def range_train():
sample_sizes = [1e6]
for size in sample_sizes:
exp = Experiment("leagueoflegends", size, 75)
model, losses, iterations = exp.regular_train()
exp.predict(model, 1000, "s", True)
plt.title("Losses for size " + str(size))
plt.plot(iterations, losses)
plt.xlabel("Iteration")
plt.ylabel("Loss")
if not os.path.exists("plots/" + str(size) + ".png"):
plt.savefig("plots/" + str(size) + ".png")
else:
count = 1
while os.path.exists("plots/" + str(size) + ".png"):
count += 1
plt.savefig("plots/" + str(size) + ".png")
print("#" * 80)
print("FINAL LOSS FOR SIZE ", size, losses[len(losses) - 1])
print("#" * 80)
plt.clf()
def custom():
filename = "../kelvin"
exp = Experiment(subreddit=None,
sample_size=0,
percentile=0,
custom=True,
custom_file=filename)
model, losses, iterations = exp.regular_train(epochs=5)
exp.predict(model, 100, "s", False)
def single_complete_train():
exp = Experiment("leagueoflegends", None, 90)
model, losses, iterations = exp.regular_train(epochs=1)
exp.predict(model, 1000, "s", True)
plt.title("Losses for size " + str(None))
plt.plot(iterations, losses)
plt.xlabel("Iteration")
plt.ylabel("Loss")