The full code for the implemented classifier is as follows:

from tflearn.datasets import titanic 
titanic.download_dataset('titanic_dataset.csv') 
from tflearn.data_utils import load_csv 
data, labels = load_csv('titanic_dataset.csv', target_column=0, 
                        categorical_labels=True, n_classes=2) 

def preprocess(data, columns_to_ignore): 
    for id in sorted(columns_to_ignore, reverse=True): 
        [r.pop(id) for r in data] 
    for i in range(len(data)): 
        data[i][1] = 1. if data[i][1] == 'female' else 0. 
    return np.array(data, dtype=np.float32) 

to_ignore=[1, 6] 
data = preprocess(data, to_ignore) 
net = tflearn.input_data(shape=[None, 6]) 

net = tflearn.fully_connected(net, 32) 
net = tflearn.fully_connected(net, 32) 
net = tflearn.fully_connected(net, 2, activation='softmax') 
net = tflearn.regression(net) 
model = tflearn.DNN(net) 
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True) 

# Evalute the model
accuracy = model.evaluate(data, labels, batch_size=16)
print('Accuracy: ', accuracy)