plot of Top1/Top5 validation accuracy for ImageNet training

plot_vals.py

import json
from pudb import set_trace
import matplotlib
import matplotlib.pyplot as plt
import numpy as np


with open('good_run_raport.json') as json_file:
    data = json.load(json_file)
    print("top 5")
    val_top5 = data['epoch']['val.top5']
    print(val_top5)
    print(len(val_top5))

    print("top 1")
    val_top1 = data['epoch']['val.top1']
    print(val_top1)
    print(len(val_top1))


# Plots
epochs = np.arange(len(val_top1))
val_acc_top1 = val_top1
val_acc_top5 = val_top5

fig, ax = plt.subplots()
ax.plot(epochs, val_acc_top1, 'k--', label='Top1')
ax.plot(epochs, val_acc_top5, 'k:', label='Top5')

ax.set(xlabel='epochs (s)', ylabel='Validation accuracy',
       title='Validation accuracy over {} epochs'.format(len(val_top1)))
ax.grid()
legend = ax.legend(loc='lower right', shadow=False, fontsize='x-large')
fig.savefig("/dockerx/val_plot.png")