def build()

in bert_layer.py [0:0]

• 33 lines of code
• 15 McCabe index (conditional complexity)

    def build(self, input_shape):
        print('Trainable', self.trainable)
        self.bert = hub.Module(
            self.bert_path, trainable=self.trainable, name=f"{self.name}_module"
        )

        # Remove unused layers
        trainable_vars = self.bert.variables
        if self.pooling == "first":
            trainable_vars = [var for var in trainable_vars if not "/cls/" in var.name]
            trainable_layers = ["pooler/dense"]

        elif self.pooling == "mean":
            trainable_vars = [
                var
                for var in trainable_vars
                if not "/cls/" in var.name and not "/pooler/" in var.name
            ]
            trainable_layers = []
        else:
            raise NameError(
                f"Undefined pooling type (must be either first or mean, but is {self.pooling}"
            )

        # Select how many layers to fine tune
        for i in range(self.n_fine_tune_layers):
            trainable_layers.append(f"encoder/layer_{str(11 - i)}")

        # Update trainable vars to contain only the specified layers
        trainable_vars = [
            var
            for var in trainable_vars
            if any([l in var.name for l in trainable_layers])
        ]

        # Add to trainable weights
        for var in trainable_vars:
            self._trainable_weights.append(var)

        for var in self.bert.variables:
            if var not in self._trainable_weights:
                self._non_trainable_weights.append(var)

        super(BertLayer, self).build(input_shape)