def __init__()

in ttw/models/language.py [0:0]

• 27 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, inp_emb_sz, hidden_sz, num_tokens, apply_masc=True, T=1):
        super(GuideLanguage, self).__init__()
        self.hidden_sz = hidden_sz
        self.inp_emb_sz = inp_emb_sz
        self.num_tokens = num_tokens
        self.apply_masc = apply_masc
        self.T = T

        self.embed_fn = nn.Embedding(num_tokens, inp_emb_sz, padding_idx=0)
        self.encoder_fn = nn.LSTM(inp_emb_sz, hidden_sz//2, batch_first=True, bidirectional=True)
        self.cbow_fn = CBoW(11, hidden_sz)

        self.T_prediction_fn = nn.Linear(hidden_sz, T+1)

        self.feat_control_emb = nn.Parameter(torch.FloatTensor(hidden_sz).normal_(0.0, 0.1))
        self.feat_control_step_fn = ControlStep(hidden_sz)

        if apply_masc:
            self.act_control_emb = nn.Parameter(torch.FloatTensor(hidden_sz).normal_(0.0, 0.1))
            self.act_control_step_fn = ControlStep(hidden_sz)
            self.action_linear_fn = nn.Linear(hidden_sz, 9)

        self.landmark_write_gate = nn.ParameterList()
        self.obs_write_gate = nn.ParameterList()
        for _ in range(T + 1):
            self.landmark_write_gate.append(nn.Parameter(torch.FloatTensor(1, hidden_sz, 1, 1).normal_(0, 0.1)))
            self.obs_write_gate.append(nn.Parameter(torch.FloatTensor(1, hidden_sz).normal_(0.0, 0.1)))

        if apply_masc:
            self.masc_fn = MASC(self.hidden_sz)
        else:
            self.masc_fn = NoMASC(self.hidden_sz)

        self.loss = nn.CrossEntropyLoss()