rnn.py 文件源码

def build(self) -> None:
        """
        ?????????? ??????.
        """
        inp = Input(shape=(None,))

        emb = Embedding(len(self.grapheme_alphabet), self.emb_dimension)(inp)
        encoded = Bidirectional(self.rnn(self.units1, return_sequences=True, recurrent_dropout=self.dropout))(emb)
        encoded = Dropout(self.dropout)(encoded)
        decoded = TimeDistributed(Dense(self.units2, activation="relu"))(encoded)
        predictions = TimeDistributed(Dense(len(self.phonetic_alphabet), activation="softmax"))(decoded)

        model = Model(inputs=inp, outputs=predictions)
        model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
        print(model.summary())
        self.model = model