Graph

from Results import *
import matplotlib.pyplot as  plt
import numpy as np

# Put all the losses in the same array, and get the averaged losses
train_bn = np.array([vgg16_bn_1, vgg16_bn_2, vgg16_bn_3, vgg16_bn_4, vgg16_bn_5])
train_bn = np.mean(train_bn, axis = 0)
scale = np.linspace(0, 30, len(train_bn))

# Compute the rolling loss
rolling_bn = [np.mean(train_bn[i:i+100]) for i in range(len(train_bn) - 100)]

# Plot
ax = plt.figure().gca()
ax.set_xticks(np.arange(0, 31, 1))
plt.plot(scale, train_bn, label = "Mean of losses on 5 runs")
plt.plot(scale[:-100], rolling_bn, label = "Rolling loss on 100 minibatches")
plt.title("Batch Norm")
plt.grid(axis = 'x', which = 'both' )
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()