i got this ValueError: not enough values to unpack (expected 5, got 3)

[Abdikader Mohamed]

previous this code was LSTM and the code is working but I also need to use GRU With this code instead of LSTM

and I got the above Error please can anyone help me with this?

k.clear_session()

latent_dim = 200

embedding_dim = 300

# Encoder

encoder_inputs = Input(shape=(max_text_len, ),name='Encoder_Inputs')

# Embedding layer

enc_emb = wv_layer(encoder_inputs)

# Encoder LSTM 1

encoder_gru1 = Bidirectional(GRU(latent_dim, return_sequences=True,

                     return_state=True, dropout=0.2,

                     recurrent_dropout=0.2,name='Encoder_BiLSTM_Layer1'))

(encoder_output1, forward_state_h1, forward_state_c1,backward_state_h1,backward_state_c1) = encoder_gru1(enc_emb)

state_h1=Concatenate()([forward_state_h1,backward_state_h1])

state_c1=Concatenate()([forward_state_c1,backward_state_c1])

# Encoder LSTM 2

encoder_gru2 = Bidirectional(GRU(latent_dim, return_sequences=True,

                     return_state=True, dropout=0.2,

                     recurrent_dropout=0.2,name='Encoder_BiLSTM_Layer2'))

(encoder_outputs, forward_state_h2, forward_state_c2,backward_state_h2,backward_state_c2) = encoder_gru2(encoder_output1)

state_h=Concatenate()([forward_state_h2,backward_state_h2])

state_c=Concatenate()([forward_state_c2,backward_state_c2])

# Set up the decoder, using encoder_states as the initial state

decoder_inputs = Input(shape=(None, ),name='Decoder_Inputs')

# Embedding layer

dec_emb_layer = Embedding(y_vocab, embedding_dim, trainable=True, name='Decoder_Embedding_Inputs')

dec_emb = dec_emb_layer(decoder_inputs)

# Decoder LSTM1

decoder_gru = GRU(latent_dim*2, return_sequences=True,

                    return_state=True, dropout=0.2,

                    recurrent_dropout=0.2,name='Decoder_LSTM_Layer')

(decoder_outputs, decoder_fwd_state, decoder_back_state) = decoder_gru(dec_emb, initial_state=[state_h, state_c])

# Attention Layer

attn_layer = AttentionLayer(name='Attention_Layer')

attn_out,attn_states=attn_layer([encoder_outputs,decoder_outputs])

decoder_concat_input=Concatenate(axis=-1,name='Concat_layer')([decoder_outputs,attn_out])

# Dense layer

decoder_dense = TimeDistributed(Dense(y_vocab, activation='tanh',name='TimeDistribution_Layer'))

decoder_outputs = decoder_dense(decoder_concat_input)

# Define the model

model = Model([encoder_inputs, decoder_inputs], decoder_outputs,name='Attn_Seq2Seq')

model.summary()