(acc,val_acc,loss,val_loss,fine_tuning_epoch)
return return_plots
plotting function that generates classification plots for fine tuned models
    :return the names and actual classification plots
plotting function that generates classification plots for fine tuned models
    :return the names and actual classification plots
plotting function that generates classification plots for fine tuned models : return the names and actual classification plots
def generate_fine_tuned_classification_plots(acc,val_acc,loss,val_loss,fine_tuning_epoch):
    '''
    plotting function that generates classification plots for fine tuned models
    :return the names and actual classification plots
    '''

    plots = []
    plot_names = []

    img_acc = plt.figure()
    plt.figure(figsize=(8, 8))
    plt.subplot(2, 1, 1)
    plt.plot(acc, label='Training Accuracy')
    plt.plot(val_acc, label='Validation Accuracy')
    plt.ylim([0, 1.0])
    plt.plot([fine_tuning_epoch-1,fine_tuning_epoch-1],
            plt.ylim(), label='Start Fine Tuning')
    plt.legend(loc='lower right')
    plt.title('Training and Validation Accuracy')

    img_loss = plt.figure()
    plt.figure(figsize=(8, 8))
    plt.subplot(2, 1, 2)
    plt.plot(loss, label='Training Loss')
    plt.plot(val_loss, label='Validation Loss')
    plt.ylim([0, 1.0])
    plt.plot([fine_tuning_epoch-1,fine_tuning_epoch-1],
            plt.ylim(), label='Start Fine Tuning')
    plt.legend(loc='upper right')
    plt.title('Training and Validation Loss')
    plt.xlabel('epoch')
    # plt.show()

    # generating plots for accuracy
    plots.append(img_acc)
    plot_names.append('accuracyvsval_accuracy')
    # generating plots for loss
    plots.append(img_loss)
    plot_names.append('lossvsval_los')

    # dynamic way to return all possible plots in case it expands together
    return_plots = {str(plot_names[x]):plots[x] for x in range(len(plots))}

    return return_plots
def generate_fine_tuned_classification_plots ( acc , val_acc , loss , val_loss , fine_tuning_epoch ) : plots = [ ] plot_names = [ ] img_acc = plt . figure ( ) plt . figure ( figsize = ( 8 , 8 ) ) plt . subplot ( 2 , 1 , 1 ) plt . plot ( acc , label = 'Training Accuracy' ) plt . plot ( val_acc , label = 'Validation Accuracy' ) plt . ylim ( [ 0 , 1.0 ] ) plt . plot ( [ fine_tuning_epoch - 1 , fine_tuning_epoch - 1 ] , plt . ylim ( ) , label = 'Start Fine Tuning' ) plt . legend ( loc = 'lower right' ) plt . title ( 'Training and Validation Accuracy' ) img_loss = plt . figure ( ) plt . figure ( figsize = ( 8 , 8 ) ) plt . subplot ( 2 , 1 , 2 ) plt . plot ( loss , label = 'Training Loss' ) plt . plot ( val_loss , label = 'Validation Loss' ) plt . ylim ( [ 0 , 1.0 ] ) plt . plot ( [ fine_tuning_epoch - 1 , fine_tuning_epoch - 1 ] , plt . ylim ( ) , label = 'Start Fine Tuning' ) plt . legend ( loc = 'upper right' ) plt . title ( 'Training and Validation Loss' ) plt . xlabel ( 'epoch' ) # plt.show() # generating plots for accuracy plots . append ( img_acc ) plot_names . append ( 'accuracyvsval_accuracy' ) # generating plots for loss plots . append ( img_loss ) plot_names . append ( 'lossvsval_los' ) # dynamic way to return all possible plots in case it expands together return_plots = { str ( plot_names [ x ] ) : plots [ x ] for x in range ( len ( plots ) ) } return return_plots