Sorry to interrupt. I had problems running Mode1 and Model5. “ValueError: Found two metrics with the same name: accuracy”. That's what I got wrong. In terms of the runtime environment, I tried several, but I'll just list a few：

    1：python=3.8.3   keras=2.7.0    tensorflow=2.7.0(CPU)
    2：python=3.8.3   cudatoolkit=11.3.1   cudnn= 8.2.1   tensorflow-gpu=2.6.0(GPU)

As you can see, the same problem exists when running both environments.So, I can only botheryou, is it my environment or something else?Please answer, thank you.

I am trying to reproduce your result for research purposes.
but I am getting an error in TimeDistributed Layer
"TimeDistributedLayer should be passed aninput_shape ` with at least 3 dimensions, received: (None, 64)"

could you please guide me on how to resolve this issue?

The Model Code is given below.
def build_model():
input = Input(shape = (None, ))
profiles_input = Input(shape = (None, 22))

Defining an embedding layer mapping from the words (n_words) to a vector of len 128

x1 = Embedding(input_dim = n_words, output_dim = 250, input_length = None)(input)
x1 = concatenate([x1, profiles_input], axis = 2)

x2 = Embedding(input_dim = n_words, output_dim = 125, input_length = None)(input)
x2 = concatenate([x2, profiles_input], axis = 2)

x1 = Dense(1200, activation = "relu")(x1)
x1 = Dropout(0.5)(x1)

Defining a bidirectional LSTM using the embedded representation of the inputs

x2 = Bidirectional(CuDNNGRU(units = 500, return_sequences = True))(x2)
x2 = Bidirectional(CuDNNGRU(units = 100, return_sequences = True))(x2)
COMBO_MOVE = concatenate([x1, x2])
w = Dense(500, activation = "relu")(COMBO_MOVE) # try 500
w = Dropout(0.4)(w)
w = tcn.TCN()(w)
y = TimeDistributed(Dense(n_tags, activation = "softmax"))(w)

Defining the model as a whole and printing the summary

model = Model([input, profiles_input], y)
#model.summary()

Setting up the model with categorical x-entropy loss and the custom accuracy function as accuracy

adamOptimizer = Adam(lr=0.0025, beta_1=0.8, beta_2=0.8, epsilon=None, decay=0.0001, amsgrad=False)
model.compile(optimizer = adamOptimizer, loss = "categorical_crossentropy", metrics = ["accuracy", accuracy])
return model