text_plain_output_4.png text_plain_output_6.png application_vnd.jupyter.stderr_output_3.png application_vnd.jupyter.stderr_output_5.png text_plain_output_2.png application_vnd.jupyter.stderr_output_1.png
import matplotlib.pyplot as plt

import numpy as np

def show_image(image):
    plt.colorbar()
imagenum = np.random.randint(len(x_train))
classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

def plot_metrics(metric_name, title, append='val_'):
    plt.xticks(list(range(len(history.history[metric_name]))))

plot_metrics(metric_name='loss', title='Plot of Model Loss against number of Epochs', append='test_')
plot_metrics(metric_name='acc', title='Plot of Model Accuracy against number of Epochs', append='test_')