astrai 2.2.0

A pretty bad neural network library
Documentation 
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import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

csv = 'lr_loss.csv'

def parse_csv():
	with open(csv, 'r') as f:
		lines = f.readlines()
		lines = lines[1:]
		lr = []
		epochs = []
		loss = []
		for line in lines:
			line = line.strip()
			line = line.split(',')
			lr.append(float(line[0]))
			epochs.append(int(line[1]))
			loss.append(float(line[2]))
		return (lr, loss, epochs)


lr, loss, epochs = parse_csv()


plt.plot(lr, loss)
plt.yscale('log')
plt.xlabel('lr')
plt.ylabel('loss')
plt.title('lr-loss')
# plt.show()


plt.plot(epochs, loss)
plt.yscale('log')
plt.xlabel('epochs')
plt.ylabel('loss')
plt.title('epochs-loss')
# plt.show()

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(np.array(lr), np.array(epochs), np.array(loss), linewidth=0, antialiased=False)
ax.set_xlabel('lr')
ax.set_ylabel('epochs')
ax.set_zlabel('loss')

min_loss = min(loss)
min_loss_index = loss.index(min_loss)
min_lr = lr[min_loss_index]
min_epoch = epochs[min_loss_index]

ax.scatter(min_lr, min_epoch, min_loss, color='r')
plt.title('lr-epoch-loss')

plt.show()