helper_functions.py

import matplotlib.pyplot as plt
from tensorflow.keras.models import Model



def print_loss_history(training_history):
    loss = training_history.history['loss']
    val_loss = training_history.history['val_loss']
    epochs = range(1, len(loss) + 1)
    plt.plot(epochs, loss, color='red', label='Training loss')
    plt.plot(epochs, val_loss, color='green', label='Validation loss')
    plt.title('Training and validation loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()
    plt.show()
    return

def print_accuracy_history(training_history):
    acc = training_history.history['accuracy']
    val_acc = training_history.history['val_accuracy']
    epochs = range(1, len(acc) + 1)
    plt.plot(epochs, acc, color='red', label='Training acc')
    plt.plot(epochs, val_acc, color='green', label='Validation acc')
    plt.title('Training and validation accuracy')
    plt.xlabel('Epochs')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.show()
    return




def display_activation(activations, col_size, row_size, act_index): 
    activation = activations[act_index]
    activation_index=0
    fig, ax = plt.subplots(row_size, col_size, figsize=(row_size*2.5,col_size*1.5))
    for row in range(0,row_size):
        for col in range(0,col_size):
            ax[row][col].imshow(activation[0, :, :, activation_index], cmap='plasma')
            activation_index += 1   
    return