emotion detection

from keras.layers import Input, Dense, Embedding, Concatenate, Activation, \
    Dropout, LSTM, Bidirectional, GlobalMaxPooling1D, GaussianNoise
from keras.models import Model


def buildModel(embeddings_matrix, sequence_length, lstm_dim, hidden_layer_dim, num_classes, 
               noise=0.1, dropout_lstm=0.2, dropout=0.2):
    turn1_input = Input(shape=(sequence_length,), dtype='int32')
    turn2_input = Input(shape=(sequence_length,), dtype='int32')
    turn3_input = Input(shape=(sequence_length,), dtype='int32')
    embedding_dim = embeddings_matrix.shape[1]
    embeddingLayer = Embedding(embeddings_matrix.shape[0],
                                embedding_dim,
                                weights=[embeddings_matrix],
                                input_length=sequence_length,
                                trainable=False)
    
    turn1_branch = embeddingLayer(turn1_input)
    turn2_branch = embeddingLayer(turn2_input) 
    turn3_branch = embeddingLayer(turn3_input) 
    
    turn1_branch = GaussianNoise(noise, input_shape=(None, sequence_length, embedding_dim))(turn1_branch)
    turn2_branch = GaussianNoise(noise, input_shape=(None, sequence_length, embedding_dim))(turn2_branch)
    turn3_branch = GaussianNoise(noise, input_shape=(None, sequence_length, embedding_dim))(turn3_branch)

    lstm1 = Bidirectional(LSTM(lstm_dim, dropout=dropout_lstm))
    lstm2 = Bidirectional(LSTM(lstm_dim, dropout=dropout_lstm))
    
    turn1_branch = lstm1(turn1_branch)
    turn2_branch = lstm2(turn2_branch)
    turn3_branch = lstm1(turn3_branch)
    
    x = Concatenate(axis=-1)([turn1_branch, turn2_branch, turn3_branch])
    
    x = Dropout(dropout)(x)
    
    x = Dense(hidden_layer_dim, activation='relu')(x)
    
    output = Dense(num_classes, activation='softmax')(x)
    
    model = Model(inputs=[turn1_input, turn2_input, turn3_input], outputs=output)
    
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
    
    return model

model = buildModel(embeddings_matrix, MAX_SEQUENCE_LENGTH, lstm_dim=64, hidden_layer_dim=30, num_classes=4)