def __init__(self, corpus, config): super(ContEncoder, self).__init__(config) self.vocab = corpus.vocab self.rev_vocab = corpus.rev_vocab self.vocab_size = len(self.vocab) self.go_id = self.rev_vocab[BOS] self.eos_id = self.rev_vocab[EOS] self.config = config self.embedding = nn.Embedding(self.vocab_size, config.embed_size, padding_idx=self.rev_vocab[PAD]) self.utt_encoder = RnnUttEncoder(config.utt_cell_size, 0.0, bidirection=False, # bidirection=True in the original code use_attn=config.utt_type == 'attn_rnn', vocab_size=self.vocab_size, embedding=self.embedding) self.ctx_encoder = EncoderRNN(self.utt_encoder.output_size, config.ctx_cell_size, 0.0, 0.0, config.num_layer, config.rnn_cell, variable_lengths=self.config.fix_batch)
def __init__(self, corpus, config, name2action): super(ActionEncoder, self).__init__(config) self.name2action_dict = name2action self.action_num = config.action_num self.vocab = corpus.vocab self.rev_vocab = corpus.rev_vocab self.vocab_size = len(self.vocab) # if self.action_num != len(self.name2action_dict.keys()) + 1: # raise ValueError("the action space should include one spare action to cover some actions \ # that are not in any learned action clusters ") self.x_embedding = nn.Embedding(self.vocab_size, config.embed_size) self.x_encoder = EncoderRNN(config.embed_size, config.dec_cell_size, dropout_p=config.dropout, rnn_cell=config.rnn_cell, variable_lengths=False) self.q_y = nn.Linear(config.dec_cell_size, config.y_size * config.k) self.config =config
def __init__(self, corpus, config): super(StED, self).__init__(config) self.vocab = corpus.vocab self.rev_vocab = corpus.rev_vocab self.vocab_size = len(self.vocab) self.go_id = self.rev_vocab[BOS] self.eos_id = self.rev_vocab[EOS] if not hasattr(config, "freeze_step"): config.freeze_step = 6000 # build model here # word embeddings self.x_embedding = nn.Embedding(self.vocab_size, config.embed_size) # latent action learned self.x_encoder = EncoderRNN(config.embed_size, config.dec_cell_size, dropout_p=config.dropout, rnn_cell=config.rnn_cell, variable_lengths=False) self.q_y = nn.Linear(config.dec_cell_size, config.y_size * config.k) self.x_init_connector = nn_lib.LinearConnector(config.y_size * config.k, config.dec_cell_size, config.rnn_cell == 'lstm') # decoder self.prev_decoder = DecoderRNN(self.vocab_size, config.max_dec_len, config.embed_size, config.dec_cell_size, self.go_id, self.eos_id, n_layers=1, rnn_cell=config.rnn_cell, input_dropout_p=config.dropout, dropout_p=config.dropout, use_attention=False, use_gpu=config.use_gpu, embedding=self.x_embedding) self.next_decoder = DecoderRNN(self.vocab_size, config.max_dec_len, config.embed_size, config.dec_cell_size, self.go_id, self.eos_id, n_layers=1, rnn_cell=config.rnn_cell, input_dropout_p=config.dropout, dropout_p=config.dropout, use_attention=False, use_gpu=config.use_gpu, embedding=self.x_embedding) # Encoder-Decoder STARTS here self.embedding = nn.Embedding(self.vocab_size, config.embed_size, padding_idx=self.rev_vocab[PAD]) self.utt_encoder = RnnUttEncoder(config.utt_cell_size, config.dropout, bidirection=False, # bidirection=True in the original code use_attn=config.utt_type == 'attn_rnn', vocab_size=self.vocab_size, embedding=self.embedding) self.ctx_encoder = EncoderRNN(self.utt_encoder.output_size, config.ctx_cell_size, 0.0, config.dropout, config.num_layer, config.rnn_cell, variable_lengths=config.fix_batch) # FNN to get Y self.p_fc1 = nn.Linear(config.ctx_cell_size, config.ctx_cell_size) self.p_y = nn.Linear(config.ctx_cell_size, config.y_size * config.k) # connector self.c_init_connector = nn_lib.LinearConnector(config.y_size * config.k, config.dec_cell_size, config.rnn_cell == 'lstm') # decoder self.decoder = DecoderRNN(self.vocab_size, config.max_dec_len, config.embed_size, config.dec_cell_size, self.go_id, self.eos_id, n_layers=1, rnn_cell=config.rnn_cell, input_dropout_p=config.dropout, dropout_p=config.dropout, use_attention=config.use_attn, attn_size=config.dec_cell_size, attn_mode=config.attn_type, use_gpu=config.use_gpu, embedding=self.embedding) # force G(z,c) has z if config.use_attribute: self.attribute_loss = criterions.NLLEntropy(-100, config) self.cat_connector = nn_lib.GumbelConnector(config.use_gpu) self.greedy_cat_connector = nn_lib.GreedyConnector() self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config) self.cat_kl_loss = criterions.CatKLLoss() self.log_uniform_y = Variable(torch.log(torch.ones(1) / config.k)) self.entropy_loss = criterions.Entropy() if self.use_gpu: self.log_uniform_y = self.log_uniform_y.cuda() self.kl_w = 0.0
def __init__(self, corpus, config): super(DiVST, self).__init__(config) self.vocab = corpus.vocab self.rev_vocab = corpus.rev_vocab self.vocab_size = len(self.vocab) self.embed_size = config.embed_size self.max_utt_len = config.max_utt_len self.go_id = self.rev_vocab[BOS] self.eos_id = self.rev_vocab[EOS] self.num_layer = config.num_layer self.dropout = config.dropout self.enc_cell_size = config.enc_cell_size self.dec_cell_size = config.dec_cell_size self.rnn_cell = config.rnn_cell self.max_dec_len = config.max_dec_len self.use_attn = config.use_attn self.beam_size = config.beam_size self.utt_type = config.utt_type self.bi_enc_cell = config.bi_enc_cell self.attn_type = config.attn_type self.enc_out_size = self.enc_cell_size*2 if self.bi_enc_cell else self.enc_cell_size # build model here self.embedding = nn.Embedding(self.vocab_size, self.embed_size, padding_idx=self.rev_vocab[PAD]) self.x_encoder = EncoderRNN(self.embed_size, self.enc_cell_size, bidirection=self.bi_enc_cell, dropout_p=self.dropout, rnn_cell=self.rnn_cell, variable_lengths=False) self.q_y = nn.Linear(self.enc_out_size, config.y_size * config.k) self.cat_connector = nn_lib.GumbelConnector() self.dec_init_connector = nn_lib.LinearConnector(config.y_size * config.k, self.dec_cell_size, self.rnn_cell == 'lstm') self.prev_decoder = DecoderRNN(self.vocab_size, self.max_dec_len, self.embed_size, self.dec_cell_size, self.go_id, self.eos_id, n_layers=1, rnn_cell=self.rnn_cell, input_dropout_p=self.dropout, dropout_p=self.dropout, use_attention=self.use_attn, attn_size=self.enc_cell_size, attn_mode=self.attn_type, use_gpu=self.use_gpu, embedding=self.embedding) self.next_decoder = DecoderRNN(self.vocab_size, self.max_dec_len, self.embed_size, self.dec_cell_size, self.go_id, self.eos_id, n_layers=1, rnn_cell=self.rnn_cell, input_dropout_p=self.dropout, dropout_p=self.dropout, use_attention=self.use_attn, attn_size=self.enc_cell_size, attn_mode=self.attn_type, use_gpu=self.use_gpu, embedding=self.embedding) self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config) self.cat_kl_loss = criterions.CatKLLoss() self.cross_ent_loss = criterions.CrossEntropyoss() self.entropy_loss = criterions.Entropy() self.log_uniform_y = Variable(torch.log(torch.ones(1) / config.k)) if self.use_gpu: self.log_uniform_y = self.log_uniform_y.cuda() self.kl_w = 1.0
def __init__(self, corpus, config): super(DirVAE, self).__init__(config) self.vocab = corpus.vocab self.rev_vocab = corpus.rev_vocab self.vocab_size = len(self.vocab) self.embed_size = config.embed_size self.max_utt_len = config.max_utt_len self.go_id = self.rev_vocab[BOS] self.eos_id = self.rev_vocab[EOS] self.num_layer = config.num_layer self.dropout = config.dropout self.enc_cell_size = config.enc_cell_size self.dec_cell_size = config.dec_cell_size self.rnn_cell = config.rnn_cell self.max_dec_len = config.max_dec_len self.use_attn = config.use_attn self.beam_size = config.beam_size self.utt_type = config.utt_type self.bi_enc_cell = config.bi_enc_cell self.attn_type = config.attn_type self.enc_out_size = self.enc_cell_size * 2 if self.bi_enc_cell else self.enc_cell_size self.full_kl_step = 4200.0 # build model here self.embedding = nn.Embedding(self.vocab_size, self.embed_size, padding_idx=self.rev_vocab[PAD]) self.x_encoder = EncoderRNN(self.embed_size, self.enc_cell_size, dropout_p=self.dropout, rnn_cell=self.rnn_cell, variable_lengths=self.config.fix_batch, bidirection=self.bi_enc_cell) # Dirichlet Topic Model prior self.h_dim = config.latent_size self.a = 1. * np.ones((1, self.h_dim)).astype(np.float32) prior_mean = torch.from_numpy( (np.log(self.a).T - np.mean(np.log(self.a), 1)).T) prior_var = torch.from_numpy( (((1.0 / self.a) * (1 - (2.0 / self.h_dim))).T + (1.0 / (self.h_dim * self.h_dim)) * np.sum(1.0 / self.a, 1)).T) prior_logvar = prior_var.log() self.register_buffer('prior_mean', prior_mean) self.register_buffer('prior_var', prior_var) self.register_buffer('prior_logvar', prior_logvar) self.logvar_fc = nn.Sequential( nn.Linear(self.enc_cell_size, np.maximum(config.latent_size * 2, 100)), nn.Tanh(), nn.Linear(np.maximum(config.latent_size * 2, 100), config.latent_size)) self.mean_fc = nn.Sequential( nn.Linear(self.enc_cell_size, np.maximum(config.latent_size * 2, 100)), nn.Tanh(), nn.Linear(np.maximum(config.latent_size * 2, 100), config.latent_size)) self.mean_bn = nn.BatchNorm1d(self.h_dim) # bn for mean self.logvar_bn = nn.BatchNorm1d(self.h_dim) # bn for logvar self.decoder_bn = nn.BatchNorm1d(self.vocab_size) self.logvar_bn.weight.requires_grad = False self.mean_bn.weight.requires_grad = False self.decoder_bn.weight.requires_grad = False self.logvar_bn.weight.fill_(1) self.mean_bn.weight.fill_(1) self.decoder_bn.weight.fill_(1) # self.q_y = nn.Linear(self.enc_out_size, self.h_dim) #self.cat_connector = nn_lib.GumbelConnector() # Prior for the Generation # self.z_mean = nn.Sequential( # nn.Linear(self.enc_cell_size, np.maximum(config.latent_size * 2, 100)), # nn.Tanh(), # nn.Linear(np.maximum(config.latent_size * 2, 100), config.latent_size) # ) # self.z_logvar = self.z_mean = nn.Sequential( # nn.Linear(self.enc_cell_size, np.maximum(config.latent_size * 2, 100)), # nn.Tanh(), # nn.Linear(np.maximum(config.latent_size * 2, 100), config.latent_size) # ) self.dec_init_connector = nn_lib.LinearConnector( config.latent_size, #+ self.h_dim, self.dec_cell_size, self.rnn_cell == 'lstm', has_bias=False) self.decoder = DecoderRNN(self.vocab_size, self.max_dec_len, self.embed_size, self.dec_cell_size, self.go_id, self.eos_id, n_layers=1, rnn_cell=self.rnn_cell, input_dropout_p=self.dropout, dropout_p=self.dropout, use_attention=self.use_attn, attn_size=self.enc_cell_size, attn_mode=self.attn_type, use_gpu=self.use_gpu, embedding=self.embedding) self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config) #self.cat_kl_loss = criterions.CatKLLoss() #self.cross_ent_loss = criterions.CrossEntropyoss() self.entropy_loss = criterions.Entropy() # self.log_py = nn.Parameter(torch.log(torch.ones(self.config.y_size, # self.config.k)/config.k), # requires_grad=True) # self.register_parameter('log_py', self.log_py) # self.log_uniform_y = Variable(torch.log(torch.ones(1) / config.k)) # if self.use_gpu: # self.log_uniform_y = self.log_uniform_y.cuda() # BOW loss self.bow_project = nn.Sequential( #nn.Linear(self.h_dim + config.latent_size, self.vocab_size), nn.Linear(config.latent_size, self.vocab_size), self.decoder_bn) self.kl_w = 0.0