plot_train.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

fname = 'model_train.log'
print('Reading:', fname)
df = pd.read_csv(fname)

epoch = df['epoch'].values + 1
loss = df['loss'].values
val_loss = df['val_loss'].values

print('epochs:   %8d ... %d' % (np.min(epoch), np.max(epoch)))
print('loss:     %.6f ... %.6f' % (np.min(loss), np.max(loss)))
print('val_loss: %.6f ... %.6f' % (np.min(val_loss), np.max(val_loss)))

plt.plot(epoch, loss, label='train loss')
plt.plot(epoch, val_loss, label='validation loss')

plt.xlabel('epoch')
plt.ylabel('mean absolute error (MW m$^{-3}$)')

plt.legend()
plt.grid()

i = np.argmin(val_loss)
min_val_loss = val_loss[i]
min_val_epoch = epoch[i]

print('min_val_loss: %.6f' % min_val_loss)
print('min_val_epoch:', min_val_epoch)

(x_min, x_max) = plt.xlim()
(y_min, y_max) = plt.ylim()

plt.plot([x_min, min_val_epoch], [min_val_loss, min_val_loss], 'k--')
plt.plot([min_val_epoch, min_val_epoch], [0., min_val_loss], 'k--')

plt.xlim(0, x_max)
plt.ylim(0., y_max)

plt.tight_layout()

fname = 'plot_train.png'
print('Writing:', fname)
plt.savefig(fname)