def __init__()

in torchmoji/model_def.py [0:0]

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

    def __init__(self, nb_classes, nb_tokens, feature_output=False, output_logits=False,
                 embed_dropout_rate=0, final_dropout_rate=0, return_attention=False):
        """
        torchMoji model.
        IMPORTANT: The model is loaded in evaluation mode by default (self.eval())

        # Arguments:
            nb_classes: Number of classes in the dataset.
            nb_tokens: Number of tokens in the dataset (i.e. vocabulary size).
            feature_output: If True the model returns the penultimate
                            feature vector rather than Softmax probabilities
                            (defaults to False).
            output_logits:  If True the model returns logits rather than probabilities
                            (defaults to False).
            embed_dropout_rate: Dropout rate for the embedding layer.
            final_dropout_rate: Dropout rate for the final Softmax layer.
            return_attention: If True the model also returns attention weights over the sentence
                              (defaults to False).
        """
        super(TorchMoji, self).__init__()

        embedding_dim = 256
        hidden_size = 512
        attention_size = 4 * hidden_size + embedding_dim

        self.feature_output = feature_output
        self.embed_dropout_rate = embed_dropout_rate
        self.final_dropout_rate = final_dropout_rate
        self.return_attention = return_attention
        self.hidden_size = hidden_size
        self.output_logits = output_logits
        self.nb_classes = nb_classes

        self.add_module('embed', nn.Embedding(nb_tokens, embedding_dim))
        # dropout2D: embedding channels are dropped out instead of words
        # many exampels in the datasets contain few words that losing one or more words can alter the emotions completely
        self.add_module('embed_dropout', nn.Dropout2d(embed_dropout_rate))
        self.add_module('lstm_0', LSTMHardSigmoid(embedding_dim, hidden_size, batch_first=True, bidirectional=True))
        self.add_module('lstm_1', LSTMHardSigmoid(hidden_size*2, hidden_size, batch_first=True, bidirectional=True))
        self.add_module('attention_layer', Attention(attention_size=attention_size, return_attention=return_attention))
        if not feature_output:
            self.add_module('final_dropout', nn.Dropout(final_dropout_rate))
            if output_logits:
                self.add_module('output_layer', nn.Sequential(nn.Linear(attention_size, nb_classes if self.nb_classes > 2 else 1)))
            else:
                self.add_module('output_layer', nn.Sequential(nn.Linear(attention_size, nb_classes if self.nb_classes > 2 else 1),
                                                              nn.Softmax() if self.nb_classes > 2 else nn.Sigmoid()))
        self.init_weights()
        # Put model in evaluation mode by default
        self.eval()