def __init__(
self,
state_dim: int,
candidate_dim: int,
num_stacked_layers: int,
num_heads: int,
dim_model: int,
dim_feedforward: int,
max_src_seq_len: int,
max_tgt_seq_len: int,
):
"""
A reward network that predicts slate reward.
It uses a transformer-based encoder to encode the items shown in the slate.
The slate reward is predicted by attending all encoder steps' outputs.
For convenience, Seq2SlateRewardModel and Seq2SlateTransformerModel share
the same parameter notations. Therefore, the reward model's encoder is
actually applied on target sequences (i.e., slates) referred in
Seq2SlateTransformerModel.
Note that max_src_seq_len is the
"""
super().__init__()
self.state_dim = state_dim
self.candidate_dim = candidate_dim
self.num_stacked_layers = num_stacked_layers
self.num_heads = num_heads
self.dim_model = dim_model
self.dim_feedforward = dim_feedforward
self.max_src_seq_len = max_src_seq_len
self.max_tgt_seq_len = max_tgt_seq_len
c = copy.deepcopy
attn = MultiHeadedAttention(num_heads, dim_model)
ff = PositionwiseFeedForward(dim_model, dim_feedforward)
self.encoder = Encoder(EncoderLayer(dim_model, c(attn), c(ff)),
num_stacked_layers)
self.decoder = Decoder(
DecoderLayer(dim_model, c(attn), c(attn), c(ff)),
num_stacked_layers)
self.candidate_embedder = Embedder(candidate_dim, dim_model // 2)
self.state_embedder = Embedder(state_dim, dim_model // 2)
self.positional_encoding = PositionalEncoding(
dim_model, max_len=2 * (max_src_seq_len + max_tgt_seq_len))
self.proj = nn.Linear(dim_model, 1)
self.decoder_start_vec = nn.Parameter(torch.zeros(candidate_dim),
requires_grad=True)
# Initialize parameters with Glorot / fan_avg.
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def __init__(
self,
state_dim: int,
candidate_dim: int,
num_stacked_layers: int,
num_heads: int,
dim_model: int,
dim_feedforward: int,
max_src_seq_len: int,
max_tgt_seq_len: int,
):
"""
A reward network that predicts slate reward.
It uses a transformer-based encoder to encode the items shown in the slate.
The slate reward is predicted by attending all encoder steps' outputs.
"""
super().__init__(
state_dim,
candidate_dim,
dim_model,
num_stacked_layers,
max_src_seq_len,
max_tgt_seq_len,
)
self.num_heads = num_heads
self.dim_feedforward = dim_feedforward
c = copy.deepcopy
attn = MultiHeadedAttention(num_heads, dim_model)
ff = PositionwiseFeedForward(dim_model, dim_feedforward)
self.encoder = Encoder(
EncoderLayer(dim_model, c(attn), c(ff)), num_stacked_layers
)
self.decoder = Decoder(
DecoderLayer(dim_model, c(attn), c(attn), c(ff)), num_stacked_layers
)
self.positional_encoding = PositionalEncoding(
dim_model, max_len=2 * (max_src_seq_len + max_tgt_seq_len)
)
self.proj = nn.Linear(dim_model, 1)
self.decoder_start_vec = nn.Parameter(
torch.zeros(candidate_dim), requires_grad=True
)
self._init_params()