def LSTNet(trainX1,trainX2,trainY,config): input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 =

The code looks fine to me. However, I would suggest making a few modifications: 1. Instead of setting the weights of `conv2` to be the same as `conv1`, you could define separate convolutional layers for each input. This would allow the model to learn different features from each input. 2. The `concatenate` function should have the `axis` parameter set to 2 instead of 1, since the inputs have shape `(batch_size, time_steps, features)`. 3. It might be a good idea to add some dropout or regularization to prevent overfitting. Here's an updated version of the code with these modifications: ``` def LSTNet(trainX1, trainX2, trainY, config): input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 = Conv1D(filters=48, kernel_size=6, strides=1, activation='relu')(input1) gru1 = CuDNNGRU(64)(conv1) attention1 = MultiHeadAttention(num_heads=8, key_dim=64)(gru1, gru1) attention1 = LayerNormalization()(attention1 + gru1) input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) conv2 = Conv1D(filters=48, kernel_size=6, strides=1, activation='relu')(input2) gru2 = CuDNNGRU(64)(conv2) output = concatenate([attention1, gru2], axis=2) output = Dense(trainY.shape[1])(output) # Highway network highway_window = config.highway_window z = Lambda(lambda k: k[:, -highway_window:, :])(input1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window * trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) output = add([output, z]) output = Activation('sigmoid')(output) model = Model(inputs=[input1, input2], outputs=output) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model ```