How to use the matplotlib.pyplot.xlabel function in matplotlib

# We add the labels for each digit.
txts = []
for i in range(2):
    # Position of each label.
    xtext, ytext = np.median(proj[y == i, :], axis=0)
    txt = ax.text(xtext, ytext, str(i), fontsize=24)
    txt.set_path_effects([
        PathEffects.Stroke(linewidth=5, foreground="w"),
        PathEffects.Normal()])
    txts.append(txt)

# add labels from representation
rep = [r.split('[')[-1] for r in clf.get_representation().split(']') if r != '']
print('rep:',rep)
plt.xlabel(rep[0])
plt.ylabel(rep[1])

plt.savefig('longitudinal_representation.svg', dpi=120)

for ts in test_time_stamps:
                try:
                    float_test_time_stamps.append(matdates.date2num(datetime.strptime(ts, time_format1)))
                except:
                    float_test_time_stamps.append(matdates.date2num(datetime.strptime(ts, time_format2)))
            plt.plot_date(x=float_test_time_stamps, y=test_data, label=test.name, fmt='.-', xdate=True)
            plt.legend(fontsize='small', loc='best')
        plt.ylabel('MPPS/Core (Norm)')
        plt.title('Setup: ' + self.name)
        plt.tick_params(
            axis='x',
            which='both',
            bottom='off',
            top='off',
            labelbottom='off')
        plt.xlabel('Time Period: ' + start_date[:-6] + ' - ' + self.end_date)
        if save_path:
            plt.savefig(os.path.join(save_path, self.name + file_name))
            if not self.setup_trend_stats.empty:
                (self.setup_trend_stats.round(2)).to_csv(os.path.join(save_path, self.name +
                                                                      '_trend_stats.csv'))
            plt.close('all')

def plot_histogram(lfw_dir):
    """
    Function to plot the distribution of cluster sizes in LFW.
    """
    filecount_dict = {}
    for root, dirs, files in os.walk(lfw_dir):
        for dirname in dirs:
            n_photos = len(os.listdir(os.path.join(root, dirname)))
            filecount_dict[dirname] = n_photos
    print("No of unique people: {}".format(len(filecount_dict.keys())))
    df = pd.DataFrame(filecount_dict.items(), columns=['Name', 'Count'])
    print("Singletons : {}\nTwo :{}\n".format((df['Count'] == 1).sum(),
                                              (df['Count'] == 2).sum()))
    plt.hist(df['Count'], bins=max(df['Count']))
    plt.title('Cluster Sizes')
    plt.xlabel('No of images in folder')
    plt.ylabel('No of folders')
    plt.show()

def draw_prediction(dates: list, awaited_results: list, prediction_results: list):
    plt.figure()

    plt.plot(dates[INPUT_SIZE:], awaited_results, color="black")  # current prices in reality
    plt.plot(dates[INPUT_SIZE:], prediction_results, color="green")  # predicted prices by neural network
    plt.title('current prices / predicted prices by date')
    plt.ylabel('price')
    plt.xlabel('date')
    plt.legend(['current', 'predicted'], loc='best')

    plt.show()

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))

    plt.scatter(x_values, y_values,marker='x',color=color) #marker='^'

    plt.gcf().subplots_adjust(left=0.15)
    plt.gcf().subplots_adjust(bottom=0.15)   # reserve space for label
    # plt.xlabel(attribute,fontsize=15)
    # # plt.ylabel("Frequency")
    # plt.ylabel("Number",fontsize=15)  #
    # # plt.title('Histogram of '+attribute)
    # # plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
    # # plt.axis([40, 160, 0, 0.03])

    ax.tick_params(labelsize=20)
    plt.xlabel(xlabel,fontsize=20)
    plt.ylabel(ylabel,fontsize=20)

    # # marked  values
    iou_thresholds = [0.5]
    for iou_thr in iou_thresholds:
        ax.axvline(x=iou_thr,color='k',linewidth=0.8,linestyle='--')
        # ax.text(area+100, 0.55, '%d $\mathrm{m^2}$'%area, rotation=90,fontsize=20)
        ax.text(iou_thr+text_loc_detX, text_locY, '%.1f ' % iou_thr, rotation=90, fontsize=20)


    # plt.grid(True)
    plt.savefig(output)
    basic.outputlogMessage("Output figures to %s"%os.path.abspath(output))

temp_acc_train = sess.run(accuracy, feed_dict={x_data: x_vals_train, y_target: np.transpose([y_vals_train])})
    train_acc.append(temp_acc_train)
    temp_acc_test = sess.run(accuracy, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])})
    test_acc.append(temp_acc_test)
    if (i+1)%300==0:
        print('Loss = ' + str(temp_loss))
        

###
# Display model performance
###

# Plot loss over time
plt.plot(loss_vec, 'k-')
plt.title('Cross Entropy Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Cross Entropy Loss')
plt.show()

# Plot train and test accuracy
plt.plot(train_acc, 'k-', label='Train Set Accuracy')
plt.plot(test_acc, 'r--', label='Test Set Accuracy')
plt.title('Train and Test Accuracy')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()

Args:
        rank_list:
        figsize: Figure width and height in inches

    Returns: Nothing but generates a plot

    """
    plt.subplots(figsize=figsize)

    k_vec = range(1, max(rank_list) + 1)
    recalls = [recall_at_k(rank_list, k) for k in k_vec]
    plt.plot(k_vec, recalls, color="darkorange", lw=2)
    plt.xlim([0.0, max(k_vec)])
    plt.ylim([0.0, 101])
    plt.ylabel("Recall")
    plt.xlabel("Top-K")
    plt.title("Recall@k curve")

def verify_hdf5(indices, results_dir):
    with h5py.File(workdir + 'ibis.hdf5', 'r') as f:
        images = f['ibis_t1']
        labels = f['qc_label']
        filenames = f['filename']

        for index in indices:
            img = images[index, target_size[0]//2, :, :]
            label = labels[index, ...]
            filename = filenames[index, ...]

            plt.imshow(img, cmap='gray')
            plt.xlabel(str(label))
            plt.ylabel(str(filename[2:-1]))
            plt.savefig(results_dir + 'img-' + str(index), bbox_inches='tight')
            plt.close()

def __plot_curve(self, name, val, ylim, suffix=''):
        x = val['learning_curve']['x']
        y_train = val['learning_curve']['y_train']
        y_cv = val['learning_curve']['y_cv']

        plt.plot(x, y_train, 'o-', color='dodgerblue',
                 label='Training score')
        plt.plot(x, y_cv, 'o-', color='darkorange',
                 label='Cross-validation score')
        plt.title(name)
        plt.xlabel('Training examples')
        plt.ylabel('Score')
        plt.grid(True)
        plt.ylim(ylim)
        plt.legend(loc="lower right")
        fname = self.params.out_dir + '/Learning curve_' + name
        if suffix != '':
            fname += '_' + suffix
        fname += '.png'
        plt.savefig(fname, bbox_inches='tight')
        plt.close()