.encoder_conv_num, ch_num=self.block_hidden_dim, k=5, block_hidden_dim=self.block_hidden_dim, n_head=self.n_heads, dropout=self.block_dropout) for _ in range(self.encoder_layers)])
self.rgcns = StackedRelationalGraphConvolution(entity_input_dim=self.node_embedding_size+self.block_hidden_dim, relation_input_dim=self.relation_embedding_size+self.block_hidden_dim, num_relations=self.relation_vocab_size, hidden_dims=self.gcn_hidden_dims, num_bases=self.gcn_num_bases,
use_highway_connections=self.gcn_highway_connections, dropout_rate=self.dropout, real_valued_graph=self.real_valued_graph)
self.attention = CQAttention(block_hidden_dim=self.block_hidden_dim, dropout=self.attention_dropout)
self.attention_prj = torch.nn.Linear(self.block_hidden_dim * 4, self.block_hidden_dim, bias=False)
self.self_attention_text = SelfAttention(self.block_hidden_dim, self.n_heads, self.dropout)
self.self_attention_graph = SelfAttention(self.block_hidden_dim, self.n_heads, self.dropout)
# recurrent memories
self.recurrent_memory_bi_input = LSTMCell(self.block_hidden_dim * 2, self.block_hidden_dim, use_bias=True)
self.recurrent_memory_single_input = LSTMCell(self.block_hidden_dim, self.block_hidden_dim, use_bias=True)
linear_function = NoisyLinear if self.noisy_net else torch.nn.Linear
self.action_scorer_linear_1_tri_input = linear_function(self.block_hidden_dim * 3, self.block_hidden_dim)
self.action_scorer_linear_1_bi_input = linear_function(self.block_hidden_dim * 2, self.block_hidden_dim)
self.action_scorer_linear_2 = linear_function(self.block_hidden_dim, 1)
# text encoder for pretraining tasks
# (we separate this because we don't want to init text encoder with pretrained parameters when training RL)
self.encoder_for_pretraining_tasks = torch.nn.ModuleList([EncoderBlock(conv_num=self.encoder_conv_num, ch_num=self.block_hidden_dim, k=5, block_hidden_dim=self.block_hidden_dim, n_head=self.n_heads, dropout=self.block_dropout) for _ in range(self.encoder_layers)])
# command generation
self.cmd_gen_attention = CQAttention(block_hidden_dim=self.block_hidden_dim, dropout=self.attention_dropout)
self.cmd_gen_attention_prj = torch.nn.Linear(self.block_hidden_dim * 4, self.block_hidden_dim, bias=False)
self.decoder = torch.nn.ModuleList([DecoderBlock(ch_num=self.block_hidden_dim, k=5, block_hidden_dim=self.block_hidden_dim, n_head=self.n_heads, dropout=self.block_dropout) for _ in range(self.decoder_layers)])
self.tgt_word_prj = torch.nn.Linear(self.block_hidden_dim, self.word_vocab_size, bias=False)
self.pointer_softmax = PointerSoftmax(input_dim=self.block_hidden_dim, hidden_dim=self.block_hidden_dim)
# observation generation
self.obs_gen_attention = CQAttention(block_hidden_dim=self.block_hidden_dim, dropout=self.attention_dropout)
self.obs_gen_attention_prj = torch.nn.Linear(self.block_hidden_dim * 4, self.block_hidden_dim, bias=False)
self.obs_gen_decoder = torch.nn.ModuleList([DecoderBlockForObsGen(ch_num=self.block_hidden_dim, k=5, block_hidden_dim=self.block_hidden_dim, n_head=self.n_heads, dropout=self.block_dropout) for _ in range(self.decoder_layers)])
self.obs_gen_tgt_word_prj = torch.nn.Linear(self.block_hidden_dim, self.word_vocab_size, bias=False)
self.obs_gen_linear_1 = torch.nn.Linear(self.block_hidden_dim, self.block_hidden_dim)
self.obs_gen_linear_2 = torch.nn.Linear(self.block_hidden_dim, int(len(self.relation_vocab) / 2) * len(self.node_vocab) * len(self.node_vocab))
self.obs_gen_attention_to_rnn_input = torch.nn.Linear(self.block_hidden_dim * 4, self.block_hidden_dim)
self.obs_gen_graph_rnncell = torch.nn.GRUCell(self.block_hidden_dim, self.block_hidden_dim)
self.observation_discriminator = ObservationDiscriminator(self.block_hidden_dim)
# action prediction
self.ap_attention = CQAttention(block_hidden_dim=self.block_hidden_dim, dropout=self.attention_dropout)
self.ap_attention_prj = torch.nn.Linear(self.block_hidden_dim * 4, self.block_hidden_dim, bias=False)
self.ap_self_attention = SelfAttention(self.block_hidden_dim * 3, self.n_heads, self.dropout)
self.ap_linear_1 = torch.nn.Linear(self.block_hidden_dim * 3, self.block_hidden_dim)
self.ap_linear_2 = torch.nn.Linear(self.block_hidden_dim, 1)
# state prediction
self.sp_attention = CQAttention(block_hidden_dim=self.block_hidden_dim, dropout=self.attention_dropout)
self.sp_attention_prj = torch.nn.Linear(self.block_hidden_dim * 4, self.block_hidden_dim, bias=False)
self.sp_self_attention = SelfAttention(self.block_hidden_dim * 3, self.n_heads, self.dropout)
self.sp_linear_1 = torch.nn.Linear(self.block_hidden_dim * 3, self.block_hidden_dim)
self.sp_linear_2 = torch.nn.Linear(self.block_hidden_dim, 1)
# deep graph infomax
self.dgi_discriminator = DGIDiscriminator(self.gcn_hidden_dims[-1])
def __init__(self, dict_args):
super(RNet, self).__init__()
#character embedding layer
self.use_charemb = dict_args['use_charemb']
self.charemb_rnn_hdim = 0
if self.use_charemb:
self.cvocab_size = dict_args['charvocab_size']
self.charemb_dim = dict_args['charemb_dim']
self.charemb_rnn_hdim = dict_args['charemb_rnn_hdim']
self.charemb_rnn_type = dict_args['charemb_rnn_type']
self.charemb_padix = dict_args['charemb_padix']
#input embedding layer
self.wordemb_dim = dict_args['wordemb_dim']
self.contextemb_rnn_hdim = dict_args['contextemb_rnn_hdim']
self.contextemb_rnn_type = dict_args['contextemb_rnn_type']
self.contextemb_num_layers = dict_args['contextemb_num_layers']
#gated attention layer
self.gated_attention_similarity_function = dict_args[
'gated_attention_similarity_function']
self.gated_attentio_rnn_type = dict_args['gated_attentio_rnn_type']
#self matching layer
self.self_matching_similarity_function = dict_args[
'self_matching_similarity_function']
#modeling layer
self.modelinglayer_rnn_hdim = dict_args['contextemb_rnn_hdim']
self.modelinglayer_rnn_type = dict_args['modelinglayer_rnn_type']
self.modelinglayer_num_layers = dict_args['modelinglayer_num_layers']
#question attention layer
self.question_attention_similarity_function = dict_args[
'question_attention_similarity_function']
#pointer network layer
self.pointer_network_similarity_function = dict_args[
'pointer_network_similarity_function']
self.pointer_network_rnn_type = dict_args['pointer_network_rnn_type']
#dropout layer
self.use_dropout = False
self.dropout_rate = 0
if dict_args['dropout_rate'] > 0:
self.use_dropout = True
self.dropout_rate = dict_args['dropout_rate']
#######Dropout layer
if self.use_dropout:
self.dropout_layer = nn.Dropout(p=self.dropout_rate)
#######character embedding layer
if self.use_charemb:
charemb_layer_args = {
'cvocab_size': self.cvocab_size,
'charemb_dim': self.charemb_dim,
'charemb_rnn_hdim': self.charemb_rnn_hdim,
'charemb_rnn_type': self.charemb_rnn_type,
'charemb_padix': self.charemb_padix,
'dropout_rate': self.dropout_rate
}
self.charemb_layer = RNNCharEmb(charemb_layer_args)
#######context embedding layer
contextemb_layer_args = {
'input_dim': 2 * self.charemb_rnn_hdim + self.wordemb_dim,
'rnn_hdim': self.contextemb_rnn_hdim,
'rnn_type': self.contextemb_rnn_type,
'num_layers': self.contextemb_num_layers,
'dropout_rate': self.dropout_rate
}
self.contextemb_layer = BiDirEncoder(contextemb_layer_args)
#######gated attention layer
self.projection_dim = 2 * self.contextemb_rnn_hdim
self.question_projection_weights_u = nn.Parameter(
torch.Tensor(self.projection_dim, 2 * self.contextemb_rnn_hdim))
self.passage_projection_weights_u = nn.Parameter(
torch.Tensor(self.projection_dim, 2 * self.contextemb_rnn_hdim))
self.passage_projection_weights_v = nn.Parameter(
torch.Tensor(self.projection_dim, 2 * self.contextemb_rnn_hdim))
self.dotproduct_weights = nn.Parameter(
torch.Tensor(self.projection_dim, 1))
stdv = 1.0 / math.sqrt(self.question_projection_weights_u.size(-1))
self.question_projection_weights_u.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.passage_projection_weights_u.size(-1))
self.passage_projection_weights_u.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.passage_projection_weights_v.size(-1))
self.passage_projection_weights_v.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.dotproduct_weights.size(-1))
self.dotproduct_weights.data.uniform_(-stdv, stdv)
gated_similarity_function_args = {
'projection_dim': self.projection_dim,
'sequence1_weights': self.question_projection_weights_u,
'sequence2_weights': self.passage_projection_weights_u,
'sequence3_weights': self.passage_projection_weights_v,
'weights': self.dotproduct_weights
}
self.gated_similarity_function_pointer = similarity.ProjectionSimilaritySharedWeights(
gated_similarity_function_args)
gated_attention_layer_args = {
'similarity_function': self.gated_attention_similarity_function,
'similarity_function_pointer':
self.gated_similarity_function_pointer,
'sequence1_dim': 2 * self.contextemb_rnn_hdim,
'sequence2_dim': 2 * self.contextemb_rnn_hdim,
'rnn_type': self.gated_attentio_rnn_type,
'rnn_hdim': 2 * self.contextemb_rnn_hdim,
'gated_attention': dict_args['use_gating']
}
self.use_bidirectional = dict_args['use_bidirectional']
self.gated_attention_layer_forward = MatchAttention(
gated_attention_layer_args)
self.gatedattn_dim = 2 * self.contextemb_rnn_hdim
if self.use_bidirectional:
self.gated_attention_layer_backward = MatchAttention(
gated_attention_layer_args)
self.gatedattn_dim = 4 * self.contextemb_rnn_hdim
#######self matching layer
self.use_selfmatching = dict_args['use_selfmatching']
self.selfmatching_dim = 0
if self.use_selfmatching:
#self.projection_dim = 2*self.contextemb_rnn_hdim #Shared
self.passage_projection_weights_v1 = nn.Parameter(
torch.Tensor(self.projection_dim, self.gatedattn_dim)) #Shared
self.passageprime_projection_weights_v = nn.Parameter(
torch.Tensor(self.projection_dim, self.gatedattn_dim))
self.dotproduct_weights1 = nn.Parameter(
torch.Tensor(self.projection_dim, 1)) #Shared
stdv = 1.0 / math.sqrt(
self.passageprime_projection_weights_v.size(-1))
self.passageprime_projection_weights_v.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.passage_projection_weights_v1.size(-1))
self.passage_projection_weights_v1.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.dotproduct_weights1.size(-1))
self.dotproduct_weights1.data.uniform_(-stdv, stdv)
selfmatching_similarity_function_args = {
'projection_dim': self.projection_dim,
'sequence1_weights': self.passage_projection_weights_v1,
'sequence2_weights': self.passageprime_projection_weights_v,
'weights': self.dotproduct_weights1
}
self.selfmatching_similarity_function_pointer = similarity.ProjectionSimilaritySharedWeights(
selfmatching_similarity_function_args)
self_matching_layer_args = {
'similarity_function': self.self_matching_similarity_function,
'similarity_function_pointer':
self.selfmatching_similarity_function_pointer,
'sequence_dim': 2 * self.contextemb_rnn_hdim,
'projection_dim': 2 * self.contextemb_rnn_hdim
}
self.self_matching_layer = SelfAttention(self_matching_layer_args)
self.selfmatching_dim = self.gatedattn_dim
#######Gated layer
self.gated_selfmatching = self.use_selfmatching and dict_args[
'use_gating']
if self.gated_selfmatching:
self.gate_dim = self.gatedattn_dim + self.selfmatching_dim
self.selfmatchinggate = Gate({
'sigmoidinputdim': self.gate_dim,
'gateinputdim': self.gate_dim
})
#######modeling layer
modeling_layer_args = {
'input_dim': self.gatedattn_dim + self.selfmatching_dim,
'rnn_hdim': self.modelinglayer_rnn_hdim,
'rnn_type': self.modelinglayer_rnn_type,
'num_layers': self.modelinglayer_num_layers,
'dropout_rate': 0
}
self.modeling_layer = BiDirEncoder(modeling_layer_args)
#######question attention layer
self.question_query_vector = nn.Parameter(
torch.Tensor(2 * self.contextemb_rnn_hdim))
stdv = 1.0 / math.sqrt(self.question_query_vector.size(-1))
self.question_query_vector.data.uniform_(-stdv, stdv)
#self.projection_dim = 2*self.contextemb_rnn_hdim #Shared
self.question_projection_weights_u2 = nn.Parameter(
torch.Tensor(self.projection_dim,
2 * self.contextemb_rnn_hdim)) #Shared
self.question_projection_weights_v = nn.Parameter(
torch.Tensor(self.projection_dim, 2 * self.contextemb_rnn_hdim))
self.dotproduct_weights2 = nn.Parameter(
torch.Tensor(self.projection_dim, 1)) #Shared
stdv = 1.0 / math.sqrt(self.question_projection_weights_v.size(-1))
self.question_projection_weights_v.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.question_projection_weights_u2.size(-1))
self.question_projection_weights_u2.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.dotproduct_weights2.size(-1))
self.dotproduct_weights2.data.uniform_(-stdv, stdv)
question_similarity_function_args = {
'projection_dim': self.projection_dim,
'sequence1_weights': self.question_projection_weights_u2,
'sequence2_weights': self.question_projection_weights_v,
'weights': self.dotproduct_weights2
}
self.question_similarity_function_pointer = similarity.ProjectionSimilaritySharedWeights(
question_similarity_function_args)
question_attention_layer_args = {
'similarity_function': self.question_attention_similarity_function,
'similarity_function_pointer':
self.question_similarity_function_pointer,
'sequence1_dim': 2 * self.contextemb_rnn_hdim,
'sequence2_dim': 2 * self.contextemb_rnn_hdim,
'projection_dim': 2 * self.contextemb_rnn_hdim
}
self.question_attention_layer = UniDirAttention(
question_attention_layer_args)
#######pointer network layer
#self.projection_dim = 2*self.contextemb_rnn_hdim #Shared
self.passage_projection_weights_h = nn.Parameter(
torch.Tensor(self.projection_dim, 2 * self.contextemb_rnn_hdim))
self.decoder_projection_weights_h = nn.Parameter(
torch.Tensor(self.projection_dim, 2 * self.contextemb_rnn_hdim))
self.dotproduct_weights3 = nn.Parameter(
torch.Tensor(self.projection_dim, 1)) #Shared
stdv = 1.0 / math.sqrt(self.passage_projection_weights_h.size(-1))
self.passage_projection_weights_h.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.decoder_projection_weights_h.size(-1))
self.decoder_projection_weights_h.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.dotproduct_weights3.size(-1))
self.dotproduct_weights3.data.uniform_(-stdv, stdv)
pointer_similarity_function_args = {
'projection_dim': self.projection_dim,
'sequence1_weights': self.passage_projection_weights_h,
'sequence2_weights': self.decoder_projection_weights_h,
'weights': self.dotproduct_weights3
}
self.pointer_similarity_function_pointer = similarity.ProjectionSimilaritySharedWeights(
pointer_similarity_function_args)
pointer_network_layer_args = {
'similarity_function': self.pointer_network_similarity_function,
'similarity_function_pointer':
self.pointer_similarity_function_pointer,
'sequence_dim': 2 * self.contextemb_rnn_hdim,
'projection_dim': 2 * self.contextemb_rnn_hdim,
'rnn_type': self.pointer_network_rnn_type,
'rnn_hdim': 2 * self.contextemb_rnn_hdim,
}
self.pointer_network_layer = PointerNetwork(pointer_network_layer_args)
def build_generator():
input_cond = Input((3, ))
embed_cond = Dense(128, activation='tanh')(input_cond)
input_img = Input((None, None, IMG_CHAN))
# encoder
d1 = CoordinateChannel2D()(input_img)
d1 = residual_block(d1, (8, 8, 32), True) # 1/1 320
d1 = residual_block(d1, (8, 8, 32))
d2 = residual_block_downscaling(d1, (16, 16, 64)) # 1/2 160
d2 = residual_block(d2, (16, 16, 64))
d2 = residual_block(d2, (16, 16, 64))
d3 = residual_block_downscaling(d2, (32, 32, 128)) # 1/4 80
d3 = residual_block(d3, (32, 32, 128))
d3 = residual_block(d3, (32, 32, 128))
d4 = residual_block_downscaling(d3, (64, 64, 256)) # 1/8 40
d4 = residual_block(d4, (64, 64, 256))
d4 = residual_block(d4, (64, 64, 256))
d5 = residual_block_downscaling(d4, (64, 64, 256)) # 1/16 20
d5 = residual_block(d5, (64, 64, 256))
d5 = residual_block(d5, (64, 64, 256))
# bottleneck
d6 = residual_block_downscaling(d5, (128, 128, 512)) # 1/32 10
d6 = residual_block(d6, (128, 128, 512))
d6 = residual_block(d6, (128, 128, 512))
d6 = filmed_residual_block(embed_cond, CoordinateChannel2D()(d6), 512)
d6 = residual_block(d6, (128, 128, 512))
d6 = residual_block(d6, (128, 128, 512))
d6 = residual_block(d6, (128, 128, 512))
d6 = residual_block(d6, (128, 128, 512))
d6 = SelfAttention()(d6)
# decoder
u1 = residual_block_upscaling(d6, (64, 64, 256)) # 20
u1 = CoordinateChannel2D()(Concatenate()([u1, se(u1, d5)]))
u1 = filmed_residual_block(embed_cond, u1, 256)
u1 = residual_block(u1, (64, 64, 256))
u1 = residual_block(u1, (64, 64, 256))
u1 = SelfAttention()(u1)
s1 = UpSampling2D(16)(Conv2D(1, 1, activation='tanh')(u1))
u2 = residual_block_upscaling(u1, (64, 64, 256)) # 40
u2 = CoordinateChannel2D()(Concatenate()([u2, se(u2, d4)]))
u2 = filmed_residual_block(embed_cond, u2, 256)
u2 = residual_block(u2, (64, 64, 256))
u2 = residual_block(u2, (64, 64, 256))
u2 = SelfAttention()(u2)
u3 = residual_block_upscaling(u2, (32, 32, 128)) # 80
u3 = CoordinateChannel2D()(Concatenate()([u3, se(u3, d3)]))
u3 = filmed_residual_block(embed_cond, u3, 128)
u3 = residual_block(u3, (32, 32, 128))
u3 = residual_block(u3, (32, 32, 128))
u3 = SelfAttention()(u3)
s2 = UpSampling2D(4)(Conv2D(1, 1, activation='tanh')(u3))
u4 = residual_block_upscaling(u3, (16, 16, 64)) # 160
u4 = CoordinateChannel2D()(Concatenate()([u4, se(u4, d2)]))
u4 = filmed_residual_block(embed_cond, u4, 64)
u4 = residual_block(u4, (16, 16, 64))
u4 = residual_block(u4, (16, 16, 64))
u4 = SelfAttention()(u4)
u5 = residual_block_upscaling(u4, (8, 8, 32)) # 320
u5 = CoordinateChannel2D()(Concatenate()([u5, se(u5, d1)]))
u5 = filmed_residual_block(embed_cond, u5, 32)
u5 = residual_block(u5, (8, 8, 32))
u5 = residual_block(u5, (8, 8, 32))
u6 = residual_block(u5, (4, 4, 16), True)
u6 = residual_block(u6, (4, 4, 16))
u6 = residual_block(u6, (4, 4, 16))
output_img = Conv2D(1, 1, activation='tanh')(u6)
return Model([input_cond, input_img], [output_img, s1, s2])
def __init__(self, training=True):
super(VGGATTModel, self).__init__()
# block 1
self.block1_conv1 = Conv2D(
64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block1_conv2 = Conv2D(
64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block1_pool = MaxPooling2D((2, 2),
strides=(2, 2),
name='block1_pool')
self.block1_batch_norm = BatchNormalization(name='block1_batch_norm')
# block 2
self.block2_conv1 = Conv2D(
128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block2_conv2 = Conv2D(
128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block2_pool = MaxPooling2D((2, 2),
strides=(2, 2),
name='block2_pool')
self.block2_batch_norm = BatchNormalization(name='block2_batch_norm')
# Block 3
self.block3_conv1 = Conv2D(
256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block3_conv2 = Conv2D(
256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block3_conv3 = Conv2D(
256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block3_pool = MaxPooling2D((2, 2),
strides=(1, 2),
name='block3_pool')
self.block3_batch_norm = BatchNormalization(name='block3_batch_norm')
# Block 4
self.block4_conv1 = Conv2D(
512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block4_conv2 = Conv2D(
512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block4_conv3 = Conv2D(
512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block4_pool = MaxPooling2D((2, 2),
strides=(1, 2),
name='block4_pool')
self.block4_batch_norm = BatchNormalization(name='block4_batch_norm')
# Block 5
self.blcok5_conv1 = Conv2D(
512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block5_conv2 = Conv2D(
512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block5_conv3 = Conv2D(
512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
self.block5_pool = MaxPooling2D((1, 2),
strides=(1, 2),
name='block5_pool')
self.block5_batch_norm = BatchNormalization(name='block5_batch_norm')
# Block 6
self.block6_reshape = Reshape(target_shape=(-1, 512))
self.self_attention1 = SelfAttention(name='attention')
# Block 7
self.block7_prediction = Dense(units=4651,
kernel_initializer='he_normal',
name='ctc_y')
self.training = training
if not training:
self.block7_softmax_pred = Activation('softmax', name='softmax')
def __init__(self, i_norm=True, noise=False):
super(Net, self).__init__()
self.noise = noise
self.normalization_layer = nn.InstanceNorm2d if i_norm else nn.BatchNorm2d
self.block_0 = PartialConv2d(in_channels=3,
out_channels=64,
kernel_size=7,
stride=2,
padding=3,
multi_channel=True,
return_mask=True)
self.block_1 = PartialConv2d(in_channels=64,
out_channels=128,
kernel_size=5,
stride=2,
padding=2,
multi_channel=True,
return_mask=True)
self.norm_1 = self.normalization_layer(num_features=128)
self.block_2 = PartialConv2d(in_channels=128,
out_channels=256,
kernel_size=5,
stride=1,
padding=2,
multi_channel=True,
return_mask=True)
self.norm_2 = self.normalization_layer(num_features=256)
self.block_3 = PartialConv2d(in_channels=256,
out_channels=128,
kernel_size=5,
stride=2,
padding=2,
multi_channel=True,
return_mask=True)
self.norm_3 = self.normalization_layer(num_features=128)
self.dilated_block_1 = PartialConv2d(in_channels=128,
out_channels=128,
kernel_size=3,
padding=2,
dilation=2,
multi_channel=True,
return_mask=True)
self.dilated_block_2 = PartialConv2d(in_channels=128,
out_channels=128,
kernel_size=3,
padding=4,
dilation=4,
multi_channel=True,
return_mask=True)
self.dilated_block_3 = PartialConv2d(in_channels=128,
out_channels=128,
kernel_size=3,
padding=8,
dilation=8,
multi_channel=True,
return_mask=True)
self.dilated_block_4 = PartialConv2d(
in_channels=128,
out_channels=128,
kernel_size=3,
padding=2,
dilation=2,
stride=1,
multi_channel=True) # decrease spatial dimension??
self.dilated_norm_1 = self.normalization_layer(num_features=128)
self.dilated_norm_2 = self.normalization_layer(num_features=128)
self.dilated_norm_3 = self.normalization_layer(num_features=128)
self.dilated_norm_4 = self.normalization_layer(num_features=128)
# self.s_attention_5 = SelfAttention(in_channels=128)
# self.block_5 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1)
# self.norm_5 = self.normalization_layer(num_features=128)
self.s_attention_6 = SelfAttention(in_channels=128)
self.block_6 = nn.Conv2d(in_channels=256,
out_channels=128,
kernel_size=3,
padding=1)
self.norm_6 = self.normalization_layer(num_features=128)
self.s_attention_7 = SelfAttention(in_channels=384)
self.block_7 = nn.Conv2d(in_channels=384,
out_channels=128,
kernel_size=3,
padding=1)
self.norm_7 = self.normalization_layer(num_features=128)
self.block_8 = nn.Conv2d(in_channels=192,
out_channels=64,
kernel_size=3,
padding=1)
self.norm_8 = self.normalization_layer(num_features=64)
self.block_9 = nn.Conv2d(in_channels=67,
out_channels=3,
kernel_size=3,
padding=1)
self.noise_layer = GaussianNoise() # noise??
self.upsample = nn.UpsamplingNearest2d(scale_factor=2.0)
Y_test) = imdb.load_data(num_words=vocabulary_size)
# Pad & truncate sequences to fixed sequence length
X_train = pad_sequences(sequences=X_train, maxlen=sequence_length)
X_test = pad_sequences(sequences=X_test, maxlen=sequence_length)
# Create word-level binary sentiment classification model
# Input Layer
X = Input(shape=(sequence_length, ), batch_size=batch_size)
# Word-Embedding Layer
embedded = Embedding(input_dim=vocabulary_size, output_dim=embedding_dims)(X)
# Optional Self-Attention Mechanisms
if config == 1:
embedded, attention_weights = SelfAttention(
size=50, num_hops=6, use_penalization=False)(embedded)
elif config == 2:
embedded, attention_weights = SelfAttention(
size=50, num_hops=6, use_penalization=True,
penalty_coefficient=0.1)(embedded)
# Multi-Layer Perceptron
embedded_flattened = Flatten()(embedded)
fully_connected = Dense(units=250, activation='relu')(embedded_flattened)
# Prediction Layer
Y = Dense(units=1, activation='sigmoid')(fully_connected)
# Compile model
model = Model(inputs=X, outputs=Y)
model.compile(loss='binary_crossentropy',
def _build_userencoder(self, his_input_title, his_input_segment,
titleencoder, entityencoder, contextencoder):
"""The main function to create user encoder of NRMS.
Args:
titleencoder(obj): the news encoder of NRMS.
Return:
obj: the user encoder of NRMS.
"""
hparams = self.hparams
# his_input_title = keras.Input(
# shape=(hparams.his_size, hparams.title_size), dtype="int32", name='ue_his_input_title'
# )
#
# his_input_segment = keras.Input(
# shape=(hparams.his_size, hparams.title_size), dtype="int32", name='ue_his_input_segment'
# )
embedded_sequences_title = layers.TimeDistributed(self.bert_model)(
his_input_title) # TODO shape可能有问题 (-1, 50,30,768)
embedded_sequences_title = keras.layers.Reshape(
(hparams['his_size'], hparams['title_size'], 768),
name='embedded_sequences_title_reshape')(embedded_sequences_title)
click_title_presents = layers.TimeDistributed(
titleencoder,
name='news_time_distributed')(embedded_sequences_title)
# y = SelfAttention(hparams['head_num'], hparams['head_dim'], seed=self.seed)(
# [click_title_presents] * 3
# )
y = MultiHeadAttention(hparams['head_num'],
hparams['head_dim'])([click_title_presents] * 3)
if entityencoder is not None:
his_input_title_entity = keras.Input(shape=(hparams['his_size'],
hparams['title_size']),
dtype="int32",
name='his_input_title_entity')
click_title_entity_presents = layers.TimeDistributed(
entityencoder,
name='entity_time_distributed')(his_input_title_entity)
entity_y = SelfAttention(hparams['head_num'],
hparams['head_dim'],
seed=self.seed)(
[click_title_entity_presents] * 3)
if contextencoder is not None:
click_title_context_presents = layers.TimeDistributed(
contextencoder,
name='context_time_distributed')(his_input_title_entity)
context_y = SelfAttention(
hparams['head_num'], hparams['head_dim'],
seed=self.seed)([click_title_context_presents] * 3)
y = layers.Concatenate()([y, entity_y, context_y])
else:
y = layers.Concatenate()([y, entity_y])
user_present = AttLayer2(hparams['attention_hidden_dim'],
seed=self.seed)(y)
if entityencoder is not None:
model = keras.Model(
inputs=[his_input_title, his_input_title_entity],
outputs=user_present,
name="user_encoder")
else:
model = keras.Model(his_input_title,
user_present,
name="user_encoder")
return model
