def attention_map(self, text):
    """
        Text to visualze attention map for.
    """
    # encode the string
    d = self.input_vocab.string_to_int(text)

    # get the output sequence
    predicted_text = run_example(
        self.pred_model, self.input_vocab, self.output_vocab, text)

    text_ = list(text) + ['<eot>'] + ['<unk>'] * self.input_vocab.padding
    # get the lengths of the string
    input_length = len(text)+1
    output_length = predicted_text.index('<eot>')+1
    # get the activation map
    activation_map = np.squeeze(self.proba_model.predict(np.array([d])))[
        0:output_length, 0:input_length]

    # import seaborn as sns
    plt.clf()
    f = plt.figure(figsize=(8, 8.5))
    ax = f.add_subplot(1, 1, 1)

    # add image
    i = ax.imshow(activation_map, interpolation='nearest', cmap='gray')
    
    # add colorbar
    cbaxes = f.add_axes([0.2, 0, 0.6, 0.03])
    cbar = f.colorbar(i, cax=cbaxes, orientation='horizontal')
    cbar.ax.set_xlabel('Probability', labelpad=2)

    # add labels
    ax.set_yticks(range(output_length))
    ax.set_yticklabels(predicted_text[:output_length])
    
    ax.set_xticks(range(input_length))
    ax.set_xticklabels(text_[:input_length], rotation=45)
    
    ax.set_xlabel('Input Sequence')
    ax.set_ylabel('Output Sequence')

    # add grid and legend
    ax.grid()
    # ax.legend(loc='best')

    f.savefig(os.path.join(HERE, 'attention_maps', text.replace('/', '')+'.pdf'), bbox_inches='tight')
    f.show()