# Import Libraries
import os
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

plt.style.use('fivethirtyeight')

def plot_performance(hist):
    hist_ = hist.history
    epochs = hist.epoch
    
    plt.plot(epochs, hist_['accuracy'], label='Training Accuracy')
    plt.plot(epochs, hist_['val_accuracy'], label='Validation Accuracy')
    plt.title('Training and validation accuracy')
    plt.xlabel('Epochs')
    plt.ylabel('Accuracy')
    plt.legend()
    
    plt.figure()
    plt.plot(epochs, hist_['loss'], label='Training loss')
    plt.plot(epochs, hist_['val_loss'], label='Validation loss')
    plt.title('Training and validation loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()

    recall = np.array(hist_['recall'])
    precision = np.array(hist_['precision'])
    val_recall = np.array(hist_['val_recall'])
    val_precision = np.array(hist_['val_precision'])
    plt.figure()
    plt.plot(epochs, 
             2*((recall * precision)/(recall + precision)), 
             label='Training f1')
    plt.plot(epochs, 
             2*((val_recall * val_precision)/(val_recall + val_precision)), 
             label='Validation f1')
    plt.title('Training and validation F1-Score')
    plt.xlabel('Epochs')
    plt.ylabel('score')
    plt.legend()
    
    plt.show()