models.py 文件源码

def vqa_model(embedding_matrix, num_words, embedding_dim, seq_length, dropout_rate, num_classes):
    vgg_model = img_model(dropout_rate)
    lstm_model = Word2VecModel(embedding_matrix, num_words, embedding_dim, seq_length, dropout_rate)
    print "Merging final model..."
    fc_model = Sequential()
    fc_model.add(Merge([vgg_model, lstm_model], mode='mul'))
    fc_model.add(Dropout(dropout_rate))
    fc_model.add(Dense(1000, activation='tanh'))
    fc_model.add(Dropout(dropout_rate))
    fc_model.add(Dense(num_classes, activation='softmax'))
    fc_model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
        metrics=['accuracy'])
    return fc_model