def __init__(self, params):
super(SequenceToSequence, self).__init__()
# load pretrain word2vec weight matrix
self.embedding_matrix = load_word2vec(params)
self.params = params
"""
batch_size : 256
embed_size : 256
enc_units : 256
dec_units : 256
attn_units : 256
"""
self.encoder = rnn_encoder.Encoder(params["vocab_size"],
params["embed_size"],
params["enc_units"],
params["batch_size"],
self.embedding_matrix)
# 计算score用的是BahdanauAttention 相加的计算方式
self.attention = rnn_decoder.BahdanauAttention(params["attn_units"])
self.decoder = rnn_decoder.Decoder(params["vocab_size"],
params["embed_size"],
params["dec_units"],
params["batch_size"],
self.embedding_matrix)
def __init__(self, params):
super(PGN, self).__init__()
self.embedding_matrix = load_word2vec(params)
self.params = params
self.encoder = Encoder(params["vocab_size"], params["embed_size"],
params["enc_units"], params["batch_size"],
self.embedding_matrix)
self.attention = BahdanauAttention(params["attn_units"])
self.decoder = Decoder(params["vocab_size"], params["embed_size"],
params["dec_units"], params["batch_size"],
self.embedding_matrix)
self.pointer = Pointer()
def __init__(self, params):
super(SequenceToSequence, self).__init__()
self.embedding_matrix = load_word2vec(params)
self.params = params
self.encoder = rnn_encoder.Encoder(params["vocab_size"],
params["embed_size"],
params["enc_units"],
params["batch_size"],
self.embedding_matrix)
self.attention = rnn_decoder.BahdanauAttention(params["attn_units"])
self.decoder = rnn_decoder.Decoder(params["vocab_size"],
params["embed_size"],
params["dec_units"],
params["batch_size"],
self.embedding_matrix)
def __init__(self, params):
super(SequenceToSequence, self).__init__()
self.embedding_matrix = load_word2vec(params) # 词向量矩阵
self.params = params
self.encoder = rnn_encoder.Encoder(params["vocab_size"], # 加载字典中词的数量
params["embed_size"], # 词嵌入向量大小
params["enc_units"], # encoder 单元个数
params["batch_size"], # 分批次加载数据的大小
self.embedding_matrix)
self.attention = rnn_decoder.BahdanauAttention(params["attn_units"]) # attention 单元个数
self.decoder = rnn_decoder.Decoder(params["vocab_size"],
params["embed_size"],
params["dec_units"], # decoder 单元个数
params["batch_size"],
self.embedding_matrix)
def __init__(self, params):
super(PGN, self).__init__()
self.embedding_matrix = load_word2vec(
params) # 才开始涉及到向量：建立embedding表按自定义的字典来，如果没有则置全0
self.params = params
self.encoder = rnn_encoder.Encoder(params["vocab_size"],
params["embed_size"],
params["enc_units"],
params["batch_size"],
self.embedding_matrix)
self.attention = rnn_decoder.BahdanauAttentionCoverage(
params["attn_units"])
self.decoder = rnn_decoder.Decoder(params["vocab_size"],
params["embed_size"],
params["dec_units"],
params["batch_size"],
self.embedding_matrix)
self.pointer = rnn_decoder.Pointer()
def __init__(self, params):
super(SequenceToSequence, self).__init__()
self.embedding_matrix = load_word2vec(params)
self.params = params
print(params["batch_size"])
self.encoder = encoder.Encoder(vocab_size=params["vocab_size"],
embedding_dim=params["embed_size"],
embedding_matrix=self.embedding_matrix,
enc_units=params["enc_units"],
batch_size=params["batch_size"])
self.attention = attention.BahdanauAttention(units=params["attn_units"]) #attention_units
self.decoder = decoder.Decoder(vocab_size=params["vocab_size"],
embedding_dim=params["embed_size"],
embedding_matrix=self.embedding_matrix,
dec_units=params["dec_units"],
batch_size=params["batch_size"])
def __init__(self, params):
super(PGN, self).__init__()
self.embedding_matrix = load_word2vec(
params, max_vocab_size=params["vocab_size"])
self.params = params
self.encoder = encoder.Encoder(vocab_size=params["vocab_size"],
embedding_dim=params["embed_size"],
embedding_matrix=self.embedding_matrix,
enc_units=params["enc_units"],
batch_size=params["batch_size"])
self.attention = BahdanauAttention(units=params["attn_units"])
self.decoder = Decoder(vocab_size=params["vocab_size"],
embedding_dim=params["embed_size"],
embedding_matrix=self.embedding_matrix,
dec_units=params["dec_units"],
batch_size=params["batch_size"])
self.pointer = Pointer()
