47966c5e49342ef9ff53c6db75a4905ffe864e4d,pyannote/audio/models.py,TripletLossSequenceEmbedding,_embedding,#TripletLossSequenceEmbedding#Any#,134

Before Change

            model.add(layer)

        // stack final dense layer
        layer = Dense(self.output_dim, activation="tanh")
        model.add(layer)

        // stack L2 normalization layer
        model.add(Lambda(lambda x: K.l2_normalize(x, axis=-1)))

After Change

    def _embedding(self, input_shape):

        inputs = Input(shape=input_shape)
        x = inputs

        // stack LSTM layers
        n_lstm = len(self.lstm)
        for i, output_dim in enumerate(self.lstm):
            return_sequences = i+1 < n_lstm
            if i:
                x = LSTM(output_dim=output_dim,
                             return_sequences=return_sequences,
                             activation="tanh")(x)
            else:
                x = LSTM(input_shape=input_shape,
                             output_dim=output_dim,
                             return_sequences=return_sequences,
                             activation="tanh")(x)

        // stack dense layers
        for i, output_dim in enumerate(self.dense):
            x = Dense(output_dim, activation="tanh")(x)

        // stack final dense layer
        x = Dense(self.output_dim, activation="tanh")(x)

        // stack L2 normalization layer
        embeddings = Lambda(lambda x: K.l2_normalize(x, axis=-1))(x)

        return Model(input=inputs, output=embeddings)

    def _triplet_loss(self, inputs):
        p = K.sum(K.square(inputs[0] - inputs[1]), axis=-1, keepdims=True)
        n = K.sum(K.square(inputs[0] - inputs[2]), axis=-1, keepdims=True)

Instances