Function Build_Model_RNN_Text

code/RNN.py:45–88

def buildModel_RNN(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5): word_index in word index , embeddings_index is embeddings index, look at data_helper.py nClasses is number of classes, MAX_SEQUENCE_LENGTH is maximum lenght

(word_index, embeddings_index, nclasses,  MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5)

def Build_Model_RNN_Text(word_index, embeddings_index, nclasses,  MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):
    """
    def buildModel_RNN(word_index, embeddings_index, nclasses,  MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):
    word_index in word index ,
    embeddings_index is embeddings index, look at data_helper.py
    nClasses is number of classes,
    MAX_SEQUENCE_LENGTH is maximum lenght of text sequences
    """

    model = Sequential()
    hidden_layer = 3
    gru_node = 256

    embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
    for word, i in word_index.items():
        embedding_vector = embeddings_index.get(word)
        if embedding_vector is not None:
            # words not found in embedding index will be all-zeros.
            if len(embedding_matrix[i]) != len(embedding_vector):
                print("could not broadcast input array from shape", str(len(embedding_matrix[i])),
                      "into shape", str(len(embedding_vector)), " Please make sure your"
                                                                " EMBEDDING_DIM is equal to embedding_vector file ,GloVe,")
                exit(1)
            embedding_matrix[i] = embedding_vector
    model.add(Embedding(len(word_index) + 1,
                                EMBEDDING_DIM,
                                weights=[embedding_matrix],
                                input_length=MAX_SEQUENCE_LENGTH,
                                trainable=True))


    print(gru_node)
    for i in range(0,hidden_layer):
        model.add(GRU(gru_node,return_sequences=True, recurrent_dropout=0.2))
        model.add(Dropout(dropout))
    model.add(GRU(gru_node, recurrent_dropout=0.2))
   #model.add(Dense(, activation='relu'))
    model.add(Dense(nclasses, activation='softmax'))


    model.compile(loss='sparse_categorical_crossentropy',
                      optimizer='adam',
                      metrics=['accuracy'])
    return model